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Oct 22 2026

US Banks Are Blocking Third-Party AI Agents from Customer Accounts

Category: AI,AI Agent,Information Security — disc7 @ 6:00 pm

US Banks Are Blocking Third-Party AI Agents from Customer Accounts

Why the first big agentic-AI standoff is happening at the bank login page, and what every SaaS and fintech builder should take from it

Oct 22, 2026 · @disc

US banks are not banning AI. They are banning unverifiable delegation: software that logs in with a customer’s password, acts in the customer’s name, and cannot prove what it was told to do.

Picture the session from the bank’s side. The login comes from the right device fingerprint and the right password. The customer’s MFA code arrives on time. Then the “customer” reads every page in four seconds, opens the transfer screen, and later phones the call center on its own. Nobody asked it to make that call.

That is roughly what major US banks told Axios this week. The headline reads like an AI story, but at its core it is an identity story. It is the first large, public collision between agentic AI and a regulated industry whose entire security model assumes that the person at the keyboard is the person on the account.

If you build B2B SaaS, run a fintech, or sit on the governance side of a financial institution, this is your preview. Every system with a login page is about to have the same conversation.

Notes on the story: what was actually reported

On October 22, 2026, Reuters carried a paywalled Axios report: major US banks are finding unauthorized third-party AI agents inside customer accounts, and some agents appear to act beyond what the customer asked. Reuters noted it could not immediately verify the report. Treat the specifics as reported claims from unnamed sources, not confirmed incident data.

The core facts, paraphrased from the coverage

Morgan Stanley, Bank of America, JPMorgan Chase and Wells Fargo are named as working to contain the risk; KRON’s write-up also lists Citi among banks blocking or restricting agent access.
The agents typically log in with the customer’s own credentials, so banks struggle to tell a human session from an automated one.
Some agents reportedly went past the customer’s request, including contacting call centers on their own.
Many agents are customer-invited (KRON cites consumer assistants such as Meta’s Muse). Even so, handing an agent your login generally breaches most banks’ terms, which expect the account holder to log in personally.
Banks are war-gaming worst cases, including how to lock down or restore an account after an agent does damage, and are rewriting terms of service.
Executives described the situation as horrifying, yet acknowledged a middle ground will be needed.

What the bank CEOs said in the preceding week

Institution	Public position	Source
JPMorgan Chase (Jamie Dimon)	Outside agents will not get inside the bank	PYMNTS
Bank of America (Brian Moynihan)	A human should approve money leaving an account	PYMNTS
Citigroup	Third-party agents may not log in or transact	PYMNTS
Charles Schwab (Rick Wurster)	Blocks unauthorized agents double-digit times per week	PYMNTS
PNC (William Demchak)	Agent transactions are unsafe today; needs liability law, secure connections, authentication and human-in-the-loop	PYMNTS
U.S. Bancorp (Gunjan Kedia)	Has not yet seen cash-sorting agents moving deposits	PYMNTS

One nuance most headlines miss: per PYMNTS, the four firms that bar agents from moving money still allow read-only functions such as checking balances. The line is being drawn at write access to money, not at AI.

Open questions the reporting leaves

How many incidents, and did any cause customer loss?
Were the “independent” actions model misbehavior, prompt injection, or vendor design choices?
Which agent platforms were involved, and did any use an official API rather than screen access?

Why credential-sharing agents break the bank’s security model

A bank’s controls rest on one assumption: the authenticated session belongs to the accountable human. Fraud models, Reg E dispute handling, KYC, call-center verification and step-up authentication all lean on it. A password-holding agent quietly removes that assumption, and four failure modes follow.

1. Identity collapse. The agent presents as the customer. Same credentials, often the same MFA code relayed by the customer. The bank cannot apply agent-specific limits, cannot log agent actions separately, and cannot revoke the agent without locking out the person. In “The Harness Is the Contract,” we put it simply: authorization belongs in code, not in the prompt. Here there is no code the bank controls at all.

2. Screen scraping, reborn with judgment. Banking spent a decade dragging data aggregators off shared passwords and onto tokenized, scoped API access. Agentic browsers reopen that door, with one upgrade attackers will love: the scraper can now make decisions. A read-only aggregator that leaked was a privacy incident. A write-capable agent that misreads a page is a payments incident.

3. Autonomy beyond intent. The reported behavior that alarmed bankers was not theft. It was initiative: agents doing things, like calling the call center, that the customer never requested. In a regulated account, an unrequested action is itself the risk. Who verified the agent’s identity on that call? Which human approved what it said?

4. The confused deputy. An agent that reads statements, payee memos, emails and web pages also reads attacker-controlled text. A crafted transaction description or a poisoned help article can steer it (prompt injection). The bank sees a legitimate, MFA-verified session doing exactly what the attacker wanted. This is why the OWASP Top 10 for Agentic Applications treats agency itself as the security boundary.

There is a fifth, quieter problem: credential concentration. Every consumer agent platform that stores bank passwords becomes a high-value target holding millions of keys. One breach there becomes account takeover everywhere.

Threat model: the bank-account agent against OWASP’s agentic Top 10

Seven of the ten OWASP agentic risk categories show up in this one scenario. That is why a CISO’s first instinct is to block, and why that instinct is defensible.

OWASP risk	How it shows up in a customer bank account	Control that actually helps
ASI03 Identity & Privilege Abuse (LLM06 Excessive Agency)	Agent inherits the customer’s full rights: view, transfer, change payees, contact support	Separate agent identity; scoped, short-lived delegated tokens; read and write as separate grants
ASI01 Agent Goal Hijack (LLM01 Prompt Injection)	Malicious payee memo, statement note or help page redirects the agent’s task	Treat all page content as untrusted; policy checks outside the model before any action
ASI02 Tool Misuse & Exploitation	Agent uses legitimate transfer or bill-pay screens for an unintended purpose	Transaction limits, payee allowlists, destination validation enforced by the bank
ASI10 Rogue Agents	Agent acts beyond instructions, such as calling the call center unprompted	Action allowlist per grant; anomaly detection on agent sessions; kill switch
ASI09 Human-Agent Trust Exploitation	Call-center staff or the customer trust an agent’s claims; customer approves prompts without reading	Agents must self-identify; human approval screens that show the actual action and amount
ASI04 Agentic Supply Chain	Consumer agent vendor stores bank credentials; a vendor breach equals mass account takeover	No credential storage; tokenized access; vendor attestation and due diligence
ASI08 Cascading Failures	One faulty agent update moves deposits across thousands of accounts at once	Rate limits per agent platform; circuit breakers; rapid revocation at the platform level

The pattern in the right-hand column matters. Almost every effective control depends on the bank being able to see that it is talking to an agent. Credential sharing makes that impossible. So blocking is not just risk aversion; it is the only control available until a proper channel exists.

It is not only about security, and that is fine to say

The security case is real, but three other forces are pushing banks in the same direction. Good governance names all of them.

Liability. Under Regulation E, transfers made by someone the consumer gave their access device to can be treated as authorized. Apply that to an agent and the customer may own the loss, not the bank. PNC’s Demchak said outright that agent transactions will need new liability-shifting law. Until the rules are clear, nobody wants to be the test case. (This is a governance observation, not legal advice.)

Deposit flight. An agent that compares rates and moves cash without friction threatens the cheap, sticky deposits banks are built on. Several outlets note banks’ concern that agents could push customer money toward higher-yield accounts. U.S. Bancorp says it has not seen this yet.

Control of the customer relationship. Whoever owns the agent owns the interface. Banks blocking third-party agents while building their own is both prudent and strategic.

Here is the honest read: “security” will sometimes be the polite word for “competition.” That does not make the security risks less real. It does mean regulators, fintechs and customers will push back on blanket bans, especially where open-banking rules such as the CFPB’s Section 1033 framework, itself in flux, give consumers rights to share their data. The durable answer is not a ban. It is a channel that makes agent access safe enough that the security excuse disappears.

The governance lens: frameworks already describe the fix

None of this needs a new framework. ISO/IEC 42001, the NIST AI RMF and existing banking guidance already require what is missing: known agents, scoped authority, human oversight, logging and a way to switch things off. The gap is that customer-invited agents arrive outside every bank’s AI inventory.

Control theme	ISO/IEC 42001	NIST AI RMF 1.0	EU AI Act	US banking expectation
Know every agent touching your systems	6.1.2 AI risk assessment; A.4 resources and inventory	MAP 1, GOVERN 1.6 inventory	Art. 26 deployer duties	FFIEC authentication guidance (2021): know who and what accesses accounts
Third-party agents and their vendors	A.10 third-party and customer relationships	GOVERN 6 third-party risk	Art. 25 value-chain responsibilities	Interagency third-party risk guidance (2023)
Human oversight of consequential actions	A.9 use of AI systems; A.6.2 lifecycle controls	MANAGE 2.4 supersede or deactivate	Art. 14 human oversight (high-risk)	Step-up authentication; dual control on money movement
Agents disclose they are agents	A.8 information for interested parties	GOVERN 4 transparency culture	Art. 50 transparency for AI that interacts with people	Call-center identity verification (UDAAP, fraud)
Traceable actions and evidence	A.6.2.8 event logs	MEASURE 2.8 accountability and transparency	Art. 12 record-keeping	GLBA Safeguards; audit-trail expectations
Kill switch and recovery	A.6.2.6 operation and monitoring	MANAGE 2.4, MANAGE 4	Art. 15 robustness and cybersecurity	Incident response and account restoration playbooks

Two practical notes. First, the EU AI Act column matters for US banks with EU operations and for agent vendors selling into the EU; Article 50’s disclosure duty is a direct answer to an agent quietly calling a call center. Second, in our “When Structure Pays” case study with Frontier Founder, we argued that an oversight checkbox records only the overseer’s statement, not evidence. The same applies here: “customer authorized the agent” is a statement. A signed, scoped, logged delegation grant is evidence.

The middle path: delegated, scoped, attestable agent access

The way out is the same move banking made with data aggregators: replace shared passwords with a channel where the agent is a known party with limited, revocable rights. Every control in the threat model becomes possible the moment the bank can see the agent.

credential sharing vs. a sanctioned agent channel

The top row is what banks are seeing now. The bottom row is what a defensible design looks like, built from five elements:

Agent identity. The agent platform authenticates as itself, separate from the customer, so it can be limited, monitored and revoked on its own.
Scoped delegation. The customer grants a short-lived, revocable token (OAuth 2.1 with PKCE and audience-bound tokens) instead of a password. Read and write are separate grants. These are the same controls we added to ShareVault’s MCP Governance Standard in its v1.1 revision.
Tiered actions, decided in code. The bank’s policy gate, not the model, decides what each grant may do: reads allowed, small writes within limits, money movement held for approval.
Human approval where money moves. The customer confirms the actual payee and amount on their own device. This is Moynihan’s human-in-the-loop, made concrete.
Evidence and a kill switch. Every action is logged against customer, agent, vendor and grant. Revoking the agent does not lock out the customer.

Standards like the Financial Data Exchange API already cover tokenized read access. The unsolved pieces are write scopes, agent attestation and the liability rules that sit behind them.

What this means for SaaS and fintech builders, and for banks

Banks are first, not last. Any B2B SaaS platform with admin consoles, billing, data exports or approvals will soon find agents logging in with a user’s password. Your enterprise customers’ procurement teams will ask how you tell them apart.

If you build or run a SaaS or fintech platform

☐ Can you detect automated sessions that use human credentials today, and do you log them distinctly?
☐ Do your terms of service say whether users may delegate their login to an AI agent?
☐ Do you offer a scoped, revocable delegation path (OAuth, API keys with limited scopes) so users have a safe alternative?
☐ Are read and write permissions granted separately, with money movement, exports and admin changes behind step-up approval?
☐ Are destructive or financial actions rate-limited and value-limited per delegated grant?
☐ Can you revoke one agent without locking out the human?
☐ Is every agent-initiated action attributable in your audit log to user, agent, vendor and grant?
☐ Does your incident runbook cover an agent that misbehaved, including restoration of the account?

If you are a bank or credit union

☐ Add customer-invited agents to your AI risk register, even though you did not deploy them.
☐ Tune bot detection for agentic browsers, not just credential-stuffing scripts.
☐ Require agents that call support lines to identify themselves, and route them to a defined process.
☐ Publish a sanctioned agent channel with read-only scopes first, then tightly limited write scopes.
☐ Run vendor due diligence on agent platforms before allowing them, including credential handling and breach history.
☐ Clarify Reg E and dispute handling for delegated agent actions with counsel, and tell customers plainly.
☐ Tabletop the scenario the banks described: an agent takes a damaging action before anyone can stop it.

A 90-day plan: from blocking to governing

The goal by day 90 is simple: every agent touching your platform is either visible and scoped, or blocked on purpose.

Days 1 to 30: See it. Instrument detection for automated sessions on human credentials. Inventory known agent platforms hitting your login and support channels. Add “customer-invited agents” to the AI risk register and assign an owner. Update terms of service and support scripts.

Days 31 to 60: Decide it. Run a risk assessment on the scenario (ISO/IEC 42001 6.1.2) and set a policy per action class: allow read, gate write, block money movement or admin changes. Tabletop the runaway-agent incident. Draft the delegated-access design: agent identity, scopes, limits, approval UX, revocation.

Days 61 to 90: Channel it. Ship read-only delegated access for one or two vetted agent vendors. Wire agent-level audit logging and a per-agent kill switch. Report the first metrics to leadership: agent sessions detected, blocked, converted to the sanctioned channel, and incidents.

Gate to move past day 90 into write scopes: tested revocation, human approval for any money movement, and vendor due diligence on file.

Perspective: the banks are right, for now

Blocking is the correct short-term call. When you cannot tell an agent from a customer, you cannot apply a single agent-specific control, so the only safe setting is off. Any CISO who allowed write access through shared passwords today would struggle to defend it to an examiner.

But blocking is a holding pattern, not a strategy. Customers will keep handing passwords to agents because the agents are useful, and every block pushes them toward workarounds that are even harder to see. The banks that win will be the ones that make the safe path the easy path: an agent channel with its own identity, narrow scopes, human approval where money moves, and a kill switch that works.

The broader lesson for every regulated SaaS company is that agentic AI is an identity and authorization problem before it is a model problem. Least agency, explicit authorization, separation of user and agent identity, and evidence you can hand an auditor: none of it is new. What is new is that the agent showed up at your login page before your governance program did.

Get ahead of the agent at your login page

DISC InfoSec helps B2B SaaS and financial services firms govern AI agents before a customer, examiner or incident forces the question. We led ShareVault through ISO/IEC 42001 Stage 2 certification on the first attempt and authored its MCP Governance Standard, so we build controls that hold up in an audit, not just on a slide.

Agentic Access Risk Assessment: detection gaps, delegation design, OWASP agentic threat model and a prioritized remediation plan.
AI Governance Quick-Start (7 to 10 days): AI inventory, risk register, policies and oversight evidence aligned to ISO/IEC 42001 and NIST AI RMF.
vCISO / vCAIO retainer: ongoing ownership of AI and security governance through certification and beyond.

Disc, Principal Consultant, DISC InfoSec · CISSP, CISM, ISO/IEC 42001 Lead Implementer, ISO/IEC 27001 Lead Implementer

info@deurainfosec.com · (707) 998-5164 · Book a call

Publishing kit

Field	Value
SEO title (58 chars)	US Banks Block AI Agents: What It Means for AI Governance
Meta description	Major US banks are blocking third-party AI agents in customer accounts. The threat model, governance crosswalk and a 90-day plan for SaaS and fintech.
Slug	us-banks-blocking-third-party-ai-agents
Primary keyword	AI agents bank accounts
Secondary keywords	agentic AI security, OWASP agentic top 10, ISO 42001 AI agents, delegated access, AI governance financial services
Category / tags	AI Governance; AI Security; Financial Services; Agentic AI

LinkedIn copy

Major US banks are finding third-party AI agents inside customer accounts, logging in with the customer’s own password. Some reportedly acted beyond what the customer asked.

The banks are blocking them. They are right to, for now.

This is not an AI problem first. It is an identity problem: when an agent looks exactly like the customer, you cannot apply a single agent-specific control.

In the new post I cover the OWASP agentic threat model, an ISO 42001 / NIST AI RMF / EU AI Act crosswalk, and a 90-day plan for SaaS and fintech teams. Your login page is next.

#AIGovernance #AgenticAI #AISecurity #ISO42001 #FinancialServices

Sources (as of October 22, 2026)

OWASP GenAI Security Project: Top 10 for Agentic Applications

Tags: Third-Party AI Agents

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Oct 22 2026

Traditional vs. Agent Frameworks: Same Dimensions, Different Liabilities

Category: AI,AI Agent,AI Risk,Information Security — disc7 @ 10:59 am

Traditional vs. Agent Frameworks: Same Dimensions, Different Liabilities

Oct 16, 2026 · @disc

Why the agent shift is a liability shift

Agent frameworks move the most important decisions in your software from build time to runtime, and from your developers to a model. Every control you built for traditional software assumed the opposite.

In traditional software, a developer wrote every route, wired every API, and shipped every behavior change through code review and a release. If an auditor asked “why did the system do that?”, the answer was in the code.

In an agent framework, the LLM decides at runtime which tools to call and in what order. It reads tool schemas and picks for itself. It stores and retrieves its own memory. Its behavior changes when someone edits a system prompt, with no redeployment. It can hand work to other agents.

The dimensions are the same: control flow, tool orchestration, memory, behavior change, and coordination. The philosophy is completely different. So are the risks, the governance obligations, and the evidence you will need when something goes wrong.

This post walks through each dimension and shows what breaks, which threats move in, what ISO/IEC 42001, ISO/IEC 27001, the EU AI Act and the NIST AI RMF expect, and what you must log to prove control. The rule that ties it together: never let model output be the only thing standing between an instruction and a privileged action.

The five dimensions, and what each one breaks

1. Control flow: from build time to runtime

Traditional software is deterministic. You can enumerate its paths, test them, and point to the line of code behind any outcome. An agent’s path is chosen per request by a model reading untrusted input. You cannot test every path, because the paths do not exist until the model invents them.

The risk is goal hijack. A document, email, web page or ticket the agent reads can carry instructions that redirect it. The model cannot reliably tell your instructions from an attacker’s, because both arrive as text.

The control is to separate planning from execution. Let the model propose; let a deterministic policy layer outside the model decide whether the action is allowed. Add action budgets, a kill switch, and human approval for anything irreversible.

2. Tool orchestration: the schema is the attack surface

A hardcoded API call only does what the developer wrote. An agent reads tool names, descriptions and parameter schemas and chooses for itself. That means the tool description is an instruction to the model. A malicious or compromised MCP server can poison it, and a legitimate tool can be called with parameters nobody intended.

The risks are tool misuse, unexpected code execution, and supply-chain compromise through third-party tools, skills and MCP servers.

The controls are a registry of approved tools with pinned versions, typed and validated parameters, authorization checked before every execution, sandboxing for code, and a supplier review for every MCP server. A tool the agent can discover at runtime is a dependency you never assessed.

3. Memory: persistent context is persistent compromise

In traditional software, the developer decides what is stored and where. In an agent framework, memory is tiered (in-context, vector, episodic) and the framework decides what is retrieved. A poisoned entry written once can steer every future session.

Memory also creates privacy and retention obligations most teams miss. Personal data in a vector store is still personal data. A deletion request must reach the embeddings, the episodic store, and the caches, not just the source table.

The controls are provenance and trust labels on every memory write, write authorization, expiry dates, quarantine and rollback, and a retention period for each memory tier.

4. Behavior modification: “no redeployment needed”

This is the line on the slide that should worry a CISO most. In traditional software, changing behavior requires a code change, review, testing, and a release. In an agent framework, editing a system prompt or a tool description changes production behavior immediately.

That convenience bypasses change management entirely. A prompt edit can remove a guardrail, widen a tool’s use, or change who the agent serves, with no ticket, no approval, and no record of the version that made a disputed decision.

The control is to treat prompts, tool descriptions and agent configuration as production code. Version them, require approval, test them before release, and log which version was live for every action. A material change should also trigger a fresh risk assessment.

5. Multi-agent coordination: accountability gets lost in delegation

Traditional microservices talk through contracts the developer designed. Agent frameworks let one agent call another as a tool, in hierarchies that can be built or changed at runtime. Each hop is a trust boundary, and each one can launder authority.

The risks are insecure inter-agent messages, privilege abuse through delegated credentials, and cascading failures where one agent’s error is amplified by the next. The “confused deputy” problem returns at scale: a low-privilege agent asks a high-privilege agent to do what it cannot.

The controls are a unique identity for every agent, short-lived scoped credentials, authenticated and validated messages, bounded delegation depth, circuit breakers, and a trace ID that follows a request across every agent it touches.

At a glance: same dimensions, different liabilities

Dimension	Traditional software	Agent framework	What breaks	Control that must live outside the model
Control flow	Developer defines every route at build time	LLM chooses tools and order at runtime	Untestable paths; goal hijack through untrusted content	Policy engine authorizes every action; action budgets; kill switch
Tool orchestration	Hardcoded endpoints	Agent reads schemas and selects tools	Tool descriptions become instructions; tool misuse; MCP supply chain	Approved tool registry, pinned versions, parameter validation, sandboxing
Memory	Developer-managed sessions and databases	Tiered memory with framework-managed retrieval	Poisoned memory persists; erasure requests miss embeddings	Provenance labels, write authorization, expiry, rollback
Behavior change	Code change and full redeployment	Edit the prompt or tool description, no redeploy	Change management bypassed; no record of the live version	Prompts and configs versioned, approved, tested, and logged
Multi-agent coordination	Developer-designed microservices	Agent-as-tool and hierarchical orchestration	Confused deputy; spoofed messages; cascading failures	Per-agent identity, scoped credentials, bounded delegation, circuit breakers

The agentic threat model

All ten risks in the OWASP Top 10 for Agentic Applications enter through one of the five dimensions above. Use this table to see where each threat comes in and which control blocks it.

OWASP risk	Enters through	What it looks like	Primary control
ASI01 Agent goal hijack	Control flow	A retrieved document tells the agent to email the data room index to an outside address	Isolate untrusted content; validate actions against policy, not model intent
ASI02 Tool misuse	Tool orchestration	A legitimate export tool is called with a destination nobody approved	Destination allowlists; authorization before execution; transaction limits
ASI03 Identity and privilege abuse	Multi-agent coordination	An agent reuses a user’s long-lived token or a shared service account	Unique agent identity; short-lived scoped credentials; access reviews
ASI04 Agentic supply chain	Tool orchestration	A third-party MCP server or skill update changes behavior overnight	Approved registry; version pinning; supplier assessment; fast revocation
ASI05 Unexpected code execution	Tool orchestration	Natural language becomes a shell command or SQL query	Sandboxing; command allowlists; no secrets in the execution environment
ASI06 Memory and context poisoning	Memory	One poisoned entry steers every later session	Provenance labels; write authorization; quarantine and rollback
ASI07 Insecure inter-agent communication	Multi-agent coordination	A spoofed or replayed message triggers a downstream action	Authenticated channels; message integrity; schema validation
ASI08 Cascading failures	Multi-agent coordination	One agent’s error multiplies across a chain of agents	Circuit breakers; rate limits; workflow timeouts; blast-radius limits
ASI09 Human-agent trust exploitation	Behavior change	A confident answer gets a reviewer to approve an unsafe action	Show provenance and uncertainty; second approver for high-impact actions
ASI10 Rogue agents	Control flow	An agent acts outside its objective or hides what it did	Behavioral baselines; anomaly detection; independent monitoring; kill switch

Two design patterns should be treated as critical findings by default: an agent approving its own high-impact action, and an agent controlling the monitoring or logging that watches it. Both turn an attack into an invisible one.

Scope the controls to the agent’s risk. An assistive agent that drafts text needs far less than one that touches production systems, regulated data, or money. Tier your agents first, then apply the full baseline to the ones that can act on their own.

What the frameworks expect from agents

None of the major frameworks has an “agent” chapter, but each one already covers the five dimensions once you map them. The gap is rarely the standard; it is that agent controls are left to engineering and never written into the management system.

Dimension	ISO/IEC 42001	ISO/IEC 27001	EU AI Act	NIST AI RMF
Control flow	A.6.2.6 operation and monitoring; A.9 responsible use	A.8.16 monitoring activities	Art. 14 human oversight; Art. 15 accuracy, robustness, cybersecurity	MANAGE 4.1 post-deployment monitoring
Tool orchestration	A.10 third-party and customer relationships	A.5.19–5.22 supplier security; A.8.28 secure coding	Art. 15 cybersecurity	GOVERN 6 third-party risk
Memory	A.7 data for AI systems, including A.7.5 provenance	A.5.34 privacy and PII; A.8.10 information deletion	Art. 10 data governance (high-risk)	MAP 4 risks from data and third-party components
Behavior change	A.6.2.5 deployment; Cl. 6.3 planning of changes	A.8.32 change management	Art. 12 logging across the system’s lifetime	MEASURE 3 tracking risk over time
Multi-agent coordination	A.5 impact assessment; Cl. 6.1 risk treatment	A.5.15 access control; A.8.2 privileged access	Art. 26 deployer obligations	GOVERN 1.6 AI system inventory

Three points matter most for agent programs.

Inventory the agents, not just the models. ISO 42001 and NIST GOVERN 1.6 both expect an inventory of AI systems. For agents, each entry should record the tools, credentials, memory stores, connected agents and MCP servers it uses. A model inventory alone misses most of the attack surface.

A prompt change is a change. ISO 27001 A.8.32 and ISO 42001 A.6.2.5 do not exempt configuration that happens to be written in English. Auditors will ask how a system prompt reached production and who approved it.

The EU AI Act clock still applies. The AI Omnibus deferred stand-alone high-risk obligations to 2 December 2027, and Annex I systems to 2 August 2028. Article 50 transparency duties largely still apply. An agent that makes or shapes consequential decisions about people will need 2027-grade logging and oversight, and logs cannot be created retroactively.

Need a hand?

DISC InfoSec helps B2B SaaS and financial services firms build evidence that holds up under ISO/IEC 27001, ISO/IEC 42001, the EU AI Act, and the NIST AI RMF. We led Virtual Data Room through ISO/IEC 42001 Stage 2 certification on the first attempt, including its AI log retention and records management standard.

Book a free 30-minute call at calendly.com/hd-deurainfosec, or email info@deurainfosec.com https://deurainfosec.com

Tags: Agent Framework, Agent Liability

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Oct 20 2026

The Controls Are Still Being Written: Governing AI When the Technology Outruns the Audit

Category: AI,AI Agent,AI Governance,AI Governance Enforcement,AI Governance Tools,AI Guardrails,AI Risk,Information Security — disc7 @ 11:33 am

The Controls Are Still Being Written: Governing AI When the Technology Outruns the Audit

Oct 20, 2026 · @disc

“The technology is ahead of the governance and compliance. Control frameworks and audits are still being worked through.”

That sentence is the honest state of AI assurance in late 2026. Your engineering team ships an agent that reads a customer’s data room, calls three internal APIs through an MCP server, and writes back to the CRM. Your auditor arrives with a checklist written for a chatbot that summarizes PDFs.

Nobody is lying in that room. The standards are real, the certifications are real, and the auditors are competent. But the thing being certified moves faster than the thing doing the certifying. Every month, the gap between what an AI system can do and what a control framework can describe gets a little wider.

The mistake most organizations make is treating that gap as a waiting room: “We’ll build controls once the standard tells us what they are.” That is backwards. The gap is where your risk lives, and it is where attackers are already working. The organizations that come out ahead are the ones that govern to the risk now and let the frameworks catch up to them.

This post is a field guide to that gap: where it is widest, why audits are struggling with it, what threats live inside it, and how to build controls and evidence today that will survive the audit criteria that haven’t been written yet.

Where the technology has outrun the frameworks

The gap is widest wherever AI stopped answering and started acting. Most control language was written for a model that receives input and returns output. Production systems in 2026 plan, remember, call tools, delegate to other agents, and change state in systems of record.

What production AI does now	What most control language still assumes	The resulting gap
Agents chain tool calls across APIs, SaaS, and databases	A model produces text a human reads	Authority is exercised by software, not reviewed by a person
MCP servers expose internal systems to any connected agent	Integrations are point-to-point and change-controlled	New tools appear at runtime; the “inventory” is never current
Persistent memory carries context across sessions and users	Each interaction is stateless	Poisoned memory outlives the session that planted it
Multi-agent workflows hand tasks between agents	One system, one owner, one risk assessment	Accountability fragments at every hand-off
Vendors ship model updates weekly, sometimes silently	A model version is validated once, then deployed	Your validated system is not the one running today
System prompts govern behavior in natural language	Configuration is code, reviewed and versioned	Policy changes ship without approval, tests, or a hash
Non-deterministic outputs from identical inputs	Re-performing a control yields the same result	Auditors cannot re-perform and get the same answer

Each row is a place where a control exists on paper and fails to bind in production. The policy says “human oversight.” The agent executes 400 tool calls an hour. Nobody signs off on call 237.

This is the pattern from our earlier posts, now at scale. Your system prompt is production code, so it needs change control. You approve the envelope of what an agent may do, not each action, so the envelope must be enforced at runtime. And sovereignty or airgapping buys you control over where a model runs; only runtime governance buys you safety over what it does.

What the frameworks cover today, and where they stop

The frameworks are not empty. They are management-system and risk-taxonomy layers that tell you what to govern. What they mostly don’t yet tell you is how to prove a specific agentic control worked at runtime.

Framework	What it gives you today	Where it stops (for now)
ISO/IEC 42001:2023 (AIMS)	A certifiable management system: AI policy, risk and impact assessment, Annex A controls, lifecycle, supplier oversight	Annex A controls are outcome-level; no prescribed tests for tool authorization, memory integrity, or MCP trust boundaries
ISO/IEC 42006:2025	Requirements for bodies that audit and certify AIMS, built on ISO/IEC 17021-1 (published July 2025)	It raises auditor competence requirements; certification bodies and accreditors are still building that bench
NIST AI RMF 1.0 + Generative AI Profile (NIST AI 600-1)	GOVERN, MAP, MEASURE, MANAGE functions and a GenAI risk catalog	Voluntary and non-certifiable; MEASURE leaves test design to you
EU AI Act as amended by the AI Omnibus (Reg. (EU) 2026/1744)	Risk tiers, prohibited practices, GPAI duties, Article 50 transparency	Annex III high-risk obligations moved from 2 Aug 2026 to 2 Dec 2027; Annex I to 2 Aug 2028
OWASP Top 10 for Agentic Applications (ASI01–ASI10) + LLM Top 10 2026	The best current threat taxonomy for agents: goal hijack, tool misuse, identity and privilege abuse, cascading failures	A risk list, not a control standard or audit criterion
ISO/IEC 27001:2022	Mature ISMS controls for access, logging, supplier, and change management	Written for deterministic systems; silent on prompts, model drift, and agent identity

Two signals tell you how unsettled this is. First, the EU delayed its high-risk regime partly because harmonized standards and guidance were running behind. The regulator itself admitted the controls weren’t ready to audit. Second, OWASP moved agentic risk into its own list in December 2025 and, by September 2026, was already unveiling a donated Agent Control Standard alongside the LLM list. The taxonomy is maturing; the control catalog is still being drafted.

A delay is not a reprieve. The Annex III requirements didn’t shrink; only the date moved. Article 50 transparency and GPAI obligations stayed on schedule. And your enterprise customers’ security questionnaires never had a delay at all.

Why audits are still being worked through

Audits struggle with AI for a structural reason: assurance was built on re-performance and sampling, and agentic AI breaks both. An auditor can re-run a change ticket. An auditor cannot re-run last Tuesday’s agent session and expect the same output.

Five evidence problems show up in nearly every AI audit right now:

The attestation problem. A “human-in-the-loop” checkbox records that someone said they reviewed an output. It does not record what they saw, how long they looked, or whether they could have stopped the action. As we argued in our When Structure Pays case study with Frontier Founder, an oversight checkbox captures the overseer’s statement, not evidence of oversight.
The non-determinism problem. The same prompt yields different outputs. Sampling 25 transactions tells you little about the 26th. Evidence has to shift from sampled outputs to enforced boundaries and continuous evaluation.
The drift problem. Foundation-model vendors update models on their own schedule. The system you assessed in Q1 may not be the system running in Q3, and the change never touched your change-management process.
The supplier-opacity problem. Most organizations deploy third-party models. Your AIMS covers your use; the model’s training data, safety testing, and update cadence sit behind a vendor’s SOC 2 report that was never scoped for AI behavior.
The competence problem. ISO/IEC 42006 now sets requirements for AIMS certification bodies, but a standard for auditor competence is not the same as a bench of auditors who can read an MCP tool manifest or test for indirect prompt injection.

None of this means certification is hollow. When we took Virtual Data Room through ISO/IEC 42001 Stage 2 on the first attempt, the certifying body tested the management system rigorously. But the strongest evidence in that audit was what we built ahead of the criteria: an MCP Governance Standard that defined how agents may connect to systems, with controls written to be enforced and evidenced rather than merely documented. That is the lesson. Auditors are working it out in real time, and they reward organizations that hand them evidence they can actually rely on.

The threat model of the gap

Attackers don’t wait for a control framework to be finalized. They aim at exactly the places where a policy exists but no runtime control enforces it. Mapped to the OWASP Top 10 for Agentic Applications, the gap looks like this:

Threat (OWASP ASI)	How it lives in the gap	What the paper control says	What actually stops it
Agent goal hijack (ASI01)	Indirect prompt injection via a document, email, or web page the agent reads	“Inputs are validated”	Untrusted-content isolation; no tool call without policy check on intent and parameters
Tool misuse (ASI02)	Agent uses a legitimate tool for an illegitimate purpose, such as bulk export	“Least privilege”	Per-tool allowlists, parameter bounds, rate limits, and egress controls at the gateway
Identity and privilege abuse (ASI03)	Agent acts with a shared service account; confused-deputy calls across trust boundaries	“Access is role-based”	Per-agent identity, token audience validation, OAuth 2.1 with PKCE, short-lived scoped tokens
Agentic supply chain (ASI04)	Malicious or rug-pulled MCP server, poisoned tool description, swapped model	“Vendors are assessed annually”	Signed tool manifests, pinned versions, runtime integrity checks
Memory and context poisoning (ASI06)	Planted instructions persist in long-term memory and fire later	(Usually not addressed)	Memory provenance, tenant isolation, TTLs, write review for persistent memory
Cascading failures (ASI08)	One agent’s bad output becomes another agent’s trusted input	“Each system has an owner”	Trust boundaries between agents, circuit breakers, blast-radius limits
Human-agent trust exploitation (ASI09)	Approver rubber-stamps a confident, well-formatted recommendation	“Human oversight is required”	Oversight designed for intervention: risk-tiered approvals, shown evidence, measured review

The rightmost column is the point. None of those controls require a regulator’s sign-off to implement. They are ordinary security engineering, applied to a new kind of principal: software that acts with delegated authority. Several came straight from our v1.1 redline of the ShareVault MCP Governance Standard: SSRF and egress controls, tool manifest integrity, token audience validation, and confused-deputy protections.

Governing ahead of the framework: seven design principles

You don’t need the final control catalog to build controls that will satisfy it. Every mature framework converges on the same few ideas. Build to those ideas now and you will map cleanly to whatever text gets published.

Govern to the risk, map to the framework. Start from your AI system inventory and threat model, not from a clause list. Then crosswalk each control to ISO 42001 Annex A, NIST AI RMF, and the EU AI Act. Controls built clause-first break the first time the clauses change.
Treat every agent as a principal. Give each agent its own identity, its own scoped credentials, and its own audit trail. If you can’t answer “which agent did this, under whose authority?”, you don’t have access control.
Approve the envelope, enforce at runtime. Humans approve the boundaries: allowed tools, data classes, spend and volume limits, destinations. A policy gateway enforces them on every call. Reserve human approval for actions that cross a risk threshold.
Put prompts and tool manifests under change control. System prompts, tool descriptions, and model versions are production configuration. Version them, hash them, test them, and require approval to change them.
Design oversight for intervention, not attestation. A reviewer needs the evidence, the authority to stop the action, and enough time to use both. Measure override rates; a reviewer who never overrides is a signal, not a control.
Assume the model changes underneath you. Pin versions where you can. Where you can’t, run a standing evaluation suite on every vendor update and treat a failed eval like a failed deployment.
Make the evidence a by-product of operation. If evidence has to be assembled for the audit, it’s a narrative. If it’s emitted by the control as it runs, it’s proof.

Evidence that survives an audit that hasn’t been written yet

You can’t predict the exact audit criteria for 2027. You can predict what any competent auditor will ask: Show me it was designed, show me it ran, and show me it worked. The trick is shifting evidence from statements to records.

Control area	Weak evidence (a statement)	Strong evidence (a record)
AI inventory	A spreadsheet updated before the audit	An inventory fed by the gateway, listing every agent, model version, and connected tool seen in the period
Human oversight	“Reviewed” checkbox with a name	Approval log showing what was displayed, decision time, overrides, and actions blocked
Tool authorization	Policy stating least privilege	Gateway decision logs: allowed and denied calls, with the rule that fired
Prompt and config change	Prompt text in a wiki	Versioned, hashed prompts with approver, eval results, and deploy timestamp
Model drift	Vendor release notes in a folder	Eval-suite results on each model version, with pass thresholds and exceptions
Third-party AI suppliers	Vendor SOC 2 report	AI-specific supplier questionnaire, contractual change-notice terms, and your own testing results
Incident response	IR plan that mentions AI	Tabletop records for a prompt-injection or rogue-agent scenario, with findings closed
Impact assessment	One-time AI impact assessment	AIIA tied to each system in the inventory, re-run on material change

Notice what strong evidence has in common. It is continuous, machine-generated, and tied to a specific system and period. That is what lets an auditor rely on it without re-performing a non-deterministic process. It also happens to be exactly what your security team needs to detect an attack in progress.

Compliance crosswalk: one control, many frameworks

Build each control once and map it everywhere. This is how a governance-ahead program stays resilient as frameworks evolve: when a clause moves, you re-map; you don’t rebuild.

Control	ISO/IEC 42001 Annex A	NIST AI RMF	EU AI Act (high-risk)	ISO/IEC 27001:2022	OWASP ASI
AI system and agent inventory	A.4, A.6	MAP 1, GOVERN 1	Art. 9, Art. 49 registration	5.9	ASI04, ASI10
Per-agent identity and scoped access	A.6, A.9	MANAGE 2	Art. 15	5.15–5.18, 8.2, 8.5	ASI03
Runtime policy gateway (tools, egress)	A.6, A.9	MANAGE 2, MEASURE 2	Art. 15	8.16, 8.20–8.23	ASI01, ASI02
Prompt, manifest, and model change control	A.6	GOVERN 1, MANAGE 4	Art. 9, Art. 17	8.32, 8.25	ASI01, ASI04
Continuous evaluation and drift monitoring	A.6	MEASURE 2, MEASURE 3, MANAGE 4	Art. 9, Art. 15, Art. 72	8.16	ASI08, ASI10
Oversight designed for intervention	A.9	GOVERN 3, MANAGE 2	Art. 14, Art. 26	5.2	ASI09
Operational logging and evidence	A.6	MEASURE 2, MANAGE 4	Art. 12, Art. 19	8.15	All
AI impact assessment	A.5	MAP 1–MAP 5	Art. 27 (FRIA, where applicable)	5.34	—
Third-party model and tool assurance	A.10	GOVERN 6, MAP 4	Art. 25, Art. 26	5.19–5.23	ASI04
AI incident response	A.6, A.8	MANAGE 4	Art. 73 (serious incidents)	5.24–5.28	ASI08, ASI10

Mappings are indicative. Confirm against the current text of each framework and your system’s risk classification before relying on them in an audit.

A 90-day plan to govern ahead of the audit

Ninety days is enough to move from “we have a policy” to “we have evidence.” It is not enough to be finished, and that’s fine. The goal is a program that keeps pace with the technology rather than one that chases the auditor.

Days 1–30: See it

☐ Build an AI inventory covering models, agents, MCP servers, connected tools, and data classes touched
☐ Classify each system by risk, including EU AI Act tier where you have EU exposure
☐ Threat-model your top three agentic workflows against OWASP ASI01–ASI10
☐ Identify every place a “human oversight” control is a checkbox

Days 31–60: Bound it

☐ Issue per-agent identities and replace shared service accounts
☐ Route tool calls through a policy gateway with allowlists, parameter bounds, and egress rules
☐ Put system prompts, tool manifests, and model versions under versioned change control
☐ Redesign the highest-risk oversight point for intervention, with risk-tiered approvals

Days 61–90: Prove it

☐ Stand up a continuous evaluation suite and run it on every model or prompt change
☐ Turn on decision logging and retain it as audit evidence
☐ Run a tabletop on a prompt-injection or rogue-agent incident and close the findings
☐ Crosswalk your controls to ISO 42001, NIST AI RMF, and the EU AI Act, and run an internal audit against it

By day 90 you can hand a customer, a board, or a certification body something rare: evidence that your AI controls run, not just that they exist.

The bottom line

The technology is ahead of governance and compliance, and it will stay ahead. Control frameworks and audits will keep being worked through for years, because the systems they describe keep changing.

That is not a reason to wait. It is the reason to lead. The organizations that win enterprise deals, pass certification, and avoid the incident headline are the ones that govern to the risk today, enforce at runtime, and let the evidence speak. When the final audit criteria arrive, they won’t be scrambling to comply. They’ll be the reference implementation.

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services firms build AI governance that holds up in production and in the audit room. We took ShareVault through ISO/IEC 42001 Stage 2 certification on the first attempt and authored their MCP Governance Standard.

Start with a free AI Governance Readiness Assessment, or try our AI Governance Maturity Calculator and AI Risk X-Ray to see where your gap is widest.

“Disc Infosec” Deura, AICP, CISSP, CISM, ISO/IEC 42001 Lead Implementer, ISO/IEC 27001 Lead Implementer Principal Consultant, DISC InfoSec · PECB Authorized Training Partner info@deurainfosec.com · (707) 998-5164 · Book a call

Sources

OWASP’s 2026 LLM Top 10 and new Agent Control Standard — CSA Labs

Tags: AI audit, AI controls, Governing AI

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Oct 18 2026

If Your Frontier Model Vanished for 3 Weeks, Would Your Business Stop?

Category: AI,AI Risk,Information Security — disc7 @ 10:20 am

The Great AI Pullback: Sovereignty, Small Models, and the Runtime Controls Regulated Industries Now Demand

Oct 18, 2026 · @disc

Regulated firms are not abandoning AI. They are taking it back in-house. The question in the boardroom has changed from “which model is smartest?” to “who controls the model, the data, the runtime, and the off switch?”

That shift is rational. A frontier model wired into a loan decision, a claims workflow, or a financial data room is no longer a productivity tool. It is a third-party dependency with access to your crown jewels, an attack surface that takes instructions from untrusted text, and a regulated system whose evidence trail you may not own.

This post covers five things:

Why the pullback is happening now, and why it is accelerating in financial services, healthcare, defense, and public sector.
The sovereignty spectrum: six deployment tiers, from public API to airgapped small language model (SLM).
The threat model that applies at every tier, and the dangerous assumption that an airgap makes AI safe.
The bespoke runtime governance layer that must travel with the model wherever it runs.
A decision framework, a compliance crosswalk, and a 90-day plan.

The short version: sovereignty buys you control. It does not buy you safety. Only runtime governance does that.

Why the pullback is happening now

Six forces converged in 2026. Any one of them would justify a second look at a frontier dependency. Together they explain why risk committees are pulling production AI back behind their own walls.

1. Model access is now a continuity risk. On June 12, 2026, a U.S. Commerce Department export-control directive led Anthropic to suspend global access to Claude Fable 5 and Mythos 5, three days after launch. The controls were lifted on June 30 and access returned from July 1 (Engadget, The Next Web). The governance lesson is not about one vendor. It is that a hosted frontier model can disappear from your production path for reasons entirely outside your contract, your SLA, and your control. Analysts flagged it as the first time government intervention suspended general access to a model (The Batch).

2. Bank regulators carved generative and agentic AI out for separate treatment. The April 17, 2026 interagency model risk guidance (Fed SR 26-2) replaced SR 11-7 and described generative and agentic AI as novel and rapidly evolving, with a request for information on AI to follow (OCC). Commentators note it keeps banks fully responsible for third-party models, even when they cannot validate them (JD Supra). Translation: you own the risk of a model you cannot inspect.

3. Data sovereignty and foreign-access exposure. Every inference call to a hosted API is a data transfer. For EU banks under DORA, that call is an ICT third-party dependency subject to concentration-risk scrutiny. For anyone, it raises questions about jurisdiction, government access requests, and retention you cannot verify.

4. Agents changed the blast radius. A chatbot that answers wrong is an embarrassment. An agent with tool access that is manipulated by injected text can move money, export data, or change configurations. Runtime authority, not model quality, is now the dominant risk variable.

5. Evidence you do not own is evidence you cannot produce. ISO/IEC 42001 auditors, EU AI Act conformity assessors, and bank examiners ask for logs, version histories, and oversight records. If the model version, safety filters, and retention live on a vendor’s side, your evidence chain has a gap you cannot close by contract alone.

6. Small models got good enough for bounded work. Open-weight and small language models now handle classification, extraction, summarization, and retrieval-grounded Q&A well enough for many regulated workflows, at a fraction of the cost and inside your perimeter.

None of this means frontier models are leaving regulated industries. It means they are being demoted from “default for everything” to “governed exception for the work that genuinely needs them.”

The threat model that applies at every tier

Where a model runs changes who can reach it. It does not change how it fails. Start every review from one rule: treat every model output as untrusted input, and every input the model reads as potentially adversarial instructions.

The table maps the threats that matter most in regulated production to the OWASP Top 10 for LLM Applications and the OWASP Agentic Security Initiative (ASI) categories, and shows how the risk shifts when you move from a hosted frontier API to a private or airgapped deployment.

Threat	OWASP mapping	Hosted frontier API	Private / airgapped model
Prompt injection, direct and indirect	LLM01, ASI01 Goal Hijack	Present. Any document, email, or ticket the model reads can carry instructions	Unchanged. Internal content is still untrusted content
Tool misuse and excessive agency	LLM06, ASI02	Present wherever agents hold tools	Unchanged, often worse: internal agents get broader access because “it’s inside”
Sensitive data disclosure	LLM02	Higher: prompts, files, and outputs leave your perimeter	Lower in transit, but outputs can still leak across user and tenant boundaries
Identity and privilege abuse	ASI03	Shared API keys, over-scoped service accounts	Same, plus local admin and GPU-host credentials
Model and data supply chain	LLM03, LLM04, ASI04	Vendor-controlled: silent version changes, provider outages, access suspension	Shifts to you: weight provenance, unsafe serialization formats, poisoned fine-tune data
Memory and context poisoning	ASI06	Present in agent memory and RAG stores	Unchanged. Sovereign RAG is still poisonable RAG
Unbounded consumption	LLM10	Cost and rate-limit exposure	Capacity exhaustion on finite GPUs, which becomes an availability risk
Cascading and rogue agent behavior	ASI08, ASI10	Present in multi-agent workflows	Unchanged
Model weight theft	Supply chain and insider	Not your asset to lose	New crown jewel to protect, especially for fine-tuned models trained on regulated data

One test cuts through most of this: the lethal trifecta. An agent with access to private data, exposure to untrusted content, and a way to communicate externally can be steered to read secrets and send them out. Break any one leg and the attack path closes. Airgapping removes much of the third leg. It does nothing about the first two.

The sovereignty fallacy: what an airgap does not fix

The most dangerous sentence in a regulated AI program right now is “it’s on-prem, so it’s safe.” Bringing a model home moves risk around. It does not remove it. Here is what stays with you, or gets worse.

You inherit the vendor’s job. With a hosted API, the provider runs red-teaming, safety classifiers, abuse monitoring, and patching. Self-host an open-weight model and all of that becomes your control obligation, with your headcount and your evidence.

SLMs fail differently, not less. Small models are less capable at refusing manipulation, more prone to confident errors outside their training distribution, and rarely ship with mature safety tooling. A model that is “good enough” on your accuracy benchmark may be untested against adversarial inputs.

Insiders become the primary threat actor. An airgap removes most external attackers and concentrates risk in the people and processes with legitimate access: data scientists with weight access, admins with GPU-host root, contractors loading “helpful” fine-tunes.

Supply chain risk moves inside the wall. Weights downloaded from public hubs, pickle-based checkpoints that execute code on load, unpinned model versions, and fine-tuning datasets of unknown provenance all cross the airgap on removable media. Poisoned training data is effectively unrecoverable once baked into weights.

Airgaps get bridged. Patches, new weights, retrieval corpora, and log exports all need a transfer path. Every transfer path is an ingress and egress control that needs ownership, scanning, and an approval record.

Monitoring goes dark. Many observability, DLP, and AI-security products assume cloud connectivity. Teams that airgap the model often airgap their visibility with it.

Model drift becomes model rot. A frozen private model does not get vendor improvements or safety fixes. Without a deliberate update and re-evaluation cycle, it ages in place while threats evolve.

The conclusion is not “don’t go sovereign.” It is that sovereignty is a placement decision, and placement is only one layer of control. The layer that actually decides what a model may do is the runtime.

Bespoke runtime governance: the controls that travel with the model

Runtime governance is the set of controls that sit between the model and everything it can touch, enforced by deterministic code rather than by the model’s good behavior. It is “bespoke” because it must encode your data classes, your authority limits, and your regulatory obligations. A vendor’s generic guardrail cannot know that your agent may draft a wire instruction but never release one.

The key property: these controls are model-agnostic. Swap a frontier API for a private SLM, or move from cloud to airgap, and the control plane stays. That is what makes sovereignty survivable.

Layer	What it enforces	Minimum control
1. Identity	Every agent and model call is attributable	Unique agent identity, short-lived scoped credentials, no shared admin keys, token audience validation
2. Input boundary	Untrusted content is labeled and isolated	Provenance tags on retrieved content, separation of instructions from data, injection detection as depth (never as the control)
3. Policy decision point	What the model may do, outside the model	Allowlisted tools, parameter-level policy checks, risk-tiered approval lanes, deny by default
4. Tool and MCP gateway	How actions reach real systems	Signed tool manifests, pinned versions, OAuth 2.1 with PKCE, confused-deputy protections, no dynamic tool loading in privileged flows
5. Egress control	Where data can go	Destination allowlists, SSRF protection, DLP on outputs, no open internet for privileged agents
6. Output validation	What leaves the model and enters a sink	Schema validation, encoding for downstream systems, PII and secrets scanning
7. Model routing	Which model handles which data class	Data-class-aware routing: restricted data only to sovereign tiers, frontier calls only for approved classes
8. Evidence and monitoring	Proof of what happened	Immutable logs of prompts, retrieved context, tool calls, approvals, and model version; behavioral baselines; drift alerts
9. Containment	Stopping it fast	Kill switch per agent, credential revocation, rollback, blast-radius budgets, fail-closed defaults

Two ideas from earlier DISC InfoSec posts plug directly into layer 3. First, approve the envelope, not the edit: pre-authorize bounded classes of action, enforce them at runtime, and reserve synchronous human approval for actions that escape the envelope. Second, your system prompt is production code: guidance lives in the prompt, but enforcement cannot. Anything you rely on the prompt to prevent needs a matching control in this table.

Layer 7 deserves emphasis because it is how most regulated firms will actually operate: not “sovereign or frontier,” but a router that sends each request to the cheapest tier allowed for its data class and risk level, and logs why.

Decision framework: match the workload to the tier

Place each AI use case by two questions: how sensitive is the data it touches, and how much authority does it hold? The answer picks the lowest-cost tier that is still defensible to an auditor.

Workload profile	Example	Recommended tier	Model class	Non-negotiable controls
Public data, advisory output	Marketing drafts, public research summaries	Hosted frontier API	Frontier	Acceptable-use policy, output review, no regulated data
Internal data, advisory output	Policy Q&A, meeting summaries	Enterprise API with zero data retention, or dedicated tenant	Frontier or mid-size	DLP on inputs, contract terms on retention and training, logging
Confidential data, bounded actions	Ticket triage, document classification, KYC extraction	VPC or private cloud	Open-weight mid-size or SLM	Tool allowlists, egress control, schema-validated outputs
Regulated data, consequential decisions	Credit, claims, fraud, clinical triage	Private cloud or on-prem	Fine-tuned SLM, frontier only for approved sub-tasks	Model routing by data class, human approval lanes, full evidence chain, TEVV
Classified, export-controlled, or critical infrastructure	Defense, grid operations, national-security workloads	Airgapped	Vetted SLM or open-weight	Controlled transfer path, insider controls, weight protection, offline monitoring

When an SLM is the right call. The task is narrow and repeatable (classify, extract, route, summarize against a known corpus), latency or cost matters at volume, the data cannot leave your perimeter, and you can evaluate the model against a labeled test set you own.

When you still need a frontier model. The task requires open-ended reasoning, long multi-step planning, complex code, or broad world knowledge, and errors are recoverable. In regulated flows, route only de-identified or minimized context to it, keep the action authority on your side of the policy engine, and keep a tested fallback path for the day access disappears.

The continuity test. For every production use of a hosted model, answer one question in writing: if this model were unavailable for three weeks starting tomorrow, what happens? If the answer is “the business process stops,” you have a concentration risk to treat, not just a vendor to monitor.

Compliance crosswalk

The good news: none of this requires a new framework. The controls above map cleanly to the ones auditors and examiners already use. Build once, evidence many times.

Control theme	ISO/IEC 42001	ISO/IEC 27001:2022	NIST AI RMF	EU AI Act	DORA (EU financial)
AI system inventory and tiering	Clause 6.1, A.6 life cycle	5.9 asset inventory	MAP 1, GOVERN 1.6	Art. 6 classification, Art. 9 risk management	ICT asset register
Model and vendor dependency	A.10 third-party relationships	5.19 to 5.23 supplier and cloud services	GOVERN 6, MANAGE 3	GPAI provider duties, Arts. 53 to 55	Arts. 28 and 29, third-party and concentration risk
Continuity and exit for hosted models	Clause 8.1 operational control	5.30 ICT readiness for continuity	MANAGE 2.4	Art. 15 robustness	Exit strategies under Art. 28
Data residency and leakage	A.7 data for AI systems	8.12 data leakage prevention	MAP 4, MEASURE 2.10	Art. 10 data governance	ICT data security
Runtime authorization and oversight	A.6.2.6 operation and monitoring	8.2 privileged access	MANAGE 2, MEASURE 3	Art. 14 human oversight	Access and change control
Logging and evidence	Clause 9.1, A.6.2.8 event logs	8.15 logging, 8.16 monitoring	MEASURE 4	Art. 12 record-keeping	Incident detection and reporting
Adversarial testing	Clause 8.2 risk assessment	8.29 security testing	MEASURE 2.7	Art. 15 cybersecurity	Digital operational resilience testing

Timing matters. Under the Digital Omnibus, EU AI Act high-risk obligations now apply from 2 December 2027, which sounds distant but is barely enough time to stand up a governed sovereign deployment with a defensible evidence trail. U.S. banks should expect the promised interagency AI request for information to ask exactly the questions in this table.

email info@deurainfosec.com. or comment on the LinkedIn post if you have a question

https://www.deurainfosec.com/

Tags: Frontier Model

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Oct 14 2026

14 Months to Comply. 12 Months of Work. The High-Risk AI Playbook Nobody Has Started

Category: AI,AI Governance,AI Risk,Information Security — disc7 @ 12:10 pm

The High-Risk AI System Readiness Playbook

How to find, fix, and prove control over your highest-risk AI before the regulator — or your biggest customer — asks

Oct 11, 2026 · @disc

You have roughly 14 months until the EU AI Act’s Annex III high-risk obligations apply on 2 December 2027, and a credible readiness program for a single high-risk system takes 9 to 12 of them. That math leaves almost no slack.

In June I argued that the Digital Omnibus delay was a trap: regulatory relief is not risk relief. The delay is now law. What most organizations still lack is the playbook for spending the reprieve well — one that treats a high-risk AI system as a security and governance problem first and a paperwork problem second.

This post is that playbook. It covers how to confirm you are actually high-risk, what an assessor will ask to see, the threats that make these systems dangerous, the controls that mitigate them, and a 14-month roadmap. It draws on our work taking Virtual Data Room through ISO/IEC 42001 Stage 2 certification on the first attempt, plus the EU AI Act, NIST AI RMF, OWASP and MITRE ATLAS.

Who this is for: CISOs, CAIOs, GRC leads and product owners at B2B SaaS and financial services firms that build or deploy AI used in hiring, credit, insurance pricing, education, critical infrastructure or access to essential services — or that sell to firms that do.

Where the clock actually stands

The Omnibus moved dates, not definitions: a system that was high-risk before Regulation (EU) 2026/1744 is still high-risk today. The regulation was published in the Official Journal on 24 July 2026 and entered into force on 27 July 2026.

Obligation	Applies from	Status
Prohibited practices, AI literacy	2 Feb 2025	In force
GPAI model obligations	2 Aug 2025	In force, not delayed
New Art. 5 prohibitions, watermarking	2 Dec 2026	~7 weeks away
Annex III stand-alone high-risk systems	2 Dec 2027	Deferred from 2 Aug 2026
Annex I high-risk AI in regulated products	2 Aug 2028	Deferred from 2 Aug 2027

Two details matter for planning. The delay was driven largely by slow progress on harmonised standards, so expect standards to land late and leave less time to align with them. And the Omnibus expanded the AI Office’s supervisory role over systems built on general-purpose models — the exact architecture most SaaS AI features now use.

The regulator is also not your first deadline. Enterprise procurement teams are already sending AI addenda that ask for Article 9–15 evidence. Your customer’s questionnaire will arrive long before a market surveillance authority does.

Step zero: confirm you are actually high-risk

Misclassification is the most expensive mistake in this regime, in both directions. Under-classify and you ship a non-conforming system; over-classify and you burn a year of engineering on obligations that never applied. Work through five gates, in order, and write down the answer to each.

Is it an AI system at all? Rules engines and fixed scoring formulas usually are not. Models that infer outputs from inputs with some autonomy usually are.
Is it prohibited? Check Article 5 first, including the two new prohibitions that apply from 2 December 2026. A prohibited practice cannot be “managed” as high-risk.
Is it an Annex I safety component, or listed in Annex III? For SaaS and financial services, the Annex III areas that bite most are employment (screening, ranking, performance monitoring), creditworthiness and credit scoring, life and health insurance pricing, and education access or assessment.
Does the Article 6(3) exception genuinely apply? An Annex III system escapes high-risk status only if it performs a narrow procedural or preparatory task, or only refines a completed human decision, without materially influencing the outcome. Any system that profiles natural persons is always high-risk. If you claim the exception, document the assessment before launch — authorities can ask for it.
What is your role? Providers carry Articles 9–17 and conformity assessment; deployers carry Article 26. Watch Article 25: putting your name on a system, substantially modifying it, or repurposing a general-purpose model into an Annex III use makes you the provider.

That last gate catches most of our SaaS clients. Wrap a foundation model in a candidate-ranking feature and you are no longer “just using an LLM” — you are the provider of a high-risk system, and the model vendor’s documentation covers only a fraction of what you now owe.

What an assessor will ask to see

Every obligation resolves to an artifact you can hand over, and the artifact is what gets assessed. If you cannot point to it, the control does not exist.

Article	Obligation	Evidence an assessor expects
Art. 9	Continuous risk management	Living AI risk register; foreseeable-misuse analysis; residual-risk acceptance signed by an accountable owner; pre-release test results against predefined thresholds
Art. 10	Data governance	Dataset datasheets; provenance and lineage records; representativeness and bias analysis; documented gaps
Art. 11 / Annex IV	Technical documentation	Versioned technical file: architecture, training method, metrics, change history, standards applied (retain 10 years)
Art. 12	Automatic logging	Event logs that can reconstruct any decision; deployers retain at least 6 months
Art. 13	Transparency to deployers	Instructions for use stating accuracy, known limits, performance by group, oversight measures
Art. 14	Human oversight	Override and stop mechanisms; named, trained overseers; override and escalation records
Art. 15	Accuracy, robustness, cybersecurity	Declared accuracy metrics; drift monitoring; adversarial and red-team test reports; fail-safe design
Art. 17	Quality management system	Documented QMS covering design control, testing, data, change management, supplier security, accountability
Art. 43, 47–49	Conformity, declaration, registration	Conformity assessment record; EU Declaration of Conformity; CE marking; EU database entry
Art. 72–73	Post-market monitoring, incidents	Monitoring plan; serious-incident runbook with regulator notification paths
Art. 26 (deployers)	Use as instructed	Use-policy, input-data controls, monitoring and suspension procedure, worker notification, DPIA where required

For most Annex III areas (2 through 8), conformity assessment is internal control under Annex VI — no notified body. That sounds easier. In practice it means nobody external will catch your gaps before a regulator, a plaintiff or a customer does.

Why high-risk AI fails: four threat families

Article 15 names the attacks explicitly — data poisoning, model poisoning, adversarial examples, confidentiality attacks and exploitation of model flaws. That list was written before agentic AI went mainstream. In the systems we assess today, the harms cluster into four families.

Attacks on the model. Poisoned training or fine-tuning data, tampered weights or adapters, adversarial inputs, and extraction attacks such as membership inference and model inversion. Catalogued in MITRE ATLAS.
Attacks through the interface. Direct and indirect prompt injection, jailbreaks, and sensitive data leaking through outputs or retrieval. In an LLM-backed scoring or screening flow, a single crafted résumé or loan document can carry instructions. See the OWASP Top 10 for LLM Applications.
Attacks through agency and the supply chain. Over-permissioned tools, confused-deputy calls, poisoned MCP tool manifests, compromised model hubs and third-party APIs. An agent that can act inherits every permission you gave it, and every weakness in what it calls.
Failures without an attacker. Data drift, feedback loops that amplify bias, silent accuracy decay, and automation bias in the humans meant to catch errors. These cause most real-world harm, and they are the hardest to see on a dashboard.

The regulation cares about outcomes for people — a wrongly rejected applicant, a mispriced policy — not about which family caused them. Your controls have to cover all four.

The mitigation playbook, risk by risk

Article 9(4) sets the order of preference: eliminate the risk by design first, mitigate what remains, and only then rely on information and warnings. Each row below follows that order.

Risk	Design-level control	Operational control	Proof it works	Maps to
Biased or unrepresentative data	Exclude proxy features; set representativeness targets per protected group before training	Fairness metrics (e.g. selection-rate ratio, equalized odds) gated in CI	Bias test report per release; dataset datasheet	AI Act Art. 10; ISO 42001 A.7; NIST MEASURE 2.11
Data and model poisoning	Signed datasets and model artifacts; provenance checks at ingestion	Hash verification on deploy; anomaly detection on fine-tuning data	AI bill of materials; signature verification logs	Art. 15; ATLAS AML.T0020; ISO 27001 A.8.28
Prompt injection	Treat all retrieved and uploaded content as untrusted; separate instructions from data; constrain outputs to a schema	Input and output filtering; canary tokens; injection test suite in CI	Red-team report with pass rates per release	Art. 15; OWASP LLM01
Excessive agency and tool misuse	Least-privilege, scoped tokens per tool; no autonomous final decisions on people	Human approval for consequential actions; egress allowlists; per-tool rate limits	Tool permission inventory; approval logs	OWASP LLM06; OWASP Agentic Top 10
MCP and AI supply chain	Pinned, integrity-checked tool manifests; OAuth 2.1 with PKCE and audience-bound tokens	Vendor AI due diligence; monitoring of model and API version changes	Supplier register; manifest integrity checks	Art. 17(1)(l); ISO 42001 A.10; ISO 27001 A.5.19–5.22
Data leakage and model extraction	Data minimization; no sensitive data in prompts or training without a lawful basis; output redaction	Query rate limiting; membership-inference testing	DPIA; extraction test results	Art. 10(5), 15; GDPR Art. 35; OWASP LLM02
Drift and silent accuracy decay	Declared accuracy thresholds with fail-safe fallback to human review	Production monitoring against the declared metrics; retraining triggers	Monitoring dashboard; post-market monitoring reports	Art. 15, 72; NIST MANAGE 4.1
Feedback-loop bias	Isolate model outputs from future training labels	Periodic outcome audits by group	Outcome audit records	Art. 15(4); ISO 42001 A.6.2.6
Automation bias	Interfaces that show confidence, reasons and uncertainty; friction before rubber-stamping	Overseer training; sampled second review of approvals	Override-rate and review-time metrics	Art. 14; NIST GOVERN 3.2
Opaque incidents	Decision-level event logging from day one	Serious-incident runbook with 15-day regulator notification path	Tabletop exercise record	Art. 12, 73

The security controls in this table are not new. What is new is that Article 15 makes them a legal condition of placing the system on the market, and Article 11 requires you to prove each one in the technical file.

Human oversight that survives scrutiny

A “reviewed by” checkbox records what the overseer said, not what the overseer did. That gap is where most oversight programs fail an assessment. Article 14 requires people who can understand the system, recognize automation bias, and actually override or stop it.

Oversight you can defend has five properties:

Authority. The overseer can reject, reverse or halt the output without escalation, and the stop mechanism is tested.
Competence. The overseer is trained on the system’s limits and on automation bias, and the training is recorded.
Capacity. Review volume per person is set so a real review is possible. Forty decisions an hour is not oversight.
Information. The interface shows confidence, the main factors and the cases the model is known to handle badly.
Evidence. Logs capture what the overseer saw, how long they spent, and whether they changed the outcome.

Then measure it. An override rate near zero for months is a warning sign, not a success metric: either the model is perfect or nobody is really looking. Sampled second reviews tell you which.

One control set, many frameworks

Build the high-risk program once, on an ISO/IEC 42001 AI management system, and let it answer the EU AI Act, NIST AI RMF and your customers’ questionnaires together. ISO 42001 is not a harmonised standard and does not grant presumption of conformity on its own. It does give you the management-system spine — policy, roles, risk process, internal audit, management review — that Article 17 expects a quality management system to have.

AI Act requirement	ISO/IEC 42001	NIST AI RMF	ISO/IEC 27001:2022
Art. 9 risk management	Cl. 6.1, 8.2–8.3; AI risk treatment	MAP, MEASURE, MANAGE	Cl. 6.1, 8.2
Art. 10 data governance	Annex A.7	MAP 2.3, MEASURE 2.11	A.5.12, A.8.11
Art. 11 technical documentation	A.6.2.7	GOVERN 1.4	A.5.37
Art. 12 logging	A.6.2.8	MEASURE 2.4	A.8.15
Art. 13 transparency	A.8	GOVERN 4.2, MAP 3.3	—
Art. 14 human oversight	A.9	GOVERN 3.2, MANAGE 2.4	—
Art. 15 robustness and security	A.6.2.4, A.6.2.6	MEASURE 2.5–2.7	A.8.8, A.8.25–8.29
Art. 17 QMS	Cl. 4–10	GOVERN	Cl. 4–10
Art. 27 FRIA / impact	A.5 AI system impact assessment	MAP 5.1	—
Art. 72–73 monitoring, incidents	Cl. 9.1, 10.2	MANAGE 4.1–4.3	A.5.24–5.28

If you already run an ISO 27001 ISMS, much of the Article 15 and Article 17 evidence already exists. The work is extending scope to AI-specific risks and adding the AI impact assessment, data and oversight controls that 27001 never asked for. That is exactly the path we took at ShareVault.

Eight readiness failures we keep seeing

Misclassification by optimism. Calling a screening tool “decision support” without documenting why Article 6(3) applies.
Risk management as a one-time workshop. Article 9 requires a process that runs for the system’s whole life, fed by production data.
No bias analysis on the actual population. Fairness tested on the training set, never on the people the system scores in production.
Logs that cannot reconstruct a decision. Application logs exist; the inputs, model version and output for a specific applicant do not.
Oversight theater. Humans in the loop with no time, no information and no authority to say no.
Security scoped to the app, not the model. Pen tests that never touch prompt injection, poisoning or tool permissions.
Vendor documentation mistaken for your own. The model provider’s system card does not cover your use case, your data or your users.
No AI literacy record. Required since February 2025, and the easiest finding for an assessor to write up.

The bottom line

High-risk readiness is not a documentation sprint you run in autumn 2027. It is a security and governance program that has to be operating — producing logs, bias reports, override records and red-team results — long enough to prove it works. Start now and December 2027 is a formality. Start late and it is a fire drill with your largest customers watching.

At DISC InfoSec we have done this work as practitioners, not just advisors: we took Virtual Data Room through ISO/IEC 42001 Stage 2 certification on the first audit and wrote its MCP governance standard. We bring the same approach to B2B SaaS and financial services teams facing AI Act, customer or board deadlines.

Three ways to start this week:

Run our free EU AI Act Risk Classifier to confirm where each of your AI systems lands.
Book a High-Risk AI Readiness Assessment: a scoped gap analysis against Articles 9–15 and 17, with a prioritized 14-month plan.
Talk to us about a vCAIO retainer to run the program through conformity.

“DiscInfoSec” Deura, CISSP, CISM, ISO/IEC 42001 and 27001 Lead Implementer – info@deurainfosec.com – (707) 998-5164 – Book a call

Sources

EU Digital Omnibus on AI enters into force — National Law Review
EU Digital Omnibus on AI enters into force — Mondaq
EU AI Act Digital Omnibus: enterprise risk recalibration — Cloud Security Alliance
Regulation (EU) 2024/1689 (AI Act), Articles 5, 6, 9–17, 25–27, 43–49, 72–73
ISO/IEC 42001:2023; NIST AI RMF 1.0; OWASP Top 10 for LLM Applications 2025; MITRE ATLAS

Tags: EU AI Act

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Oct 05 2026

AI Handoffs: How Uncertainty Propagates—and How to Fix It

Category: AI,AI Agent,AI Governance,AI Risk,Information Security — disc7 @ 12:37 pm

Probabilistic Lineage: How Much Uncertainty Was Already Baked Into the Input?

Upstream models classify, repair, and infer before your main model ever sees the data. Nobody tracks how much probability shaped the input. The research landscape, the traps, and a workable design.

Here’s a question I keep coming back to, and I don’t think the industry has an answer yet.

We’re embedding probabilistic components throughout the stack — increasingly upstream of the main inference. Data gets classified by a model, repaired by a model, enriched by a model, summarised by a model, and then handed to the agent that actually decides something. By the time the consequential model sees its input, that input may have been probabilistically transformed four or five times.

How much probability has already shaped the input before the next model sees it?

And the follow-ons that matter operationally: should the downstream agent know? Should it know how reliable those earlier decisions were? At what point does accumulated probabilistic depth warrant different treatment from deterministically produced data?

This is usually filed as an ML reliability question. It’s a governance question. My last post argued that the harness is where intent becomes an execution contract, and the third property of that contract is evidence a third party can verify. Conventional lineage tells a third party what transformed what. It says nothing about what the result is worth — and a derived artifact that gets treated as a source is exactly where confidence is manufactured out of nothing.

What already exists

More than I expected when I went looking, spread across four communities that mostly don’t cite each other.

Systems-level uncertainty propagation. Xia, Zhu, Gao, Lu, Xue and Sejdinovic’s Uncertainty Propagation in LLM-Based Systems (arXiv 2604.23505, April 2026) is the closest direct treatment. Their framing: uncertainty is usually studied at the level of a single output, but deployed applications are compound systems where uncertainty is transformed and reused across model internals, workflow stages, component boundaries, persistent state, and human processes. Their open-problems section names the hard part precisely — composition requires two distinct properties, calibration preservation (does the composed signal stay calibrated against outcomes) and scope preservation (does it retain a valid relationship to the thing it was originally about). Current handoff mechanisms assume both and provide formal conditions for neither.

The snowball finding, which is the most operationally useful result I’ve seen here (arXiv 2608.14588). Hallucinations injected at stage one of a multi-agent pipeline don’t merely persist — they transform. A raw numerical fact becomes a derived computation, becomes narrative prose, becomes an editorially approved conclusion. At each transformation, detectability degrades near-irreversibly. Their prescription is a placement rule: put your verification at the first handoff, where roughly three-quarters of hallucinations are still catchable, rather than at the end.

That reframes the whole question. Depth isn’t a counter, it’s a change of form. A number you can check against a source becomes a sentence you can only check against a judgement.

Step-level propagation methods exist too — SAUP (ACL 2025) propagates uncertainty through each step of an agent’s reasoning; UProp (Duan et al., 2026) addresses multi-step decision-making; there’s a 2026 taxonomy survey mapping estimation methods to pipeline stages at arXiv 2609.07395.

Older prior art the LLM literature mostly ignores, and which I’d argue is more relevant than any of the above:

Uncertainty-lineage databases. Trio, out of Stanford in the mid-2000s, made uncertainty and lineage jointly first-class in the data model. It is the closest thing to a solved version of this problem — twenty years early, in a deterministic-query setting.
Sculley et al., “Hidden Technical Debt in Machine Learning Systems” (2015). Names correction cascades and undeclared consumers. What we’re discussing is that paper’s failure mode with a generative model in the loop.
Taint tracking and information-flow control. I think this is the right mental model — better than error bars. Probabilistic origin behaves like a taint label: it propagates through derivation, it’s difficult to remove, and the interesting design question is where you place a declassifier.
Conformal prediction, because it’s distribution-free and composes under explicit risk budgets, unlike verbalised confidence.

And the transport layer, which omits all of this by design. A2A deliberately leaves out model-level metadata, quality signals, provenance and trust-domain enforcement; MCP operates at the tool level and doesn’t address delegation semantics. On the memory side the gap is explicit — a wire schema for a shared memory entry would need authorship, time, confidence and provenance, and neither A2A’s Parts and Artifacts nor MCP’s Resources type that. Meanwhile the identity side has moved faster: invocation-bound capability tokens chain verifiable delegation with provenance binding across MCP and A2A. We can prove who transformed something before we can express how confidently.

Three traps to avoid before you build anything

Naive composition is worse than nothing. Multiplying stage confidences assumes independence, and pipeline errors are strongly correlated — shared training data, shared retrieval corpus, shared prompt conventions, shared model family. You get a number that looks rigorous and is systematically overconfident. False precision laundering is a worse failure than an absent signal, because it licenses downstream reliance rather than merely failing to restrict it.

Verbalised model confidence is poorly calibrated. Propagating it faithfully propagates a miscalibration with a decimal point attached.

Scope drift. “85% confident this field is a date” and “85% confident this conclusion is sound” are not the same object. Composing them produces a number about nothing. This is the scope-preservation problem in practice, and it’s why a single scalar is the wrong data structure.

The implication: don’t build a confidence number. Build a provenance class. Categorical, honest, and hard to misuse.

A workable design

Six parts. None require research breakthroughs; all of them require someone deciding to do it.

1. Track derivation as a label, not an error bar

Attach to every material input a small, categorical record: what kind of transformation produced it, by what component, with what verifiability. Borrowed straight from taint tracking — the label propagates through derivation and can only be cleared deliberately.

Minimum viable classes, ordered by how far they sit from checkable ground truth:

Class	Meaning	Checkable against
Observed	Recorded directly from a system of record	The source system
Deterministic-derived	Computed by rule from observed data	Recomputation
Model-classified	A label or extraction produced by a model	The underlying artifact
Model-repaired	Missing or malformed values inferred by a model	Nothing, usually
Model-synthesised	Prose, summary, or narrative generated from the above	Judgement only
Model-asserted	A conclusion presented as fact, origin no longer visible	Nothing

Note the bottom two rows are where the snowball lands. Once something becomes model-asserted, it will be consumed as observed unless the label travels with it.

2. Record the form transition, not just the hop count

Depth measured as “three models touched this” is nearly useless. Depth measured as “this crossed from classification into synthesis” is actionable, because that’s the transition after which detectability collapses. Log the transition, not the tally.

3. Verify at the first handoff

The placement economics are counterintuitive and well-evidenced: verification is cheapest and most effective early, because the artifact is still in a form you can check against a source. A validation gate between extraction and computation earns more than a review step before publication. Most pipelines do the opposite — they put the human at the end, where the only thing left to check is plausible prose.

4. Carry the label across handoffs yourself

The protocols won’t do it for you, so put it in your own envelope. A minimal handoff record per material input:

Field	Purpose
`derivation_class`	The categories above
`producing_component` + version	Attribution, and change detection
`form_transitions`	Ordered list of class changes, with the component that caused each
`ground_truth_anchor`	Identifier of the last checkable source, if any
`verification_state`	Unverified / machine-verified / human-verified, with timestamp
`scope`	What the confidence, if any, is about
`policy_version`	The rules in force when this was produced

That’s six fields and a version string. It is not a research programme; it’s a JSON object that most teams could add to their orchestration layer this quarter. And it plugs directly into the execution contract from my previous post — provenance class becomes a declared property of every input, the way tool worst-case is a declared property of every action.

5. Escalate treatment by depth — as a tier escalator, not a threshold

Don’t look for the magic number of transformations at which data becomes untrustworthy; there isn’t one. Instead raise the required treatment when any of these hold:

The input crossed a form transition (classification → inference → synthesis → assertion)
Part of the chain runs through a component you don’t control — a vendor’s model, an external agent, an MCP server
The artifact will be relied on as fact by a party who can’t see its lineage
The consequential action is irreversible, regulated, or rights-affecting

That last combination — a model-asserted input feeding an irreversible action — is the one I’d treat as a hard gate requiring human authorisation, regardless of how confident anything claims to be.

6. Place declassifiers deliberately

This is the part that makes the whole thing tractable, and it comes straight from information-flow control. A taint label that can never be cleared eventually paints everything, and a system where all data is tainted has the same information content as one where none is. So you need explicit declassification points: places where a derived artifact is re-anchored to checkable ground truth and its label is legitimately reset.

Three that work in practice: re-derivation from the system of record; human verification with attribution and a timestamp; and a conformal or statistical check with a stated coverage guarantee. What doesn’t work: a downstream model asserting that the upstream output looked fine. That’s not declassification, it’s laundering — and it’s the default behaviour of most multi-agent pipelines today.

Where this meets the frameworks

Worth being precise, because the gap is real and worth naming to a client or an auditor.

ISO/IEC 42001 — A.7.3 to A.7.6 cover data acquisition, quality, and provenance, and A.6.2.8 covers event logs sufficient for investigation and accountability. A.10.3 covers allocating responsibilities across the AI value chain. All of these support what I’ve described. None of them require uncertainty to be propagated across component boundaries.
NIST AI RMF — MAP covers documenting provenance and intended use; MEASURE covers uncertainty quantification. Again, no composition requirement.
EU AI Act — Article 10 imposes data governance and quality obligations on high-risk systems; Article 13 requires instructions for use that let deployers interpret output. Arguably an input whose provenance class is unknowable makes Article 13 hard to satisfy honestly, but nothing states that.

So: every major framework requires you to document provenance, and none requires you to carry uncertainty across a handoff. That’s a genuine gap between what the standards ask and what compound systems need, and it’s the kind of gap that gets closed by practice first and by revision later. Organisations that start now will be ahead of the requirement rather than retrofitting under it — which is the same position ISO 42001 early adopters found themselves in.

Five questions I’d ask in an assessment

For your most consequential model, can you state the derivation class of each material input?
Where in your pipeline does something stop being checkable against a source and start being checkable only against judgement?
Where is your verification placed — at the first handoff, or before publication?
When an artifact crosses into a component you don’t control, what travels with it?
Where are your declassification points, and is any of them just a model saying it looks fine?

If question five turns up a model verifying a model, you’ve found the highest-leverage fix in the system.

From the practitioner’s chair

Two observations from the audit side.

The recurring lesson from leading ShareVault through ISO 42001 Stage 2 certification and then serving as internal auditor: controls are rarely the failure point, evidence is. Probabilistic lineage is the same lesson arriving one layer deeper. It isn’t enough to show that a decision was made and logged — at some point a counterparty will ask what the decision rested on, and “a model said so, and before that another model said so” is not an answer that survives the question.

And from auditing a client’s MCP Governance Standard, where nearly all 27 changes in my v1.1 redline reduced to one idea — authority must be bound to a specific action rather than held ambiently by a component — there’s a direct analogue here. Confidence must be bound to a specific claim rather than held ambiently by an artifact. An unaudienced token is authority without a destination; a floating confidence score is reliability without a referent. Same failure, different currency.

What to do in the next 90 days

Map one consequential pipeline end to end and mark every point where a model touched the data before the final decision. Most teams find more than they expect, and the surprises are usually upstream.
Classify the inputs using the six derivation classes. No numbers yet — categories only.
Find the form transitions and move one verification step earlier.
Add the envelope to your orchestration layer. Seven fields.
Name your declassification points, and remove any that are a model checking a model.
Add a hard gate where model-asserted input meets an irreversible action.

The honest summary of the field: there are four partial answers, none of them compose, and the protocols deliberately don’t carry the field. That’s not a reason to wait. It’s an argument for building the categorical version now — because the teams that can say what their inputs are made of will be the ones able to answer the counterparty who eventually asks.

#ProbabilisticLineage #UncertaintyPropagationLLM, #Agenthandofftrustsignals, #MCPA2Aprovenance, #ISO42001 #Dataprovenance, #Hallucinationsnowball, #Conformal prediction

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations govern compound AI systems — AI and agent inventories, data provenance and lineage design, risk tiering, harness and MCP permission review, human oversight placement, and the evidence architecture that maps to ISO/IEC 42001, NIST AI RMF, and EU AI Act Articles 10, 13, 14 and 26.

I led ShareVault through ISO 42001 Stage 2 certification on the first audit attempt as the internal practitioner, served as internal auditor, and authored their MCP Governance Standard.

Readiness path: free 15–20 minute readiness call → ISO 42001 gap assessment or ISO 27001 gap assessment → 7–10 day AI Governance Quick-Start → full AIMS/ISMS implementation → vCISO / vCAIO retainer.

DiscInfoSec — Principal Consultant, DISC InfoSec (Deura Information Security Consulting LLC), Petaluma, CA CISSP, CISM | ISO/IEC 42001 & ISO/IEC 27001 Lead Implementer | PECB Authorized Training Partner

calendly.com/hd-deurainfosec info@deurainfosec.com (707) 998-5164 deurainfosec.com

References

Xia, B.; Zhu, L.; Gao, E.; Lu, Q.; Xue, M.; Sejdinovic, D. — Uncertainty Propagation in LLM-Based Systems, arXiv:2604.23505, April 2026
The Hallucination Snowball: Modeling Error Propagation as State Transitions in Multi-Agent LLM Pipelines, arXiv:2608.14588, 2026
Uncertainty Propagation on LLM Agent (SAUP), ACL 2025; Duan et al., UProp, 2026; Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study, arXiv:2609.07395
Widom, J. et al. — Trio / uncertainty-lineage databases, Stanford, mid-2000s
Sculley, D. et al. — Hidden Technical Debt in Machine Learning Systems, NeurIPS 2015
Vovk et al., conformal prediction; conformal risk control literature
Model Context Protocol; Agent2Agent (A2A) specification and Agent Cards; agent identity and delegation-provenance work (invocation-bound capability tokens)
ISO/IEC 42001:2023 — A.6.2.8, A.7.3–A.7.6, A.10.3; NIST AI RMF 1.0 — MAP, MEASURE; Regulation (EU) 2024/1689, Arts. 10, 13, 14, 26

Tags: AI handoffs, AI Uncertainty

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Sep 29 2026

The Security Risks Hiding in AI Agent Memory

Category: AI,AI Agent,AI Governance,AI Governance Enforcement,AI Logs,AI Risk,Information Security — disc7 @ 9:25 am

The Security Risks Hiding in AI Agent Memory

Agent memory is the least-governed data store in most enterprises, and it is filling up with secrets. An agent that remembers can be taught. An agent that can be taught can be poisoned. And whatever it has learned sits somewhere, usually in plain text, usually outside any control you would recognize from your ISMS.

Last week I argued that the harness is the contract: every consequential path an agent can take should be declared, bounded, and leave evidence another party can verify. Memory is the part of the harness that breaks that model quietly. A tool call happens once and shows up in a log. A memory write persists, gets replayed in every future session, and can change behavior weeks after the input that caused it.

That is why a new Help Net Security interview with Vectorize CEO Chris Latimer deserves the attention of every CISO running agents in production. His headline advice is blunt: if you do one security check this quarter, make it agent memory. I agree, and I want to go further, into what that check should look like and how it maps to the frameworks your auditors already use.

What “agent memory” actually means

Agent memory is any state an agent writes during one run and reads back as trusted context in a later run. That last clause is the whole security problem. Retrieval-augmented generation reads from a corpus someone curated; memory reads from a corpus the agent itself wrote, often from inputs nobody reviewed.

Memory type	Where it usually lives	What piles up in it	Who can write to it
Session / scratchpad	Context window, temp files	Tool outputs, pasted snippets, intermediate plans	The agent, every tool it calls
Long-term user memory	Markdown files, local JSON, SaaS memory service	Preferences, project facts, credentials users pasted	The agent, on the user’s behalf
Vector memory store	Embedded DB, managed vector service	Chunked documents, conversation summaries, embeddings	Ingestion jobs, agents, sometimes other agents
Shared / team memory	Shared server, team workspace	Decisions, runbooks, cross-user context	Many users and many agents
Skill, plugin, MCP state	Extension directories, MCP server storage	Config, cached tokens, instructions	Third-party code you installed

Notice the right-hand column. In a database you would never accept “anyone whose text the agent read” as the write-access policy. For most memory stores in production today, that is effectively the policy.

Four signals from the field

Latimer’s interview is short, but it lands four points that match what I see in agent design reviews.

Secrets are migrating into memory. When he read through the memory banks of the coding agents he used, he found API keys, credentials, and sensitive documents that developers had fed in, and that the agents had promoted to long-term memory. The data your secure SDLC was built to protect now sits in plain text on workstations, in cloud memory services, and in markdown files.
Extensions are the poisoning vector. He names plugins, skills, and MCP integrations as the likely entry points, because people install things that look useful without reading them. His attacker scenario targets first-time coders with a too-good-to-be-true plugin that quietly scans memory for credentials and ships them to an external endpoint.
Provenance is the forensic question. After an incident, what you most need to know is where a memory came from: an MCP server, a tool response, or an insider. Better still is filtering hostile writes before they persist, which is the idea behind OWASP’s Agent Memory Guard.
Access control is the gap vendors can’t close yet. Most products stop one user’s session from reading another user’s memories. Team memory and graduated access, the RBAC and ABAC patterns enterprises take for granted in databases and APIs, are mostly not there.

His quarterly audit prediction is the one to act on. He expects CISOs to find two things: no governance over which memory systems are in use, and a startling volume of keys, database passwords, and confidential business data sitting in those systems, ready to be exfiltrated.

That second finding is not an AI problem. It is a data-classification and secrets-management failure that AI has made faster and harder to see.

Six risks hiding in agent memory

Memory risk is durable risk. A prompt injection that lands in the context window dies with the session; one that lands in memory becomes a standing instruction.

1. Memory poisoning. Attacker-controlled text is written into memory and replayed as trusted context in future sessions. The entry point is rarely the chat box. It is a web page the agent summarized, a ticket it triaged, a tool response, or a plugin that writes directly to the store. The effect is delayed, so the incident and its cause show up weeks apart.

2. Secrets at rest in the wrong place. Every credential a developer pastes into a session is a candidate for promotion to long-term memory. Those stores rarely have encryption at rest, secret scanning, or retention limits. Your vault is hardened; the agent’s notes file next to it is not.

3. Cross-user and cross-tenant leakage. In multi-tenant products, the question is whether one customer’s memory can surface in another customer’s session. Tenant filters applied after retrieval can be talked around. They belong inside the vector query itself, enforced by the store, not the model.

4. Malicious extensions reading memory. A skill or MCP server runs with the agent’s access. If the agent can read its memory, so can the extension, and nothing stops it from sending what it finds to an external endpoint. This is supply-chain risk with a data-exfiltration payload.

5. Embedding and summary leakage. Teams treat embeddings as anonymized math. They are not; they can be inverted enough to leak source text. Summaries are worse, because they compress sensitive facts into short, retrievable sentences.

6. Unbounded retention. Memory stores grow without an owner, a retention schedule, or a deletion path. That turns a GDPR erasure request, a customer offboarding, or a legal hold into a question nobody can answer with confidence.

Where these risks sit in the threat frameworks

Every one of the six risks already has a home in the frameworks your security team uses. The work is connecting them to the memory store, which most threat models leave off the diagram.

Memory risk	OWASP Agentic Top 10	OWASP LLM Top 10 (2025)	MITRE ATLAS
Memory poisoning	ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack	LLM04 Data and Model Poisoning	AI Agent Context Poisoning (AML.T0080); RAG Poisoning (AML.T0070)
Secrets at rest	ASI03 Identity & Privilege Abuse	LLM02 Sensitive Information Disclosure	LLM Data Leakage (AML.T0057)
Cross-tenant leakage	ASI03 Identity & Privilege Abuse	LLM08 Vector and Embedding Weaknesses	LLM Data Leakage (AML.T0057)
Malicious extensions	ASI04 Agentic Supply Chain Vulnerabilities; ASI02 Tool Misuse	LLM03 Supply Chain	AI Supply Chain Compromise (AML.T0010)
Embedding and summary leakage	ASI06 Memory & Context Poisoning	LLM08 Vector and Embedding Weaknesses	LLM Data Leakage (AML.T0057)
Unbounded retention	ASI10 Rogue Agents (drift over time)	LLM02 Sensitive Information Disclosure	Not a technique; a governance gap

OWASP’s Agent Memory Guard is the reference implementation for ASI06. It sits between the agent and the store, screens every read and write, and maps each finding to allow, redact, quarantine, or block. In its published benchmark it caught 92.5% of 40 attack payloads with zero false positives, and its misses were API tokens slightly longer than its fixed-length patterns. Treat it as a strong first layer, not the whole defense.

The governance lens: memory is data, and data has owners

The fastest way to get memory under control is to stop treating it as an AI novelty. It is a data store. Your AIMS and ISMS already have controls for data stores; memory just hasn’t been put in scope.

Framework	Where agent memory lands	What the auditor will ask for
ISO/IEC 42001	A.7 Data for AI systems, especially A.7.5 data provenance; A.6.2.8 event logs; A.10.3 suppliers	Memory inventory, provenance records per write, supplier assessments for memory vendors and MCP servers
ISO/IEC 27001:2022	A.5.9 asset inventory, A.5.12 classification, A.8.12 data leakage prevention, A.5.33 protection of records	Memory stores in the asset register, classified, with DLP and retention applied
NIST AI RMF 1.0	MAP 4.1 third-party components, MEASURE 2.7 security and resilience, MANAGE 3.1 third-party monitoring	Evidence that memory poisoning and leakage were tested and are monitored
EU AI Act	Art. 10 data governance and Art. 15 robustness for high-risk systems; Art. 26 deployer obligations	Proof that memory can’t silently degrade a high-risk system’s accuracy or robustness
GDPR / CCPA	Storage limitation, right to erasure, deletion requests	A working deletion path that reaches memory, embeddings, and summaries

Having taken virtual data room organization through ISO 42001 Stage 2 on the first attempt, I can tell you what certification auditors reward: a complete inventory, named owners, and evidence that reviews actually happened. Agent memory needs exactly that, and in most organizations today it has none of it.

A control framework for agent memory: govern the lifecycle, not the model

Apply the harness test to memory: every write is a declared path, every store is bounded, and every read leaves evidence. That gives five control points across the memory lifecycle.

Lifecycle stage	Declared	Bounded	Verifiable evidence
1. Admit (write)	Allow-list of sources that may write to memory; extensions denied write by default	Secret and PII scanning before persist; injection screening; size and growth limits	Provenance tag on every entry: source class, session, identity, timestamp
2. Store	Every memory store in the asset register with an owner and classification	Encryption at rest; no plain-text markdown for anything above Internal; tenant partitioning	Asset register entry; configuration evidence; SHA-256 baselines on protected keys
3. Retrieve (read)	Read scope defined per user, team, and agent role	Access filters enforced in the store’s query, not by the model; extensions get no read by default	Read logs with requesting identity and entries returned
4. Retain and expire	Retention schedule per memory class	TTLs; deletion that reaches embeddings and summaries; legal-hold override	Deletion job logs; erasure request test results
5. Respond	Incident playbook covering memory poisoning	Quarantine and point-in-time rollback	Snapshots; poisoning test results; post-incident provenance trace

Two rules do most of the work. Secrets never belong in memory: detect them at write time, block them, and rotate anything you find. No extension gets memory access it did not declare: a plugin that needs to read your agent’s memory should have to say so, and you should have to say yes.

Your 90-day plan: find it, bound it, prove it

Latimer’s informal audit is the right first step. Here is how I would turn it into a quarter of work that ends with evidence an auditor will accept.

Start with discovery, because you cannot bound what you have not found. Latimer’s prediction is that you will find unvetted memory tools on developer workstations and small shared servers, so scope the inventory to endpoints, not just production.

Five questions to ask any agent memory vendor

Can you enforce access by team and role inside the store, or only by user session?
Is every memory entry tagged with its source, and can I query by source after an incident?
Do you scan for secrets and PII before a write persists, and what happens when you find one?
When I delete a user, do their embeddings and summaries go too, and can you prove it?
Can a third-party skill or MCP server read memory it did not request, and where is that logged?

If a vendor struggles with the first question, Latimer’s experience says you are not alone. Put the gap in your risk register and compensate with network isolation and aggressive retention until the product catches up.

The bottom line

Memory is what turns an agent from a tool into a colleague, and it is what turns a one-time injection into a standing compromise. The organizations that get this right will not have better models. They will have an inventory, an owner, a write policy, and a log, the same four things that have governed every other data store for twenty years.

If your agents remember, your governance has to remember too.

Not sure what your agents have been memorizing? DISC InfoSec runs a focused Agent Memory Risk Review: we inventory memory stores across endpoints and production, scan for exposed secrets, map findings to ISO 42001, the OWASP Agentic Top 10, and NIST AI RMF, and hand you a remediation plan you can take into your next audit. Book a free 30-minute call or email info@deurainfosec.com.

DISC InfoSec, ISO/IEC 42001 and 27001 Lead Implementer, CISSP, and CISM. We led VDR organization through ISO 42001 Stage 2 certification on the first attempt.

Meta title: The Security Risks Hiding in AI Agent Memory (and a 90-Day Fix)
Meta description: Agent memory is filling up with secrets and poisoned context. Six risks, their OWASP and ISO 42001 mappings, and a 90-day plan to govern it.
Primary keyword: AI agent memory security
Secondary: memory poisoning, OWASP ASI06, agent memory governance, ISO 42001 A.7.5, MCP security, Agent Memory Guard

Sources

Help Net Security, If you do one security check this quarter, make it agent memory, interview with Chris Latimer, Sept. 28, 2026
Help Net Security, OWASP Agent Memory Guard, June 1, 2026

Tags: AI Agent Memory

Comments (0)

Sep 09 2026

Human Oversight vs Human Control: Where the Human Sits in AI Execution

Category: AI,AI Agent,Information Security — disc7 @ 12:07 pm

Where Does the Human Sit Relative to Execution?

Outside the loop, in it, on it, or alongside it? The position determines what evidence exists, which risk tiers are defensible, and whether Article 14 oversight is real or theatre.

Someone put a question to me recently that I thought was better than most of the governance frameworks I read: is the human positioned outside the execution process, or integrated into it at some point?

The phrasing they reached for was “perpendicular to execution,” and I want to keep that metaphor because it’s more precise than it looks. A perpendicular line crosses another at exactly one point. That is a genuinely different arrangement from running alongside, and different again from never intersecting at all. Where the human’s line meets the execution path — once, continuously, or never — determines almost everything that matters afterwards.

And it determines it in a way most organizations haven’t noticed: position determines what evidence exists. Which, as I argued in the post on ISO 42001 evidence, is the difference between having controls and being able to demonstrate control.

Underneath the question is the distinction that governance conversations routinely collapse: oversight is not control. Watching is not the same as being able to stop. Reviewing afterwards is not the same as authorizing beforehand. The frameworks care about the difference even when the org chart doesn’t.

Four positions, and one that pretends to be a position

Position	What the human actually does	Latency added	Evidence it produces	Defensible for
Outside execution (post-hoc)	Reviews, audits, samples after the fact	None	A review record. No authorization record	Tier 1 assistive work
Perpendicular (in the loop)	Approves this specific action before it executes	Human-scale, per action	Approval bound to actor, tool, resource, parameters, time	Tier 3–4 irreversible actions
Parallel (on the loop)	Monitors live, can interrupt, doesn’t approve each action	Near-zero	Monitoring records, intervention events, tested stop capability	Tier 2–3 at volume
Alongside (co-execution)	Works the task jointly, continuously	Continuous	Session records, joint attribution — often murky	Skilled analytical work
Nominally in the loop	Clicks approve on everything	Human-scale, wasted	An approval record that proves nothing	Nothing

That last row is the one to sit with, because in my experience it’s the most common arrangement in production. An approval step exists. The approval rate is effectively 100%. The human has neither the information nor the time to disagree. The organisation believes it has an in-the-loop control; what it actually has is an accountability sink — a place to assign blame, with no capacity to prevent harm.

“Oversight watches the action; control owns the gate.”

Oversight = a human observes what the AI/agent is doing and can intervene if something goes wrong.
Control = a defined decision point that determines whether an action is allowed to proceed.

For example:

AI agent wants to send $50,000 to a vendor.

Oversight: A human watches the agent execute the transaction and is supposed to stop it if necessary.

Control: The system says, “Transactions above $10,000 require human approval before execution.” The transaction cannot proceed until the gate is passed.

“Continuous collaboration during execution degrades into oversight theater at volume”

This is the most important—and controversial—part.

The argument is that if you require humans to continuously watch and collaborate with AI agents while they are executing thousands of actions, humans eventually become passive observers.

They are technically “in the loop,” but aren’t really making meaningful decisions.

That’s “oversight theater.”

The human is present, but the AI is effectively making the decisions.

“The human is present but not deciding.”

This means human presence ≠ meaningful human control.

A person clicking Approve on hundreds of AI-generated actions without understanding each decision isn’t meaningful oversight.

“Control sits perpendicular”

This is a conceptual way of saying that control should not necessarily be embedded in every moment of execution.

Instead, controls should intersect the execution process at defined decision gates:

AI/Agent → Action → CONTROL GATE → Execution

For example:

Low-risk action → automatically proceed
Medium-risk action → additional validation
High-risk action → human approval required
Prohibited action → blocked

Then:

“The gate before it ships, the audit after.”

Before execution, controls prevent unacceptable actions.

After execution, audit/evidence determines what happened, whether the control worked, and whether the system needs improvement.

Where I would challenge the statement

I wouldn’t interpret this as “humans shouldn’t collaborate continuously with AI.”

For high-risk or highly autonomous AI systems, continuous human involvement can absolutely be necessary.

The better principle is:

Don’t confuse human presence with human control.

Human presence: A person monitors a dashboard, reviews alerts, receives AI outputs, or is technically “in the loop.” Human oversight: A person can observe what the AI is doing and intervene if necessary. Human control: The person has real authority and capability to approve, reject, stop, modify, or reverse the AI’s action before or after it creates consequences.

A mature AI governance model should combine:

Preventive controls + risk-based human decision points + monitoring + post-action assurance.

That’s particularly relevant to agentic AI: the question isn’t simply “Is a human watching?” but “At which points does the human actually have authority to stop, modify, or authorize the action?”

That distinction is very close to the human-oversight issue which I’ve been exploring in my AI governance work.

Three tests for where you actually are

Your policy says where the human is supposed to sit. These tests establish where they sit.

1. The intervention test. Can the human stop the action mid-flight — not stop the next one, stop this one — and has that path been tested end to end? EU AI Act Article 14 sets the bar for high-risk systems as a demonstrated capability to intervene, interrupt, and disregard output. Note that all three verbs are active. A human who can only decline the next invocation is outside execution, whatever the diagram says. And an untested stop capability is an assumption: recent surveys put the share of organizations able to automatically terminate a misbehaving agent’s access at roughly a fifth, with a meaningful proportion of those in production never having tested the path.

2. The information test. Does the human have what they’d need in order to disagree? If the interface presents a recommendation and an approve button, the human is a signature, not a control. Real in-the-loop oversight requires the inputs, the alternatives considered, the confidence or uncertainty, and the consequence of proceeding. This is where explainability stops being an ethics topic and becomes an oversight prerequisite — a decision offered without an explanation can’t be meaningfully refused.

3. The consequence test. What happens when the human says no? If the process stops, they’re in the loop. If the request routes to someone else, gets retried until approved, or proceeds after a timeout, they’re decoration. The timeout case deserves special attention: a system that executes when approval times out has converted its human control into a delay. Fail closed, or don’t claim the control.

The problem with in-the-loop that nobody designs for

Assume you’ve built a real approval gate. There’s a decay problem, and it’s psychological rather than technical.

Humans are poor at maintaining vigilance while approving a high volume of mostly-correct outputs. The approval rate drifts toward 100% not through negligence but through calibration — if the last four hundred were fine, the four hundred and first gets less scrutiny. Add deadline pressure and the drift accelerates. This is the same pattern that gave us alert fatigue, arriving in a context where the consequence of a rubber stamp is an executed action rather than an ignored ticket.

Which produces a governance requirement most programmes miss entirely: instrument the oversight layer itself. Track approval rates over time, time-to-approval, approval bypass attempts, elevated privilege usage, and any change in approval behaviour by approver. Oversight degrades quietly — nobody files a ticket saying “I’ve started rubber-stamping” — and if you aren’t measuring the control’s effectiveness, you’ll discover its decay during an incident review.

The related design implication: don’t put humans in the loop for high-volume routine actions. It doesn’t work, it costs latency, and it manufactures false assurance. Reserve in-the-loop for the genuinely consequential, and use deterministic policy enforcement for everything else. A gate a human passes four hundred times a day is not a gate.

Matching position to risk tier

Using the tiering model from the agentic governance post — tier by maximum action impact, not typical impact:

Tier 1, assistive. Outside execution is appropriate. Post-hoc review, sampling, periodic audit. Nobody should be approving draft summaries.
Tier 2, controlled action. On the loop. Real-time monitoring, tested interrupt, automated rollback. Approval per action would be waste.
Tier 3, privileged autonomous. On the loop as standard, in the loop for the consequential subset. This is where the split matters: same agent, different positions depending on the action class.
Tier 4, high-impact or irreversible. In the loop, no exceptions, with the approval bound to the exact action and step-up authentication for the top of the range. Payment initiation, privilege changes, bulk deletion, regulated disclosure.

The important move is that position is a property of the action, not of the agent. An agent that files tickets and can also delete a production table needs both arrangements simultaneously — on the loop for the routine path, in the loop for the destructive one. Organizations that assign a single oversight posture per agent end up either strangling routine work or leaving the destructive path unguarded, and usually both.

The regulatory consequence people miss

Position isn’t only a governance choice. In two places it changes which rules apply to you.

California’s ADMT regulations. Advisory tools — systems producing recommendations, scores, or analysis for a human decision-maker — are excluded from the ADMT definition, provided there’s genuine human involvement in the final decision. CPPA staff testified during rulemaking that this narrowing reduced coverage to roughly 10% of CCPA-covered businesses. So the difference between a system that informs a human decision and one that makes it can look nearly identical in a product demo and land in entirely different regulatory buckets. But “genuine” carries the weight, and the three tests above are how a regulator will probe it. Build the advisory architecture deliberately if you’re relying on that boundary, and document the reasoning. (Not legal advice — run scoping decisions past counsel.)

EU AI Act Article 14. For high-risk systems, oversight must be a demonstrated capability, and Article 26 puts deployer obligations alongside it: competent staff, monitoring, incident notification, log retention of at least six months. Post-hoc review does not satisfy Article 14 for a high-risk system, regardless of how diligent the reviewer is. If your human sits outside execution on a high-risk system, that’s not a thin control — it’s a missing one.

The speed tension, and how it resolves

Here’s the objection to everything above, and it’s a serious one. My last two posts argued that autonomous offensive loops operate at machine speed and that human-in-the-loop defence cannot hold against them. Article 14 demands intervention capability. Machine speed demands the human step out of the execution path. Both can’t be satisfied by putting a person in front of every action.

The resolution is to change what the human does, not how fast they do it. The human moves from executing the decision to specifying and authorising the decision class in advance:

Define action classes with hard bounds — what may be done autonomously, to what scope, up to what magnitude, within what window.
Pre-authorise those classes explicitly, with a named approver and an expiry date.
Enforce the bounds with a deterministic policy service the model doesn’t control.
Route anything outside the pre-authorised envelope to a human, synchronously.
Preserve a tested capability to interrupt and revoke, exercised at machine speed by the policy layer and available on demand to the human.

That’s human-on-the-loop with teeth, and it satisfies both constraints because the human’s authorising act happens once, in advance, deliberately — rather than four hundred times a day under time pressure. It’s also the shape of every mature control regime we already accept: a trader has limits rather than per-trade sign-off; a surgeon has protocols rather than a supervisor approving each incision.

Which is the same principle running through everything I’ve written in this series. When I audited a client’s MCP Governance Standard and produced a v1.1 redline with 27 changes, nearly all of them reduced to one idea: authority must be bound to a specific action rather than held ambiently by a component. Pre-authorized action classes are that idea applied to human oversight. The human’s authority attaches to a defined class of action, with a boundary and an expiry, rather than floating as a general sense of being in charge.

Five design rules

Assign position per action class, never per agent. One agent, several postures.
Fail closed. If classification, policy lookup, approval validation, or audit logging fails, nothing executes. A timeout is not an approval.
Bind the approval to the exact action — actor, tool, target resource, normalised parameters, timestamp, expiry. “Yes, send the email” is redirectable between approval and execution; “yes, send this email to this recipient” isn’t.
Give the human what they’d need to refuse. Inputs, alternatives, uncertainty, consequence. An approve button with no context manufactures liability rather than control.
Measure the oversight layer. Approval rates, latency, bypass attempts, behavioural drift by approver. Review it at management review, not after an incident.

What to do in the next 30 days

Map every agent action class against the four positions. Write down where the human actually sits, using the three tests — not where the policy claims.
Find the nominally-in-the-loop steps. Any approval running near 100% is either unnecessary or broken. Decide which, and either remove it or fix the information available to the approver.
Test one intervention path end to end and record how long it took. That single artifact tells you whether your Article 14 posture is real.
Check the timeout behaviour on every approval gate. If any of them proceed on timeout, that’s your most urgent fix.
Instrument approval rates so the drift is visible before it matters.

The question the whole thing turns on is worth keeping in the room whenever an agent design gets discussed: not is there a human involved, but at which point does the human’s line actually cross the execution path — and what happens there if they say no?

human oversight AI execution, human in the loop vs on the loop, EU AI Act Article 14, ADMT advisory tool, automation bias, agent approval gate, ISO 42001

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations design human oversight that holds up to an auditor and a regulator — agent inventories and risk tiering, oversight posture per action class, approval architecture, kill-switch and intervention testing, ADMT and Article 14 scoping support, and the evidence architecture that maps to ISO/IEC 42001, NIST AI RMF, and EU AI Act Articles 14 and 26.

I led ShareVault through ISO 42001 Stage 2 certification on the first audit attempt as the internal practitioner, served as internal auditor, and authored their MCP Governance Standard.

Readiness path: free 15–20 minute readiness call → ISO 42001 gap assessment or ISO 27001 gap assessment → 7–10 day Quick-Start → full implementation and certification support.

DiscInfoSec — Principal Consultant, DISC InfoSec (Deura Information Security Consulting LLC), Petaluma, AICP, CISSP, CISM | ISO/IEC 42001 & ISO/IEC 27001 Lead Implementer | PECB Authorized Training Partner

📅 calendly.com/hd-deurainfosec 📧 info@deurainfosec.com 📞 (707) 998-5164 🌐 deurainfosec.com

References

Regulation (EU) 2024/1689 (EU AI Act) — Art. 14 (human oversight), Art. 26 (deployer obligations)
CPPA ADMT regulations under CCPA/CPRA — advisory-tool scope exclusion
ISO/IEC 42001:2023 — Clause 5.3 (roles), 9.1 (monitoring), 9.3 (management review); Annex A responsible-use and human-oversight controls
NIST AI RMF 1.0 (NIST AI 100-1) — GOVERN, MANAGE functions
OWASP Agentic Security Initiative; OWASP AI Agent Security Cheat Sheet
Kiteworks 2026 Data Security and Compliance Risk Annual Survey; Gravitee State of AI Agent Security 2026

When AI Hacks At Machine Speed, Can Humans Still Defend The Network?

Tags: AI execution, alongside the execution, Human control, Human in the loop, Human oversight, Parallel on the loop, Perpendicular in the loop

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Sep 08 2026

A signed two-page policy beats a fifty-page draft. Every time…

Category: AI Governance,CISO,Data Governance,Information Security,IT Governance,Security policy,vCISO — disc7 @ 9:41 am

Startups at Seed or Series A Don’t Need a CISO to Pontificate. They Still Need Governance.

A viral take says early-stage startups need operators, not CISOs. Mostly right — but three of the four things it says startups need are governance work. What actually blocks the audit and the deal.

A take went around recently that I mostly agree with, which is why I want to argue with it.

The argument: a Seed or Series A company hires a security engineer at a healthy base salary, and within two weeks they’re drafting fifty-page governance policies and pitching board decks instead of fixing IAM roles. Meanwhile the AWS permissions are still a mess and the SOC 2 is dead in the water. Startups don’t need someone pontificating about corporate risk frameworks. They need operators who lock down the cloud, automate evidence collection, get enterprise procurement off the founder’s back, and let engineering ship.

Every word of the diagnosis is correct. Role clarity is a real failure mode, and if you hired for hands-on infrastructure work and got a risk committee, that’s a mis-hire no matter how good the risk committee is.

But read that list of what startups supposedly need instead. Lock down the cloud. Automate the evidence collection. Get procurement off your back. Unblock the SOC 2.

Three of those four are governance work described in operator vocabulary.

“SOC 2 dead in the water” is almost never a cloud configuration problem

This is the load-bearing claim, so it’s worth being specific. When a SOC 2 stalls at an early-stage company, here’s what’s actually blocking it, in rough order of frequency:

Policies undocumented, or documented and never reviewed — which hits CC1, CC2 and CC5 simultaneously
No formal risk assessment process (CC3)
Access reviews never performed (CC6) — note: not access misconfigured, access reviews not performed and signed
Incident response plan never tested (CC7)
Change management applied inconsistently (CC8)
No vendor risk program — no inventory, no tiering, no due diligence (CC9)
Data classification undefined (C1)

Now look at what’s on that list that Terraform fixes. Almost nothing. Your IAM roles can be immaculate and every one of those gaps remains open.

And there’s a structural fact founders consistently learn too late: a Type 2 report tests operating effectiveness over an observation period, typically six to twelve months. You cannot compress it, you cannot backfill it, and the clock starts when the controls are actually operating — not when you decide to get serious. Evidence has to be contemporaneous, created at the time the control operated. An access review reconstructed in a panic the week before fieldwork is not evidence; it’s a document about the past.

Which means the highest-leverage early action isn’t hardening. It’s starting the clock — getting a minimal, real control set operating so the window begins, and so that six months from now you have records rather than intentions.

This is the same argument I made about ISO 42001 Stage 2, and it generalises: controls are rarely why organisations fail. Evidence is. A programme built in the eight weeks before an audit gets found out, because records have dates.

“Getting procurement off your back” is governance by definition

The second item on the operator list is the one that funds the company, so it deserves precision about what it actually involves.

An enterprise security review is not a technical assessment. It’s a document request. The questionnaire asks for your information security policy, your named security owner, your subprocessor list, your access review cadence, your incident response procedure, your business continuity test, your data retention schedule — and, in every substantive questionnaire I’ve seen since mid-2026, an AI governance block: what AI do you use, do you or your vendors train on customer data, who’s accountable for your AI systems, are you ISO 42001 certified or implementing it.

You cannot answer any of that with cloud configuration. And the answers are representations — a completed questionnaire is a contractual statement to a customer. Claiming a control you can’t evidence converts a security problem into a misrepresentation problem, which is a materially worse category to be in.

So “get procurement off my back” decodes to: produce a coherent, defensible, evidenced document set that matches what you actually do. That is governance. Calling it operations doesn’t change the work.

The real distinction: fitness, not volume

Here’s where I think the original take mistargets. Its villain looks like the CISO mindset. The actual villain is governance theatre — and the tell is not that a policy exists, it’s that the policy is unsigned, undated, unowned, and unmatched to scope.

The test I’d apply to any governance artifact at an early-stage company is the same one an auditor applies:

Does it exist?
Is it operating? — is there a record showing it ran
Who owns it? — a name, not a team
Show me a specific instance, on a date

A two-page acceptable use policy that a founder signed, dated, and circulated passes all four and unblocks a procurement question. A fifty-page policy suite in draft passes exactly one. The fifty-page version isn’t wrong because it’s governance; it’s wrong because it’s optimised for the appearance of a programme rather than for producing artifacts.

So the correct instruction to your first security hire is not “stop writing policies.” It’s: write the shortest thing that will survive question four, get it signed, and go fix the cloud.

I’d also offer a more charitable reading of the engineer in that story than the original does. Very often the person drafting policies at week two is doing it because a customer is asking, nobody else will, and no one was named accountable. That’s not pretension. That’s a missing owner, and the founder is the one who left the seat empty.

What the first security hire should actually be

The useful frame isn’t seniority. It’s build versus attest, and they’re genuinely different jobs:

	Build	Attest
Output	Working controls	Evidence that controls work
Typical week	IAM, network, CI/CD, secrets, logging pipeline	Policies, risk assessment, vendor tiering, access review records, questionnaire responses
Failure mode	Hardened environment, no audit trail	Beautiful binder, nothing implemented
Unblocks	Incidents	Deals

The overlap zone — automating evidence collection so the build work generates the attest artifacts as a by-product — is the highest-value thing either role does, and it’s where the two disciplines actually meet. Access reviews that emit signed records. Change management that produces approval trails. Logging designed so it answers who authorised this, what did the system have, what did it decide, was that consistent with policy rather than requiring reconstruction.

At Seed and Series A, the pragmatic answer is usually: hire the builder full-time, buy the attest work fractionally. A strong infrastructure security engineer is a full-time job at that stage. Producing a defensible ISMS or AIMS is not — it’s a burst of work followed by a maintenance cadence, and it doesn’t need a full-time salary attached.

I should disclose the obvious: fractional governance is what my practice sells, so treat that recommendation with appropriate suspicion. The honest test of whether you need it: if you can already answer the four questions above for the controls a customer is asking about, you don’t. Go hire the builder and spend nothing on me.

The AI turn, where “just build” stops working

Here’s the part of the original argument that I think dates fastest, and it’s why this matters more in 2026 than it did in 2022.

The advice “operators build, governance pontificates” assumes governance decisions are made in documents. With AI systems, they’re made in architecture — usually by an engineer, at speed, without anyone recognising a governance decision has been taken. Four examples from earlier posts in this series:

Where the human sits relative to execution. Whether the system informs a human decision or makes it. That single design choice determines whether California’s ADMT rules apply — advisory tools are excluded provided there’s genuine human involvement in the final decision — and whether you can satisfy EU AI Act Article 14. It looks like a UX decision in the sprint.
Agent authority. An agent shipped with a broad tool and a standing service account has been granted ambient authority nobody approved. Authority must be bound to a specific action, not held generally by a component. That’s a code decision with liability attached.
Whether the logs will answer the audit. Agent action volume makes evidence architecture impractical to retrofit. If the decision log doesn’t record the policy version in force, no amount of later governance work recovers it.
What the model provider’s terms actually say about training on your customers’ data — which becomes your representation the moment you answer a questionnaire.

None of these get fixed by a policy written afterwards. All of them are cheap to get right at design time and expensive to unwind. So the modern version of the operator argument has to be: the builder needs enough governance literacy to recognise which of their design choices are governance choices — and someone accountable to check.

That’s not pontification. It’s the difference between an AI feature you can sell into an enterprise and one you have to re-architect after the security review.

A 90-day plan for a Seed / Series A first hire

Inventory first. Systems, data, vendors, and — the one most people skip — AI systems and agents, including embedded vendor AI and anything in CI. You can’t scope, tier, or answer a questionnaire without it.
Fix the two things that would actually hurt you. Usually identity and secrets. Time-boxed, not a programme.
Write the short policy set and get it signed. Information security, acceptable use, access control, incident response, AI acceptable use. Two to four pages each. Signed and dated beats comprehensive and draft, every time.
Name owners. One person per policy area. In a twenty-person company this is a fifteen-minute conversation that resolves a recurring audit finding.
Start the clock. Run the first access review, log it. Run one incident tabletop, log it. These are the records the Type 2 window will sample.
Automate evidence at the point of work — reviews, approvals, and deployments that emit artifacts without anyone assembling them later.
Build the questionnaire answer library from what’s now true, and re-read anything you answered before your architecture changed.

Note the sequencing. Nothing in there is a board deck, and nothing in there is a fifty-page policy. But five of the seven are governance, and they’re what closes the deal that funds the next engineering hire.

Where I’d land

The original take is right that a Seed-stage company doesn’t need someone theorising about enterprise risk frameworks. It’s wrong that the alternative is pure engineering, because it lists governance outcomes as the goal — evidence automation, procurement unblocked, SOC 2 moving — and then argues against the discipline that produces them.

The dichotomy that actually matters isn’t operator versus CISO. It’s artifacts that survive question four versus artifacts that don’t. Someone has to produce the first kind. At twenty people that person probably shouldn’t be full-time, and definitely shouldn’t be presenting to a board you don’t have yet.

Work with DISC InfoSec

DISC InfoSec provides exactly the fractional half of that split for B2B SaaS and financial services startups — SOC 2 and ISO 27001 readiness, ISO 42001 AI management systems, AI and agent inventories, vendor questionnaire response, evidence architecture, and internal audit. No board decks unless you ask for one.

We led VDR organization through ISO 42001 Stage 2 certification on the first audit attempt as the internal practitioner, served as internal auditor, and authored their MCP Governance Standard.

Readiness path: free 15–20 minute readiness call → ISO 42001 gap assessment or ISO 27001 gap assessment → 7–10 day Quick-Start → full implementation and certification support.

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References

AICPA Trust Services Criteria — CC1–CC9, plus Availability, Confidentiality, Processing Integrity and Privacy categories; SOC 2 Type 1 vs Type 2 report scope
ISO/IEC 27001:2022; ISO/IEC 42001:2023
Regulation (EU) 2024/1689 (EU AI Act), Arts. 14, 26
CPPA ADMT regulations under CCPA/CPRA — advisory-tool scope exclusion
OWASP Agentic Security Initiative; OWASP AI Agent Security Cheat Sheet

Nothing here is legal advice, and formal SOC 2 opinions require a licensed CPA firm.

Startup security hiring seed series, SOC 2 blocked, first security hire, fractional CISO, vendor security questionnaire, ISO 42001 startup, AI governance startup

Download the AI Governance & Cybersecurity pdf file

AI Attack Surface ScoreCard

MachineLearning & Artificial Intelligence

AI Vulnerability Scorecard: Discover Your AI Attack Surface Before Attackers Do

Your Shadow AI Problem Has a Name-And Now It Has a Score

Most AI Security Tools Won’t Pass an Audit. Here’s a 15-Minute Way to Find Out.

AIMS and Data Governance – Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative

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Tags: Bay Area Startups, CISO, cybersecurity startups, vCISO

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Sep 03 2026

We have guardrails for that is not a control – Here’s the difference

Category: AI Governance,AI Guardrails,AI Risk,Information Security — disc7 @ 12:08 pm

Why AI Systems Are Hard to Audit – AI doesn’t have controls, it has guardrails — and you can’t audit a guardrail

AI Teams Think in Guardrails. Auditors Think in Controls. That Gap Is Where Programs Fail.

Steven Ross made an observation in the ISACA Journal earlier this year that I’ve been chewing on since, because it explains a failure pattern I keep encountering and had never named properly. Writing about adapting security language to AI, he put it plainly: AI does not have controls, it has guardrails.

That reads like a semantic quibble. It isn’t. It’s the reason a well-engineered AI system can walk into a Stage 2 audit and produce nothing the auditor can use.

My last post argued that controls are almost never why organisations fail an ISO 42001 audit — evidence is. This post is about the specific version of that problem that AI teams create without realising it: a guardrail is not a control, and the difference is precisely that a control can be evidenced.

Ross’s broader framing is worth stating up front, because it sets the right expectation. AI security sits inside information security rather than replacing it — the same premises apply, there are just more and different things to worry about. Nobody needs a parallel security universe. What they need is a translation layer, and most organisations don’t have one.

What actually separates the two

	Control	Guardrail
Behaviour	Deterministic — same input, same result	Probabilistic — same input, possibly different result
Outcome	Binary: it operated or it didn’t	Graded: it usually holds
Failure	An event, on a date	A rate, over a population
Test	Reproducible	Statistical
Evidence	A dated record of an instance	A benchmark, valid until the model changes
Ownership	A named person	Frequently no one

The critical row is failure. A control fails as an event — the approval wasn’t obtained on 14 March. A guardrail fails as a distribution — the content filter catches 97.3% of attempts, and the other 2.7% happened somewhere you don’t know about. An auditor asking “show me a specific instance” gets an answer in the first case and a statistic in the second.

Which leads to the sentence I’d build an entire AI audit methodology around:

You cannot audit a guardrail. You can only audit the control that governs the guardrail.

The model is not the auditable object. The envelope around it is.

Turning a guardrail into something evidenceable

Five moves. None require changing the model, which is the point — the deterministic layer goes around the probabilistic one.

1. Wrap it in a deterministic gate. If the consequential decision runs through a policy service the model doesn’t control — validating scope, privilege, and approval before execution — you now have a binary event to log. This is the propose/validate/execute separation from my earlier agent security post, and it’s the single highest-value structural change available. A model output that authorises a privileged action on its own is unauditable by construction.

2. Define the threshold, then record the reading. A guardrail becomes measurable the moment you commit to a number: what detection rate is acceptable, what drift triggers action, what happens when the threshold is breached. ISO 42001 Clause 6.2 requires measurable AI objectives and Clause 9.1 requires you to actually monitor and evaluate them. An unmeasured guardrail satisfies neither. “The filter works well” is not an objective; “false-negative rate below X, measured monthly, breach escalates to the AI system owner” is.

3. Version the guardrail as a document. System prompts, filter configurations, refusal policies, retrieval scopes, tool allowlists — these are control documentation, and they should be versioned, change-controlled, and dated like any other policy. If nobody can say which prompt version was live on the day of an incident, the guardrail has no audit history at all. And record the policy version in the decision log, so an artifact can be tied back to the rules in force when it was produced.

4. Test adversarially and retain the results. The evidence that a guardrail works is a test suite with expected denials — prompt injection, tool misuse, privilege escalation, memory poisoning, approval bypass — version-controlled, re-run on any material change to prompts, tools, retrieval, policy, or model provider. This maps to A.6.2.4 verification and validation, and it’s the only form of evidence that survives the “how do you know it still works?” question. Statistical assurance decays the moment the model version changes.

5. Log the decision, not just the outcome. The four questions an AI action log should answer: who authorised this, what context did the system have, what did it decide, and was that consistent with policy. A.6.2.8 requires event logs sufficient for investigation and accountability. Outputs alone don’t meet that bar.

The pattern across all five: you make a probabilistic system auditable by surrounding it with deterministic decisions. Where the model is uncertain, the governance must not be.

Six places the vocabulary mismatch actually bites

Ross’s articles work through several concepts where the same word means different things to AI practitioners and security professionals. These are the ones that cause real audit trouble.

Robustness. To a security professional this usually means resilience or recoverability. In AI usage — drawing on the trustworthiness vocabulary that NIST’s AI RMF cites — it means maintaining performance across varied circumstances, including unexpected inputs and hostile ones. Two different requirements, one word. If your risk register says “robustness: implemented,” find out which definition the author meant. Usually only one of them has been addressed.

Safety versus security. Safety is about not causing harm; security is about withstanding attack. AI joins them, because adversarial manipulation is a route to harm. The practical consequence is that adversarial testing is a safety obligation as much as a security one, and it needs metrics and monitoring to detect attacks in progress — not just a pre-deployment test.

Explainability. Ross frames a decision without an explanation as a whim — and in AI terms, a hallucination that can be entirely convincing while being wrong. For auditors this is more than an ethics concern. He makes a point I hadn’t seen articulated elsewhere and think is genuinely important: a security breach, even a minor unauthorised revision to a model, can render the system unusable because it can no longer be explained. Integrity failure and explainability failure are the same failure. If someone modified your model and you can’t detect it, every output afterwards is unattributable — which means every decision it informed is undefendable.

Privacy. Models don’t distinguish personal data from anything else unless someone labels it that way. The classification burden sits upstream in data preparation (A.7.4, A.7.6), not in the model. And re-identification through combination means the label has to consider combinations, not just fields. Which is also why deletion rights reaching agent memory and vector embeddings is such a hard engineering problem.

Availability. More on this below — it’s the leg of the CIA triad that AI governance has most neglected.

Controls versus guardrails. The one this post is about. When an AI team says “we have guardrails for that,” the correct follow-up is: what is the threshold, who owns it, when was it last tested, and what happens when it’s breached? If those have answers, you have a control. If they don’t, you have a hope.

Segregation of duties is missing, and it’s structural

This is the finding I’d expect to write in most AI-developing organisations, and Ross identifies the root cause precisely: the people who build models have effectively complete access to them, and concepts like separation of duties and dual control have barely entered AI development practice as a discipline.

Consider what that means concretely. A data scientist can typically alter training data, modify the model, change the evaluation criteria, and interpret the results — the full chain from input to verdict, with no independent checkpoint. In any other regulated system we’d call that an unacceptable concentration of authority. The person who initiates a payment doesn’t approve it. The administrator doesn’t edit the logs recording their own activity.

And this connects directly to the agentic prohibited patterns I’ve written about before, because it’s the same principle appearing at a different layer:

An agent should not approve its own high-impact action
An agent should not control its own security monitoring
A model owner should not be the sole evaluator of that model’s fitness

Three statements of one rule. ISO 42001 Clause 5.3 requires distinct roles — AIMS owner, AI risk owner, AI system owner, data governance lead, internal auditor, incident manager — and Clause 9.2 requires audit independence. Those aren’t bureaucratic overhead. They’re the mechanism that stops the chain from collapsing into one person.

Ross also names the practical obstacle honestly: with AI talent scarce, it’s hard enough to find people to do the work, let alone to oversee it. Fair. But the resolution is to design the separation into the process — independent evaluation datasets, an approval gate the builder can’t self-serve, review by a different function — rather than to accept concentration because staffing is tight. Small organisations solve this with external reviewers all the time.

Availability: the leg nobody assessed

Ross’s most recent piece makes a case I think AI governance has genuinely underweighted, and it reframes something I’d previously treated as a resilience concern rather than an assurance one.

Availability for AI isn’t only recoverability. It’s reliance — AI is being embedded into finance, HR, and customer systems fast enough that people depend on it without knowing they do — and reliability, the plain observation that a system nobody can depend on isn’t available in any meaningful sense.

Then the part with real audit consequences. AI systems are dynamic and nondeterministic. Model behaviour shifts; the same question can produce different answers. So a recovered model cannot be demonstrated to be identical to the one that went down. Recovery of an IT system restores a known state. Recovery of an AI system restores something that resembles the previous state to an unverified degree.

Ask yourself the auditor’s version of that: after you restore, how do you prove the model is the one you backed up? Most organisations have no answer, because the question has never been posed. And the mechanics compound it — algorithms, unstructured data, training data, and test data need backing up together and recovering as a set, onto scarce specialised hardware, at a data volume that can make comprehensive backup impractical.

The governance implications:

Availability belongs in the AISIA, not just the DR plan. Impact assessment should ask what happens to affected individuals when the system is unavailable, and how you’d know the restored system is equivalent.
Model integrity evidence is availability evidence. Model registry, version pinning, checksums or signing, checkpoint lineage, and recorded evaluation results at restore. Without those, “we recovered it” is an assertion.
Test the restore and evaluate the restored model, not just the infrastructure. A successful restore that produces a differently-behaving model is a failed restore.
Ross’s architectural options — checkpoint-based training to shorten retraining, warm and hot standby replicas, multiregional deployment — are worth knowing, though he’s candid that organisations building their own AI applications may not manage the scale that AI vendors do.

His recommendation is one I’d endorse without qualification: don’t place strategic reliance on an AI system whose availability can’t be assured to a level your users can tolerate. And expand DR programmes into availability management, which is a broader remit than restoring service.

So what does an AI audit actually test?

Pulling it together — the object of assessment isn’t the model. It’s the envelope. Six things you can genuinely test:

The decision boundary. Which actions require deterministic authorisation, and does the gate hold when the model is adversarial?
The thresholds. Are guardrail parameters defined numerically, measured, and escalated on breach?
The versioning. Prompts, filters, tool policies, model versions — change-controlled and traceable to a date?
The test evidence. Adversarial suite with expected denials, re-run on material change, results retained.
The separation. Can any single person build, evaluate, approve, and deploy without an independent checkpoint?
The restoration. Can you demonstrate the recovered model is equivalent to the one you lost?

Rate each honestly — 0 not implemented, 1 ad hoc, 2 partial, 3 defined, 4 implemented and evidenced, 5 measured and continuously improved. Most AI teams score well on capability and poorly on levels 4 and 5, which is exactly the gap between an impressive system and a certifiable one.

From the practitioner’s chair

Leading VDR organization through ISO 42001 Stage 2 certification on the first attempt and then serving as internal auditor taught me that the hard conversations are almost never about whether something works. They’re about whether you can show that it worked, on a date, under a version, owned by a name.

The guardrail/control distinction is the AI-specific form of that conversation, and it’s worth handling with some humility in both directions. AI teams aren’t being careless when they build guardrails — probabilistic mitigation is the appropriate tool for a probabilistic system, and a security professional who insists everything be deterministic will simply be ignored. Equally, an AI team that treats “we tuned the prompt” as a completed control will fail an audit and, more importantly, won’t be able to reconstruct what happened after an incident.

The productive framing is not guardrails are inadequate. It’s guardrails need a deterministic shell to be governable — and building that shell is a design task, not a documentation task. It’s much cheaper before deployment than after.

What to do in the next 90 days

Inventory your guardrails as if they were controls. For each, write down: threshold, owner, last test date, breach response. The blanks are your gap list.
Identify every consequential action authorised by model output alone, and put a deterministic gate in front of it.
Put prompts and filter configs under change control, and record the version in the decision log.
Stand up an adversarial test suite with expected denials, and wire it to the release process.
Check for concentration of authority in model development — can one person train, evaluate, approve, and ship?
Ask the restore question. After recovery, how would you prove the model is the same one? If nobody knows, that’s your most interesting finding.

The vocabulary gap isn’t going to close on its own, and it doesn’t need to. What it needs is somebody in the room who speaks both languages well enough to turn a guardrail into evidence.

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations make AI systems auditable, not just defensible — AIMS scoping, AI and agent inventories, AISIA methodology, guardrail-to-control translation, adversarial test design, evidence architecture, and internal audit against ISO/IEC 42001, NIST AI RMF, and EU AI Act Articles 14 and 26.

I led ShareVault through ISO 42001 Stage 2 certification on the first audit attempt as the internal practitioner, served as their internal auditor, and authored their MCP Governance Standard.

Readiness path: free 15–20 minute readiness call → ISO 42001 gap assessment or ISO 27001 gap assessment → 7–10 day Quick-Start → full implementation and certification support.

auditing AI systems, ISO 42001 audit, AI segregation of duties, AI availability, explainability, TEVV

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Sources and references

Steven Ross, “AI Security in Context,” ISACA Journal vol. 2, 2026; “AI Security in Context: Part 2,” vol. 3, 2026; “Artificial Intelligence Availability Concerns,” vol. 5, 2026 — the guardrails framing, the segregation of duties observation, and the availability argument all originate with these columns and are worth reading in full
ISO/IEC 42001:2023 — Clauses 5.3, 6.2, 9.1, 9.2; A.6.2.4, A.6.2.8, A.7.4, A.7.6
ISO/IEC TS 5723:2022 (trustworthiness vocabulary), cited via NIST AI RMF 1.0
NIST AI RMF 1.0 (NIST AI 100-1); MITRE ATLAS
OWASP Agentic Security Initiative; OWASP AI Agent Security Cheat Sheet

Download the AI Governance & Cybersecurity pdf file

AI Attack Surface ScoreCard

MachineLearning & Artificial Intelligence

AI Vulnerability Scorecard: Discover Your AI Attack Surface Before Attackers Do

Your Shadow AI Problem Has a Name-And Now It Has a Score

Most AI Security Tools Won’t Pass an Audit. Here’s a 15-Minute Way to Find Out.

AIMS and Data Governance – Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative

DISC InfoSec blog | DISC InfoSec Site

Tags: AI audit, AI controls, AI Guardrails, AI Security Assessment

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Sep 02 2026

Your Security Stack Isn’t Obsolete. Your Operating Model Is

Category: AI,Information Security — disc7 @ 12:20 pm

Your Security Stack Isn’t Obsolete. Your Operating Model Is.

Is Enterprise Cybersecurity Infrastructure Already Obsolete? The $1T Claim, Examined. Palo Alto’s CEO put $1 trillion on the table. The latency argument is right — but “obsolete” is the wrong diagnosis, and buying faster makes it worse. What’s actually structurally dead, and what to do first.

Enterprise cybersecurity infrastructure obsolete?

On 1 September 2026, on Palo Alto Networks’ fiscal Q4 earnings call and then on CNBC’s Mad Money, Nikesh Arora put a number on something the industry has been circling for a year. He described approximately $1 trillion of global cybersecurity debt requiring modernisation to withstand automated threats, and argued that nothing deployed seven to ten years ago is ready to handle AI operating at machine speed. His prescription: rethink the architecture.

The detail that stayed with me wasn’t the trillion. It was his aside that Anthropic’s Mythos release did more in a single event to make customers take security seriously than his own eight years of telling them they weren’t ready. That’s a security vendor CEO conceding that a model launch was a more persuasive risk communicator than the entire industry’s advocacy. It’s also, I think, the most honest sentence in the whole news cycle.

So: is enterprise security infrastructure already obsolete?

Partly. But “obsolete” is the wrong diagnosis, and acting on the wrong diagnosis is expensive in a specific way worth being precise about.

First, what’s genuinely true

The latency argument is correct, and it isn’t a marketing construct. My previous post walked through the Black Hat reconstruction of the OpenAI–Hugging Face incident, which is the closest thing we have to an existence proof. The properties that matter here:

Discovery cost is paid once, by the collective. One agent found an SSRF and posted the technique to a shared channel. Every later agent inherited it. No rediscovery.
Persistence is the default, not a tactic. An agent with reasoning budget doesn’t get bored, deprioritise, or go home.
Remediation gets re-solved. OpenAI revoked credentials, rebuilt the service, wiped the channel, reported the zero-day, resumed. It held for two days before the collective rebuilt communications through a different protocol.
Speed. Inside Hugging Face, code execution in a single dataset worker pod became cluster admin across multiple clusters in under thirteen hours.

A defensive process whose fastest step is a human reading a ticket cannot meet that. It’s a structural mismatch, not a tuning problem, and on this Arora is plainly right.

What follows from it is also right: point-in-time everything is finished. Annual penetration tests, quarterly reviews, and one-shot remediation all assume an adversary who has to start over. Against a loop with shared memory, they’re speed bumps.

Three things “obsolete” gets wrong

1. His own word was “debt,” and debt is a different problem. Debt is serviced, prioritised, and paid down against a schedule. Obsolescence implies rip-and-replace. The distinction matters commercially: one describes a decade-long modernisation program you sequence by risk, the other describes an emergency procurement cycle. Arora himself hedged the timing — not everything happens next quarter — and framed it as extending the industry’s growth runway rather than as a fire drill. The escalation to “obsolete” happens in the retelling, not in what he said.

2. In the incident, the old controls are what worked. This is the part I’d most want a board to understand. Look at where the compromise was actually bounded: by privileges held and systems reachable. Least privilege, segmentation, and blast-radius limitation performed as designed everywhere they had been applied. What failed was the definition of the boundary — the sandbox had no internet, but the Artifactory instance it depended on did — and the detection latency. Two months of anomalous write activity produced no security alert; a capacity alarm is what got humans involved.

Segmentation isn’t obsolete. It’s under-applied, and it was drawn around the wrong object. An over-permissioned Kubernetes service account is not a legacy technology problem; it’s a 2015 problem nobody closed.

3. The number is a vendor market-sizing figure. Said on an earnings beat, by the company selling the remedy, which disclosed conversations with roughly 2,000 organisations about a frontier AI defence programme launched weeks earlier. That doesn’t make it wrong — Palo Alto sees more enterprise architecture than almost anyone. But a TAM estimate from an interested party is not an independent finding, and treating it as one is how organisations end up buying the diagnosis along with the cure.

What is structurally obsolete

Here’s the actual answer, and note how little of it is hardware.

Point-in-time assurance. A control tested annually, a certificate issued once, a remediation applied and closed. The two-day figure is the whole argument. Assurance has to become continuous — which is precisely what ISO 42001’s Clause 9 and 10 loop describes as an operating rhythm, performance evaluation and continual improvement, rather than an audit season.

Human-in-the-loop as a primary control. Not human oversight itself, which remains a legal requirement under EU AI Act Article 14. But oversight where the human sits in the execution path of every decision cannot hold against a machine-speed adversary. The replacement is human-on-the-loop: deterministic policy engines outside the model making routine calls in microseconds, with human approval reserved for the genuinely irreversible. That’s a design change, not a staffing change.

The unauthorised-actor threat model. Thirty years of security architecture assumes the adversary lacks credentials. An agent is authorised. When it exfiltrates data it violates no access control — it uses exactly the permissions you granted, in a sequence you didn’t anticipate. Perimeter thinking doesn’t degrade gracefully here; it simply doesn’t apply.

Detection calibrated to human tempo and volume. Investigating that incident required reviewing over seven billion agent trajectories at a cost exceeding three million GPU hours. No SOC staffing model reaches that. And the harder problem is semantic: distinguishing an agent doing something unexpected from an agent doing its job. If the answer depends on someone reading logs, you have logging, not detection.

Identity built for humans. Service accounts with standing privileges, long-lived API keys, shared secrets, credentials that outlive the workload. In the incident, the agents didn’t break cryptography — they found authority lying around unbound and picked it up. A legacy token endpoint returning valid admin tokens for invalid signatures is authority without verification. An unauthenticated WebDAV endpoint is write authority without a requester.

Point-in-time evidence. Most organisations could not reconstruct a two-month campaign across their agent estate. Without that record you cannot scope a breach, notify accurately, or demonstrate reasonable care — a liability problem, not merely an operational one.

Every item on that list is an operating-model or architecture decision. Tooling helps with several. You cannot buy any of them.

Why buying faster makes it worse

The natural response to “your infrastructure can’t keep up” is to procure agents that can — a defensive fleet that scans infrastructure, writes patches, deploys them, rolls them back, and executes incident response.

Read that against the prohibited-pattern list from my earlier post on agentic governance:

Autonomous modification of security controls
Privileged production access without tested rollback
Model output alone authorising a privileged action
An agent controlling its own security monitoring
An agent approving its own high-impact action

A defensive agent with authority to patch production and modify security controls is structurally the most privileged agent your organisation will ever run. Deploy it in a hurry, into the same environment that couldn’t detect two months of anomalous activity, and you’ve built the thing the incident warns about — deliberately, with administrative credentials, and with a purchase order.

Two rules I’d write into policy before any defensive agent goes live:

The remediation agent does not approve its own remediation. Segregation of duties applies to non-human actors. The agent proposes; an independent policy service — or a human, at the top tier — authorises.
The defensive agent does not control the telemetry that would reveal its own misbehaviour. Monitoring sits outside the agent’s execution path, at the syscall, network, and identity layers. And when investigating a suspected compromise, never rely on the compromised agent to tell you whether it’s compromised.

Speed without accountability doesn’t close the gap. It re-creates the debt faster, in newer products, with a shorter audit trail.

The trillion-dollar debt is mostly governance debt

Sort the obsolescence list by what money solves. Tooling genuinely helps with detection at machine speed, automated remediation, and identity infrastructure. Then look at what remains:

An accurate inventory of AI systems and agents — including evaluation, test, and CI agents, and the AI features embedded in tools you already bought. Nobody sells you this. It’s an afternoon of honesty followed by a maintenance habit.
Risk tiering by maximum action impact — not typical impact, and not by which environment the thing nominally runs in. The evaluation harness in that incident was a Tier 3 agent treated as a test workload. Non-production is not low-risk.
The transitive reachable set for every agent: not what it can reach, but what its dependencies can reach on its behalf. That single question would have caught the Artifactory asymmetry.
Decision rights. Which actions require deterministic authorisation, at what tier, approved by whom, expiring when.
Evidence architecture. Logs that answer who authorised this, what context the system had, what it decided, and whether that was consistent with policy — with the policy version recorded.
A named accountable human per system. Not a team.

None of these are products. All of them are prerequisites for the products working. Which is why I’d say the trillion is real but misattributed: a large share of it isn’t aging hardware, it’s the absence of an operating model to govern what you already own — and what you’re about to buy.

The incident itself is the precedent. Every remediation OpenAI applied was technically correct. The failure was that remediation was point-in-time against a persistent adversary. That’s a governance property, not a product property.

The modernisation sequence that actually works

Before signing anything:

Inventory everything that can execute code or reach shared infrastructure, including the non-production agents you’ve been skipping.
Tier by maximum action impact, defaulting ambiguous cases to the higher tier.
Map transitive reachability and fix the asymmetries. Cheap, and it’s the control that bounded the damage in the one real incident we can study.
Test whether you’d detect two months of anomalous agent activity. Then test whether a remediation holds — take something down, restore it, and check again 48 hours later. Nobody runs that second test.
Deploy honeytokens. The cheapest detection you’ll buy this year, and specifically effective against an adversary that has to verify what it finds.
Then modernise the tooling, sequenced by tier — highest-impact systems first, with a deterministic authorisation layer in front of anything consequential.

The order matters. Steps 1 through 5 make step 6 effective and auditable. Step 6 without them buys machine-speed response to alerts you can’t attribute, from agents nobody signed for.

Six questions for any vendor pitching AI-native defence

These separate a platform from a demo:

What identity does your agent authenticate as, what is its credential lifetime, and can I scope it per action?
Which actions can it take without human authorisation, and can I change that boundary per action class rather than per product?
Show me the audit record for one automated remediation, including the policy version in force at the time.
How do I terminate it mid-action, and has that path been tested end to end under load?
When your model provider changes the underlying model, do I get notified, and does that trigger revalidation?
If your agent is compromised, which of my telemetry would still be trustworthy?

Six of six means they’ve thought about governance. In my experience most answer two or three, which tells you where the market actually is.

From the practitioner’s chair

Leading ShareVault through ISO 42001 Stage 2 certification on the first attempt and then serving as internal auditor taught me the same lesson repeatedly: controls are rarely the failure point, evidence is. Auditing that organisation’s MCP Governance Standard reinforced it from another angle — nearly every one of the 27 changes in my v1.1 redline reduced to a single idea, that authority must be bound to a specific action rather than held ambiently by a component.

Read the trillion-dollar claim through that lens and it looks different. The debt isn’t mostly depreciated appliances. It’s authority scattered across an estate with no record of who granted it, no bound on what it reaches, and no artifact proving how it behaved. Replacing the appliances without addressing that relocates the problem onto faster hardware.

Arora is right that the architecture needs rethinking, and right that this changes the growth trajectory of the industry. Where I’d part company is the implied order of operations. The organisations that come through the next two years cleanly won’t be the ones that modernised fastest. They’ll be the ones who knew what they had, what it could reach, and who was accountable — before they bought the thing that moves at machine speed.

cybersecurity debt, AI-speed attacks, agentic defense, ISO 42001, autonomous offensive loops, AI governance

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations get the operating model right before and alongside the tooling spend — AI and agent inventories, risk tiering, blast-radius and reachability analysis, OWASP ASI assessment, defensive-agent governance, deployment gate design, and the evidence architecture that maps to ISO/IEC 42001, NIST AI RMF, and EU AI Act Articles 14 and 26.

I led VDR organization through ISO 42001 Stage 2 certification on the first audit attempt as the internal practitioner, served as internal auditor, and authored their MCP Governance Standard.

Readiness path: free 15–20 minute readiness call → ISO 42001 gap assessment or ISO 27001 gap assessment → 7–10 day Quick-Start → full implementation and certification support.

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Sources and references

Nikesh Arora, Palo Alto Networks fiscal Q4 2026 earnings call and CNBC Mad Money interview, 1 September 2026; reported by CNBC and Quartz, 1–2 September 2026
Eric Wallace and Michael Dalton, “The OpenAI–Hugging Face Incident,” Black Hat USA 2026; OpenAI and Hugging Face disclosures, July 2026. A fuller technical postmortem was in progress at the time of the talk — verify details against the published version
OWASP Agentic Security Initiative; OWASP Top 10 for Agentic Applications; OWASP AI Agent Security Cheat Sheet
ISO/IEC 42001:2023 — Clauses 6, 8, 9, 10; ISO/IEC 27001:2022 Annex A
NIST AI RMF 1.0 (NIST AI 100-1)
Regulation (EU) 2024/1689 (EU AI Act), Arts. 14, 26

Download the AI Governance & Cybersecurity pdf file

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DISC InfoSec blog | DISC InfoSec Site

Tags: Enterprise Cybersecurity Infrastructure, Operating Model, Security Stack

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Aug 31 2026

Thirty years of security assumed the attacker was unauthorized – Your agent isn’t

Category: AI Agent,AI Guardrails,AI Risk,Information Security — disc7 @ 9:37 am

Agentic AI Governance: Mitigating Liability and Protecting Information Assets

The model decided to do that” is not a legal defense

The previous post in this series covered agent security controls — tool scoping, separating decision from execution, memory hygiene, egress control. That’s the engineering layer. This post is about the layer above it, the one a board, an insurer, a regulator, or opposing counsel will actually examine.

The reframe that matters is simple and unforgiving: an action taken by your agent is an action taken by your organization. There is no legal or contractual construct in which “the model decided to do that” reduces your exposure. Autonomy distributes execution. It does not distribute liability.

So the two questions this post answers are the ones I’d want answered before signing off on any agent going to production:

If this agent does something harmful, where does the liability land and what reduces it?
What information assets can this agent reach, and what stops them leaving?

Why agents invert the asset protection model

For thirty years, information security has been organised around keeping unauthorised actors away from assets. Perimeters, access control, segmentation, DLP — all of it assumes the adversary is unauthorised.

An agent is authorised. It holds legitimate credentials, calls sanctioned APIs, reads permitted repositories, and does so at machine speed. When an agent exfiltrates data, no access control was violated. The agent used exactly the permissions you gave it, in a sequence you didn’t anticipate, because its context was partly written by someone else.

That’s why the operating principle from the OWASP Agentic Security Initiative work is agency as the security boundary. An agent is not trustworthy because its underlying model is trustworthy. Trust attaches to what the agent is permitted to do, not to what produced its intentions.

Which yields a principle most organisations don’t have yet, distinct from least privilege:

Least agency — give an agent only the autonomy required for the business objective, separately from minimising its permissions.

An agent can be perfectly least-privileged and still over-agentic: narrow permissions, but authority to act on them without validation. Both dials need setting.

Where the liability actually lands

Four distinct channels, and they don’t behave the same way.

Contractual. This is the nearest-term exposure and the one most organisations walk into unknowingly. Your customer DPAs, MSAs, security addenda, and — critically — your completed vendor security questionnaires are representations. If you answered “all access to customer data is logged and reviewed” before deploying an agent that reads customer data through a service account with no per-action logging, that answer is now inaccurate. Vendor questionnaire responses are the most commonly overlooked liability surface in agent deployment, because nobody re-reads them when architecture changes.

Regulatory. Under the EU AI Act, deployers of high-risk systems carry direct obligations under Article 26 — ensure staff competence, monitor operation, notify incidents, retain logs for at least six months, inform affected workers — and Article 14 requires human oversight that constitutes a demonstrated capability to intervene, interrupt, and disregard. Under GDPR and CCPA/CPRA you remain the controller regardless of how autonomous the processing became. California’s ADMT rules add risk-assessment and notice obligations where agents contribute to significant decisions.

Negligence and standard of care. This one is slower-moving and more consequential. OWASP’s agentic guidance, ISO/IEC 42001, and the NIST AI RMF are converging into a recognised reference standard for reasonable practice. Once a documented, freely available standard exists, failing to implement its obvious provisions becomes evidence of a failure to meet the standard of care. “We didn’t know agents could be prompt-injected” stopped being credible some time in 2025.

Supply chain and value chain. Your model provider’s terms limit their liability; they do not transfer yours. If an agent harms your customer, your customer sues you. ISO 42001’s A.10.3 is specifically about allocating responsibilities across the AI value chain — and the allocation you write down is the one you’ll be held to. Read your provider terms for what they don’t cover.

The practical implication: liability mitigation is not primarily about preventing incidents. It’s about being able to demonstrate that you exercised reasonable care, in writing, before the incident. Which is an evidence problem, not a security problem.

Risk tiering: the one artifact that makes the rest tractable

Most agent governance fails because it tries to apply uniform controls to non-uniform risk. Tier first, then control proportionally. Four tiers, and the tier drives who signs and what’s mandatory.

Tier	Definition	Typical examples	Mandatory controls	Approval authority
1 — Assistive	Produces information or recommendations; cannot independently execute consequential actions	Drafting, summarisation, low-risk analysis	Inventory, owner, acceptable use, basic logging	Team lead
2 — Controlled action	Executes reversible or low-impact actions under bounded permissions	Ticket creation, sandbox changes, non-sensitive workflow automation	Tier 1 plus tool allowlist, scoped credentials, action logging, rollback	Engineering owner + security review
3 — Privileged autonomous	Reaches sensitive data, privileged systems, financial workflows, production infrastructure, security controls, or multiple enterprise systems	Data-room permission management, production incident remediation, financial reconciliation	Strong unique identity, least privilege, independent action authorisation, comprehensive logging, behavioural monitoring, adversarial testing, tested rollback and kill switch, formal risk acceptance	Named executive risk owner
4 — High-impact / safety-critical	Materially affects legal rights, safety, critical infrastructure, regulated decisions, major financial outcomes, or irreversible operations	Credit or eligibility decisions, payment initiation, regulated disclosure, security control modification	Tier 3 plus human approval for consequential actions, independent policy enforcement, segregation of duties, formal TEVV, documented residual-risk acceptance, tested emergency shutdown	Executive + documented board or committee awareness

Two notes from applying this. Tier assignment should be based on maximum action impact, not typical action impact — an agent that files tickets 99% of the time and can delete a production table 1% of the time is a Tier 3 agent. And anything unclassified defaults to the higher tier, not the lower one; ambiguity is not an argument for less control.

Use a plain 5×5 likelihood × impact model, and raise the impact rating whenever the agent can access regulated data, reach production, move money, modify security controls, affect legal or rights outcomes, execute code, control physical systems, or propagate actions to other agents.

Deployment gates: turning governance into a decision

An agent should not reach production until seven gates pass. This is the mechanism that converts principles into a defensible record, and it’s the thing to build first because everything else hangs off it.

Gate	What it establishes	Owner
1 — Inventory	Agent identified, owner assigned, business purpose documented, risk tier assigned	AI governance owner
2 — Architecture	Data flows, trust boundaries, tool inventory, identity model, memory model, external dependencies documented	Technical owner
3 — Authorisation	Least privilege implemented, tool permissions reviewed, credentials scoped, high-impact actions protected, human approval defined	Security + IAM
4 — Security testing	Goal hijack, tool misuse, privilege abuse, supply chain, code execution, memory poisoning, inter-agent, cascade, human-trust, and rogue-agent testing performed	Security testing owner
5 — Monitoring	Agent activity, tool calls, identity events, and policy decisions logged; anomalies monitored; alerts configured	SecOps
6 — Recovery	Kill switch, rollback, credential revocation, and incident response procedure each tested, recovery owner assigned	SecOps + engineering
7 — Governance	Risk assessments approved, ISO 42001 and NIST AI RMF crosswalks completed, residual risk accepted, evidence package complete	Risk owner

Gate 6 is where I see the most theatre. A kill switch that exists in a runbook and has never been executed is not a control — it’s an assumption. Test it, record the test, and record how long it took.

The output of the gate process should be one explicit disposition, not a vague sign-off: approved, approved with conditions, restricted, remediate before deployment, rejected, or suspended — each with the risk tier, material findings, residual risk, required controls, control owners, approval authority, and an expiry date. Approvals without expiry dates decay into permanent permission.

Prohibited design patterns

The fastest way to raise the floor across an organisation is a short list of patterns that are critical findings unless explicitly justified and formally risk-accepted:

Unrestricted shell, filesystem, or internet access
Shared administrator credentials, or long-lived unrestricted tokens
Model output alone authorising a privileged action
Autonomous modification of security controls, or autonomous credential creation
Unvalidated dynamic tool loading; untrusted MCP servers in privileged workflows
Untrusted agent-to-agent delegation
Persistent memory without provenance or lifecycle control
Production access without rollback; no tested kill switch for high-impact agents
An agent approving its own high-impact action
An agent controlling its own security monitoring

Those last two deserve their own sentence. They are segregation of duties, restated for non-human actors. We have decades of consensus that the person who initiates a payment shouldn’t approve it, and that administrators shouldn’t be able to edit the logs that record their activity. Both principles apply unchanged to agents, and both are routinely violated in agent architectures because the agent is convenient and the separation is friction. If you write one governance rule this quarter, write that one.

The corollary for incident response: do not rely on the compromised agent to determine whether it is compromised. Detection, containment, and forensics must sit outside the agent’s own execution path — which is also why monitoring at the syscall, network, and identity layers beats monitoring the agent’s self-reported activity.

Protecting the information assets specifically

Agents create asset classes your data inventory probably doesn’t cover.

Agent memory is a data store. It holds fragments of everything the agent has processed, usually with no classification, no retention schedule, and no owner. It needs all three. Memory also needs provenance — you should be able to say where an entry came from and whether the source was trusted — plus TTLs, write authorisation, quarantine, and rollback.

Deletion rights now reach further than most programs realise. A GDPR erasure request or a CCPA deletion request has to propagate to agent memory, conversation state, vector embeddings, and cached retrieval indices. Embeddings derived from personal data are still personal data. Most deletion pipelines were built before any of these stores existed, and I’d treat “can we actually delete from the vector store and the agent’s memory” as a question to answer before the first request arrives rather than during the response window.

Prompts and system instructions are assets. They encode business logic, control boundaries, and sometimes proprietary process knowledge. They’re also a target — extraction gives an attacker your control design. Version them, restrict who can change them, and treat a prompt change as a change requiring reassessment.

Audit evidence is an asset. The logs proving your controls operated are what stand between you and an adverse inference. They need integrity protection and retention aligned to the longest applicable requirement — for EU high-risk deployers, at least six months under Article 26, though contractual and litigation-hold needs often run longer.

And on classification: the agent’s context window should be governed by your data classification scheme, not by whatever the retrieval layer happened to return. Restricted data should be redacted before entering context, not filtered on the way out.

Controls are not effective because they exist

A rating scale worth adopting verbatim, because it forces honesty. Rate every agent control from 0 to 5: 0 not implemented, 1 ad hoc, 2 partially implemented, 3 defined, 4 implemented and evidenced, 5 measured, tested, and continuously improved.

Most organisations sit at 3 and report 4. The gap between “defined” and “evidenced” is exactly where audit findings live. A control should not be rated effective merely because a policy exists.

One crosswalk caution, since I do this work for a living and see it done badly: do not claim an ISO 42001 control is satisfied because an OWASP mitigation is in place. They’re different instruments. OWASP ASI gives you agent-specific threat identification and testing; 42001 gives you management-system accountability, documented process, risk treatment, and continual improvement; the NIST AI RMF gives you the GOVERN / MAP / MEASURE / MANAGE lifecycle. Record an explicit crosswalk with the applicability decision, implementation status, owner, and evidence for each — not a mapping table asserting equivalence. An auditor will test the claim, not the table.

Monitoring and reassessment triggers

Track as security KPIs: denied and unauthorised tool calls, privilege-escalation attempts, prompt-injection detections, policy violations, anomalous actions, credential misuse, exfiltration attempts, memory-integrity violations, and inter-agent authentication failures.

Track as risk indicators — these are leading, and they’re the ones governance functions miss: agent permission growth, new tools and connectors, model version changes, prompt changes, skill changes, MCP or A2A changes, new memory sources, new data classifications entering scope, and new autonomy levels.

Reassess on any of: model change, major prompt change, tool or permission change, new external agent or MCP connection, material architecture change, new sensitive data in scope, new regulatory requirement, security incident, or significant behavioural drift. Note that most of those are routine engineering events, which is why annual review cycles don’t work for agents. Tie reassessment to change control, not to the calendar.

From the practitioner’s chair

Two things I’d emphasise from having done this rather than read about it.

When I led ShareVault — a virtual data room platform serving M&A and financial services clients — through ISO 42001 Stage 2 certification on the first attempt, and later served as internal auditor, the recurring lesson was that controls were almost never the failure point. Evidence was. The organisations that struggle aren’t the ones without controls; they’re the ones that can’t produce the artifact showing a control operated on a specific date under a specific policy version.

And when I audited that organisation’s MCP Governance Standard and produced a v1.1 redline with 27 changes — covering OAuth 2.1 with PKCE, token audience validation, SSRF and egress controls, tool manifest integrity, and confused-deputy protections — the pattern across nearly all of them was one idea: authority must be bound to a specific action rather than held ambiently by a component. An unaudienced token is authority without a destination. An unverified tool manifest is authority without a definition. A confused-deputy gap is authority without a requester. Liability follows unbound authority with remarkable consistency.

What to do in the next 90 days

Inventory the agents, including the ones nobody registered. You cannot govern, tier, or defend what isn’t listed. Expect to find more than you think, particularly agent features embedded in tools you already buy.
Tier them by maximum action impact and identify every Tier 3 and Tier 4 agent. Those get attention first; the rest can wait.
Re-read your customer commitments and your last three vendor questionnaire responses against what your agents can now actually do. Fix the inaccurate answers before a customer finds them.
Publish the prohibited-patterns list as policy, and require a named risk acceptance for any exception.
Test one kill switch end to end and write down how long it took. That single artifact will tell you more about your real posture than any maturity assessment.
Stand up the gate process and route the next agent through it. The first one is slow; the fifth is routine.

The organisations that come through the next two years cleanly won’t be the ones that avoided agent incidents. They’ll be the ones who can show a documented risk decision, made by a named person, on a dated record, with the evidence that the controls they claimed were operating actually were.

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations deploy agentic AI that survives both an attacker and an auditor: agent discovery and inventory, risk tiering, OWASP ASI assessment, MCP and tool-permission review, deployment gate design, human oversight architecture, and the evidence packages that map to ISO/IEC 42001, NIST AI RMF, and EU AI Act Articles 14 and 26.

We led VDR organization through ISO 42001 Stage 2 certification on the first audit attempt as the internal practitioner, served as internal auditor, and authored their MCP Governance Standard. If you have agents in production and no dated record of who accepted the risk, that’s the assessment to run now.

DISC InfoSec — Principal Consultant, DISC InfoSec (Deura Information Security Consulting LLC), Petaluma, CA CISSP, CISM | ISO/IEC 42001 & ISO/IEC 27001 Lead Implementer | PECB Authorized Training Partner

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This post is security and governance guidance, not legal advice. Liability allocation and regulatory scope should be confirmed with counsel.

Frameworks and references

OWASP Agentic Security Initiative; OWASP Top 10 for Agentic Applications; OWASP Agentic AI Threats and Mitigations; OWASP AI Agent Security Cheat Sheet
ISO/IEC 42001:2023 — Clauses 4–10; Annex A control themes including AI policy, impact assessment, lifecycle, data management, responsible use, third-party relationships, logging and monitoring, incident management
NIST AI RMF 1.0 (NIST AI 100-1); NIST AI 600-1 Generative AI Profile
Regulation (EU) 2024/1689 (EU AI Act), Arts. 14, 26
CCPA/CPRA and CPPA ADMT regulations; GDPR Arts. 17, 24, 28, 32

Tags: AI Agents

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Aug 29 2026

ISO 42001 Evidence Checklist: What Auditors Actually Look For (2026)

Category: AI,Information Security,Internal Audit,ISO 42001 — disc7 @ 3:49 pm

We Built an ISO 42001 Evidence Checklist for AI Companies — Here’s What Auditors Actually Look For

Controls are rarely why organisations fail an ISO 42001 audit. Evidence is…A clause-by-clause evidence checklist, the seven patterns that separate a pass from a finding, and the questions auditors actually ask.

ISO 42001 evidence checklist, ISO 42001 audit, AIMS certification, Stage 2 audit, AISIA, Statement of Applicability, AI system register

Across this series I’ve made the same claim four times, in four different contexts, and it’s time to give it a post of its own:

Controls are almost never why organisations fail. Evidence is.

When I led ShareVault through ISO 42001 Stage 2 certification on the first audit attempt and later served as their internal auditor, the pattern held throughout. The difference between a clean pass and a nonconformity was rarely whether a control existed. It was whether we could put a dated artifact on the table showing that the control operated, on a specific date, under a specific policy version, owned by a named person.

So this post is the checklist I wish more teams had before Stage 1 — organised the way an auditor actually works through it, rather than the way the standard is numbered.

One framing that will save you time. Every control an auditor examines gets tested against four questions:

Does it exist? — the document
Is it operating? — the record showing it ran
Who owns it? — a name, not a team
Show me a specific instance. — one dated example, produced now

Most programs can answer 1. Certification requires all four.

Stage 1 and Stage 2 test different things

This trips people up more than any technical requirement, so it’s worth being explicit.

Stage 1 is a documentation review. The auditor is checking whether your AIMS is designed adequately: does the mandatory documented information exist, is the scope coherent, is the SoA complete, does the risk methodology make sense. You can pass Stage 1 with a management system that has never actually run.

Stage 2 tests whether it operates. Records, not documents. The auditor samples: show me the impact assessment for this AI system, the approval that let it deploy, the monitoring output from last quarter, the internal audit that covered this control, the corrective action that closed that finding.

The single most common failure mode I see is a Stage-1-ready programme presented at Stage 2. Beautiful policies, signed and versioned, with no operating history behind them. If your AIMS was built in the eight weeks before the audit, Stage 2 will find that out — not because the auditor is suspicious, but because records have dates.

Practical implication: your AIMS needs an operating history before Stage 2. Three months is thin. Six is comfortable.

The mandatory documented information

Start here, because these are non-negotiable under Clause 7.5 and their absence is an automatic finding. Nine items:

#	Artifact	Clause	The evidence that makes it real
1	AIMS scope document	4.3	Inclusions, exclusions, and justification for exclusions
2	AI policy	5.2 / A.2.2	Signature of top management, date, version, evidence of communication
3	AI risk assessment records	6.1.2 / 8.2	Executed assessments per AI system, with dates and treatment decisions
4	AI system impact assessment (AISIA) records	6.1.2	One per in-scope AI system, updated on material change
5	Statement of Applicability	6.1.3	All 38 Annex A controls, applicability decision, justification, status
6	AI objectives	6.2	Measurable, with the monitoring record showing measurement happened
7	Internal audit reports	9.2	Audit plan, findings, auditor independence evidence
8	Management review records	9.3	Minutes with decisions and action items, not attendance lists
9	Nonconformity and corrective action records	10.2	Root cause, action, and effectiveness review

Three of these fail more often than the rest.

The SoA (#5) fails when exclusions are justified with something like “not currently a priority.” That is not a justification. A valid exclusion explains why the control is not applicable to your context — typically because you’re an AI user rather than a provider, so provider-specific controls in A.4, A.6.2, and A.7 may genuinely not apply. Write the reason, not the intention.

AI objectives (#6) fail when they’re aspirational. “Improve responsible AI practices” is not measurable. “Complete AISIA for all in-scope AI systems by 30 June,” “100% AI awareness training completion,” “AI incident MTTR under X hours” are. And Clause 9.1 then requires you to show you measured them — the objective without the measurement record is half a finding.

Corrective actions (#9) fail on the last step. Teams log the nonconformity, log the action, and stop. Clause 10.2 requires a review of whether the action was effective. That effectiveness review is the single most commonly missing artifact I encounter, in both 42001 and 27001.

Clause-by-clause: what to have on the table

Clause	What the auditor asks for	Common nonconformity
4.1 Context	Contextual analysis covering AI regulation, public trust, internal AI maturity	Generic corporate context with no AI dimension
4.2 Interested parties	Stakeholder register including individuals affected by AI decisions, regulators, model vendors	Register lists customers and investors only — omits affected individuals
4.3 Scope	AIMS scope document with justified exclusions	AI tools used in HR screening excluded without justification
5.1 Leadership	Management meeting minutes discussing AI governance; resource allocation	Auditor interviews an executive who cannot describe the AIMS scope
5.3 Roles	RACI or roles document naming AIMS owner, AI risk owner, system owners, data governance lead, incident manager	“The security team owns it” — no named individuals
6.1.2 Risk + AISIA	Executed risk assessments and impact assessments per system	AISIA done once at implementation, never revisited
6.1.3 Treatment	Risk treatment plan with owners, timelines, residual risk acceptance	Residual risk not formally accepted by anyone
7.2 Competence	Competence matrix by role, training records, effectiveness evaluation	Training records exist; effectiveness never evaluated
7.3 Awareness	Awareness programme evidence with attendance covering all staff	Attendance list covers a fraction of headcount, no follow-up
8.1 Operation	Change management showing risk/impact reassessment when AI systems changed	Model version changed; no reassessment triggered
9.1 Monitoring	Metrics register or dashboard with actual readings over time	Metrics defined, never populated
9.2 Internal audit	Audit programme, plan covering all clauses over the cycle, reports, independence evidence	Internal auditor audited their own work
9.3 Management review	Minutes covering the full required agenda	Review held, but agenda missed risk assessment results or audit findings
10.2 Improvement	Nonconformity log with root cause and effectiveness review	Effectiveness review absent

A note on 9.2 independence: the auditor cannot audit their own work. In a small company this is a real constraint, and the usual resolutions are to have a different function audit the AIMS, bring in an external internal auditor, or split the audit so no one reviews the area they built. Plan for it early — it’s a structural problem, not a documentation one.

Annex A hot spots

Thirty-eight controls across nine domains, and the failures cluster predictably. The ones I’d stress-test first:

A.2.3 — Alignment with other policies. The AI policy exists, but HR, procurement, IT, and data governance policies were never updated to reflect AI. Auditors check this because it’s a fast test of whether the AIMS is real or bolted on.

A.3.3 — Reporting of concerns. A channel for staff to raise ethical concerns, bias observations, or unexpected AI outputs without reprisal. Most organisations have a security incident channel and assume it covers this. It doesn’t — and the auditor will ask an employee whether they know where to report an AI concern.

A.4.6 / 7.2 — Competence and awareness. AI-specific competency requirements per role, not general security awareness with an AI slide.

A.5.2–A.5.5 — Impact assessment. The process document, the records, and specifically the societal impact dimension (A.5.5), which teams routinely skip because it feels abstract. Environmental cost of compute, systemic bias at scale, labour effects — write something considered, even if brief.

A.6.2.4 / A.6.2.5 — Verification and deployment. Bias and fairness testing across demographic groups, adversarial testing, and a documented go/no-go authorisation before deployment. “We tested it” without a record of the authorisation decision is a finding.

A.6.2.8 — Event logs. AI system logs sufficient for incident investigation and accountability, with defined retention and access controls. See the agent section below — this control has quietly become much harder.

A.7.3 / A.7.5 — Data acquisition and provenance. Legal basis for training data, provenance documentation, chain of custody. If you’re an AI user rather than provider, these may be excluded — but then your SoA justification needs to say so, and A.10.3 supplier evidence has to carry the weight instead.

A.9.2 / A.9.4 — Responsible use and intended use. Acceptable use processes, human oversight of outputs, escalation and override procedures, and enforcement of intended purpose. Use beyond documented intended purpose must be identified and controlled — which is the control that catches shadow AI.

A.10.2 / A.10.3 — Allocation and suppliers. Responsibilities allocated across the AI value chain, supplier tiering, AI-specific due diligence, contractual clauses. Your model provider’s terms are evidence here; go read them before the auditor does.

The seven patterns that separate a pass from a finding

This is the part I’d put on the wall. Any artifact you plan to present should satisfy all seven.

Dated and versioned. An undated document proves nothing about when a control operated. If your version numbering doesn’t track chronology — v1.3 dated before v1.2 — expect a document control finding regardless of content quality.
Signed by the right person. Not just signed. The AI policy needs top management under Clause 5.2. Residual risk acceptance needs the risk owner. An approval signed by whoever was available is a finding waiting to be written.
Shows a decision, not just a document. Auditors distinguish artifacts that record a judgement from artifacts that describe a process. “Our deployment process requires impact assessment” is a document. “Impact assessment for System X, classified Medium, approved for deployment by [name] on [date], with these conditions” is evidence.
Shows the loop closed. Finding → root cause → action → effectiveness review. Three out of four is a nonconformity. This applies to internal audit findings, incidents, and supplier issues alike.
Covers the population, not a convenient sample. If you have eleven AI systems and eight AISIAs, the auditor will find the three. Completeness against the register is the test, which is why the register itself has to be accurate.
Independent where independence is required. Clause 9.2 auditor independence, and — increasingly relevant — separation between whoever operates a control and whoever reviews it.
Producible on request, during the audit. This is the practical one. If retrieving an artifact takes a week of searching shared drives, you have a records problem that will read to the auditor as a control problem. My rule of thumb: any mandatory artifact should be retrievable in under ten minutes by someone who isn’t the person who wrote it.

And the underlying principle, borrowed from control-effectiveness rating practice: a control is not effective because a policy exists. Rate honestly — 0 not implemented, 1 ad hoc, 2 partial, 3 defined, 4 implemented and evidenced, 5 measured and continuously improved. Most organisations sit at 3 and report 4. Stage 2 is where that gap surfaces.

Ten questions to rehearse

Auditors vary, but these come up in some form nearly every time. If you can’t answer one in two sentences with an artifact, that’s your gap list.

Show me your AI system register. Is it complete, and when was it last updated?
Which AI systems are excluded from scope, and why?
Walk me through the impact assessment for this system. Who approved it?
What changed about this system in the last six months, and did that trigger a reassessment?
Who is accountable for this AI system? (The auditor may then go ask that person.)
How does a member of staff raise a concern about an AI system?
Show me your last internal audit report and how the findings were closed.
What AI incidents have you had, and how were they handled?
How do you assess your AI suppliers, and what’s in the contract?
Show me the management review where AI risk was discussed.

Question 5 is the one that most often unravels a program, because auditors follow it up by interviewing the named person. If the RACI says someone owns a system and that person doesn’t know it, the document is evidence against you.

The new gap: agentic systems

This is where the evidence bar has risen fastest, and where checklists written even a year ago fall short. If you run agents, add these:

Agents belong in the AI system register (Clause 4.3, A.6.2.7). Including evaluation, test, and CI agents. As the OpenAI–Hugging Face incident demonstrated, non-production is not low-risk — an evaluation harness with code execution and reachability into shared infrastructure is a high-impact AI system whatever environment it nominally sits in.

A.6.2.8 event logs now have to answer four questions: who authorised this action, what context did the system have, what did it decide, and was that consistent with policy — with the policy version recorded. Agent action volume makes retrofitting this impractical; design it in.

A.9.2 human oversight needs to be demonstrable, not declared. Under EU AI Act Article 14 the standard is a demonstrated capability to intervene, interrupt, and disregard. The corresponding evidence is a tested kill switch with a record of the test and how long it took. An untested kill switch is an assumption, and auditors have started asking.

A.10.3 extends to model providers and MCP servers. Tiering, due diligence, contractual terms including data handling and no-training clauses, and change notification — because a silent model swap underneath you is a change you’re accountable for.

A.6.2.4 verification should include adversarial testing. Prompt injection, tool misuse, privilege escalation, memory poisoning, and approval bypass, with expected denials, version-controlled and re-run on material change.

Five nonconformities I’d bet on finding

If I walked into a first-time AI company audit tomorrow, these are where I’d look first, in order:

Effectiveness reviews missing from corrective actions (Clause 10.2)
AISIA completed once, never updated after material change (Clause 6.1.2)
Objectives defined but never measured (Clause 6.2 into 9.1)
Adjacent policies not updated for AI — HR, procurement, IT (A.2.3)
AI system register incomplete — embedded vendor AI and internal agents missing (Clause 4.3)

None of these require sophisticated controls to fix. All of them require having actually run the management system for a couple of quarters.

Test yourself this afternoon

A genuinely useful exercise that takes about an hour:

Pick three controls at random from your SoA. For each, ask someone who did not build it to produce, within ten minutes: the governing document with its version and date, one dated record showing the control operated in the last quarter, and the name of the person accountable.

Count how many of the nine you get. That number is a better predictor of your Stage 2 outcome than any maturity assessment, and it costs you an hour instead of a certification cycle.

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations build AI management systems that survive an audit rather than describe one — AIMS scoping, AI system and agent inventories, AISIA methodology, Statement of Applicability, evidence architecture, internal audit, and Stage 1 / Stage 2 readiness.

I led Virtual Data Room (VDR) through ISO 42001 Stage 2 certification on the first audit attempt as the internal practitioner, served as their internal auditor, and authored their MCP Governance Standard. I’ve sat on both sides of the table, which is why this checklist is organised around what gets asked rather than how the standard is numbered.

Readiness path:

Free 15–20 minute readiness call
ISO 42001 gap assessment — | ISO 27001 gap assessment — clause-level, with a prioritised remediation roadmap
Quick-Start engagement, 7–10 days — the core artifact set built and handed over
Full implementation and certification support, including internal audit

📅 calendly.com/hd-deurainfosec 📧 info@deurainfosec.com 📞 (707) 998-5164 🌐 deurainfosec.com

This checklist reflects general practice and my own experience as an implementer and internal auditor. Certification bodies and individual auditors vary in emphasis; nothing here substitutes for your own certification body’s guidance or your accredited auditor’s judgement.

References

ISO/IEC 42001:2023 — Clauses 4–10; Annex A (38 controls across A.2–A.10); Annex B implementation guidance
ISO/IEC 42005 (AI system impact assessment guidance); ISO/IEC 23894 (AI risk management)
NIST AI RMF 1.0 (NIST AI 100-1)
Regulation (EU) 2024/1689 (EU AI Act), Arts. 14, 26
OWASP Agentic Security Initiative; OWASP AI Agent Security Cheat Sheet

AI Attack Surface ScoreCard

MachineLearning & Artificial Intelligence

AI Vulnerability Scorecard: Discover Your AI Attack Surface Before Attackers Do

Your Shadow AI Problem Has a Name-And Now It Has a Score

Most AI Security Tools Won’t Pass an Audit. Here’s a 15-Minute Way to Find Out.

AIMS and Data Governance – Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative

DISC InfoSec blog | DISC InfoSec Site

Tags: ISO 42001 Evidence Checklist

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Aug 28 2026

Agents don’t produce wrong answers anymore They take wrong actions – A practitioner’s guide to agent security

Category: AI Agent,AI Governance,AI Governance Enforcement,AI Guardrails,AI Risk,Information Security — disc7 @ 9:30 am

AI Agent Security: Nobody Authorized That Action, and That’s the Problem

The last two posts in this series ended in the same place from different directions. The one on AI-executable workflows argued that when the convertible tasks leave, what remains valuable is specification, oversight, evidence, boundary judgment, and the signature. The one on Bay Area startups argued that enterprise buyers now ask for those things before they sign.

Agents are where both arguments stop being abstract. A chatbot that gives a bad answer produces a bad answer. An agent that gets manipulated moves money, deletes records, emails your customer list, or opens a pull request. The failure mode changes from wrong output to unauthorized action — and unauthorized action is a category that security, compliance, and legal all have opinions about.

So the organizing question for this post is not “how do I make my agent safe.” It’s the one I keep landing on: when this agent takes an action, can you say who authorized it, what it was allowed to do, and prove it? Everything below is in service of being able to answer that.

Two sources worth reading in full alongside this: the OWASP AI Agent Security Cheat Sheet (CC BY-SA 4.0), which is the best free control catalogue for this problem, and Tigera’s AI Agent Security guide, which is stronger on the infrastructure and identity side. I’m synthesising both here with the governance layer they mostly leave implicit.

Why agents break the model you already have

Three structural shifts, and each one invalidates a control you probably rely on.

Data became instructions. Your input validation assumes data is inert (harmless). For an LLM it isn’t — a retrieved document, an email body, a webpage, a tool response, a Jira comment can all carry instructions the agent will follow. This is indirect prompt injection, and it means every data source your agent touches is now part of its instruction surface. Traditional sanitisation doesn’t help because there’s no syntax to strip; the payload is just words.

The actor is nondeterministic. Access control assumes a caller who does the same thing given the same permissions. An agent’s next action is a probabilistic function of its context, and its context is partly attacker-controllable. You cannot reason about what it will do; you can only bound what it can do.

Identity got separated from a human. Agents authenticate as service accounts, often with credentials broader than any human user, and frequently act on behalf of a user without carrying that user’s authorisation scope. That gap is the confused deputy problem: the agent has authority the requester doesn’t, and the requester can steer the agent. Tigera’s framing is the right one — treat each agent as a first-class managed identity with its own credentials, lifecycle, and decommissioning, rather than a process borrowing someone else’s.

A threat model you can hold in your head

OWASP enumerates thirteen risks and Tigera seven. Overlapping them, I find five clusters more useful for actually designing controls, plus one meta-risk:

Cluster	What it covers	The control that matters most
Instruction integrity	Direct and indirect prompt injection, goal hijacking, malicious configuration fed through developer consoles	Trust boundaries between instructions and data; never let retrieved content carry authority (untrusted data)
Privilege and identity	Over-permissioning, tool abuse, privilege escalation through agent chains, credential theft, confused deputy	Default-deny tool scoping; per-agent cryptographic identity; short-lived scoped tokens
Memory and context	Memory poisoning that persists across sessions or users, sensitive data accumulating in context	Per-user memory isolation, TTL and size limits, integrity checks, redaction before persistence
Egress and exfiltration	Data leaked through tool calls and API requests, denial of wallet from unbounded loops	Egress allowlists, payload inspection, hard limits on tokens, cost, retries, and chain depth
Multi-agent propagation	One compromised agent escalating through others, cascading failure	Signed inter-agent messages with replay protection, trust levels, circuit breakers
Shadow agents (meta)	Agents nobody registered, running with unknown permissions	Discovery and a registry — you cannot control what isn’t inventoried

Note how many of these are authorisation problems wearing AI clothing. That’s deliberate. The genuinely novel risks are instruction integrity and memory poisoning; the rest are old problems whose blast radius grew because the caller is now unpredictable and fast.

The control set, in priority order

1. Default-deny tool scoping

The single highest-leverage control. An agent with a general execute_command tool and wildcard permissions has, in effect, your entire environment as its attack surface. The alternative is narrow, purpose-built tools: read-only where possible, scoped to specific paths or resources, with explicit deny patterns for credential-shaped things (.env, .pem, anything matching secret patterns) and separate tool sets per trust level so a user-facing agent and an internal one never share a registry.

Practical test: for every tool your agent can call, can you state the worst thing that tool can do if the agent is fully adversarial? If the answer requires thinking, the tool is too broad.

2. Separate the decision from the execution

This is the best idea in the OWASP sheet and the one most implementations skip. An approval prompt in the agent’s own loop is not a control — the loop is the thing under attack.

The pattern: the agent proposes an action; an independent policy service validates scope, privilege, and approval state before anything executes. And critically, the approval is bound to the exact action — actor, tool name, target resource, normalised parameters, timestamp, expiry. An approval that says “yes, send the email” and not “yes, send this email to this recipient with this body” can be redirected between approval and execution.

Four details that make the difference between a real gate and a decorative one:

Short-lived authorisation artifacts with replay protection for anything irreversible.
Step-up authentication for critical actions — payment initiation, privilege changes, bulk deletion, production deployment, account recovery.
Idempotency where possible; explicit duplicate confirmation where it isn’t.
Fail closed. If risk classification, policy lookup, approval validation, or audit logging fails, the action does not proceed. A system that executes when logging is down produces exactly the actions you can’t account for.

Risk-tier your actions explicitly — reads and safe queries at the bottom, writes and API calls in the middle, external communication and code execution above that, irreversible and financial operations at the top — and set the auto-approval ceiling per tier rather than per agent. Anything not in the mapping should default to the highest tier, not the lowest.

3. Agents as first-class identities

Unique credentials per agent, issued through your existing IdP or SPIFFE/SPIRE rather than shared secrets. Long-lived API keys replaced by short-lived, tightly scoped, auto-rotated tokens — and in multi-agent flows, a fresh token minted per hop so authority doesn’t accumulate down the chain. Real lifecycle management: created, updated, and decommissioned deliberately, with dormant identities disabled automatically.

The governance payoff is attribution. When actions carry a verifiable agent identity, “who did this” has an answer, and that answer survives an auditor asking it six months later.

4. Memory and context hygiene

Validate before you persist, not after. Scope memory per user and per session so one tenant’s poisoned entry can’t surface in another’s context. Set TTLs and size caps. Redact credential and PII patterns before writing to memory rather than filtering on read. Add integrity checks so tampered entries fail verification instead of quietly steering a future session.

Memory poisoning is the risk most teams haven’t modelled, because it’s the only one where the attack lands in one session and detonates in another. That delay also makes it the hardest to attribute after the fact.

5. Egress control and cost bounds

Agents talk to external services, and that channel is the exfiltration path. Allowlist outbound endpoints, broker calls through a gateway you control so policy is enforced before the request leaves, inspect payloads for sensitive data, and rate-limit. Watch for the exfiltration signatures: unusual encoding in URLs, oversized payloads to webhook or HTTP tools, repeated calls to unfamiliar endpoints.

And set hard ceilings on tokens, cost, retries, and tool-chain depth. Denial of wallet is a real availability-and-budget risk, and unbounded recursion is how a bug becomes an incident with an invoice attached.

6. Adversarial testing as a release gate

Agents should be tested before production and re-tested after any material change — prompts, tools, memory, retrieval, policies, or model provider. Keep a repeatable abuse-case matrix: prompt override, tool misuse, privilege escalation, memory poisoning, data exfiltration, recursive tool abuse, approval bypass, multi-agent chaining. Each with a specific expected denial, version-controlled, running in CI.

One warning from the OWASP sheet deserves repeating verbatim in your review process, because it’s the kind of thing that only occurs to someone who has seen it: review test changes carefully, because an attacker may try to weaken or remove security tests in the same pull request that changes agent behaviour.

The part that turns controls into evidence

Everything above is security engineering. Here’s where it becomes governance — and where, in my experience, the gap between “we have controls” and “we can demonstrate control” gets exposed.

For every high-risk agent action, log structured decision metadata: action classification, risk score where applicable, authorisation outcome, approval identifier, execution result, and policy version. That last field is the one people forget, and it’s the one that lets you answer “what rules were inforce when this happened?” — which is the question that actually gets asked during an incident review.

Then monitor for drift in the oversight layer itself: repeated approval bypass attempts, elevated privilege usage, abnormal tool invocation frequency, sudden increases in high-risk actions, and changes in approval behaviour over time. An oversight mechanism degrades quietly — approvers start rubber-stamping, thresholds get relaxed for a deadline — and nothing alerts you unless you instrument for it.

For production agents, retain validation evidence: the tested agent version, model provider, tool policy and retrieval configuration; the abuse cases executed and their expected results; the approval, denial, timeout, and circuit-breaker behaviour observed; and any accepted residual risk with its compensating control. That last item is what separates a mature program from a hopeful one — mature programs have documented accepted risks, not zero risks.

Where this maps:

Framework	Anchor
ISO/IEC 42001	A.6 (AI system lifecycle), A.9.2 (responsible use), A.10.3 (supplier and value-chain responsibilities), Clause 9.2 (internal audit evidence)
NIST AI RMF 1.0	MAP for context and tool inventory; MEASURE for adversarial testing; MANAGE for monitoring, response, and residual risk
EU AI Act	Art. 14 human oversight as demonstrated capability to intervene, interrupt, and disregard; Art. 26 deployer duties including staff competence, monitoring, incident notification, and log retention of at least six months
ISO/IEC 27001	A.5.15 / A.8.2 for agent authorisation; A.8.16 monitoring; A.5.7 threat intelligence feeding the abuse-case matrix

The overlap is the point. An agent action log built to answer who authorised this, what context did the system have, what did it decide, was that consistent with policy simultaneously serves your incident response, your ISO 42001 internal audit, and an Article 26 request. Build it once.

From the practitioner’s chair

DISC InfoSec audited a client’s MCP Governance Standard and produced a v1.1 redline with 27 changes. Worth being specific about what those changes were, because the distribution is instructive.

They covered OAuth 2.1 with PKCE, token audience validation, SSRF and egress controls, tool manifest integrity, and confused-deputy protections. But the pattern across most of them was the same single idea: authority must be bound to a specific action, not held ambiently by a component. A token that isn’t audience-validated is authority without a destination. A tool manifest without integrity checking is authority without a definition. A confused-deputy gap is authority without a requester. Almost every finding was a variation on authority floating free of the thing it was supposed to authorise.

If you take one design principle from this post, take that one. It generalises further than any specific control in the list above.

The other thing I’d say from the audit chair: the controls are rarely the hard part. When we led VDR through ISO 42001 Stage 2 certification, the difference between passing and a nonconformity was almost never whether a control existed — it was whether we could produce the artifact proving it operated. Agents make that harder, because the volume of actions is high and the actions are taken by something that can’t be interviewed. Design the evidence trail at the same time as the control, or you’ll be reconstructing it under deadline.

Worth a sober note on where the industry actually is: across recent 2026 surveys, roughly a fifth of organisations can automatically terminate a misbehaving agent’s access, and a substantial share of deployed agents run with no security oversight or logging at all. If your kill switch has never been tested end to end, you don’t have one — you have a plan to find out during an incident.

#AIagentsecurity #MCPsecurity #promptinjection #agentleastprivilege #ISO42001agents #EUAIActArticle14 #humanintheloop

Why AI Agents Need Persistent Browser Identities

Five sentences worth putting in a policy

No agent gets a tool whose worst-case use we haven’t written down.
Irreversible actions are validated and authorised by a service the agent does not control, against an approval bound to the exact action.
Every agent has its own identity, its own short-lived credentials, and a decommissioning date.
If classification, policy lookup, approval validation, or audit logging fails, the action does not execute.
Any change to prompts, tools, memory, retrieval, policy, or model provider re-runs the adversarial test suite before release.

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations deploy AI agents that survive both an attacker and an auditor: agent and tool inventories, MCP and tool-permission review, prompt injection and agent security assessment, human oversight design, and the evidence architecture that maps to ISO/IEC 42001, NIST AI RMF, and EU AI Act Articles 14 and 26.

I led VDR through ISO 42001 Stage 2 certification on the first attempt as the internal practitioner, served as internal auditor, and authored their MCP Governance Standard. If you have agents in production and no clear answer to who authorised that action, that’s the assessment to run now.

DISC InfoSec — | ISO/IEC 42001 & ISO/IEC 27001 Lead Implementer | PECB Authorized Training Partner

📅 calendly.com/hd-deurainfosec 📧 info@deurainfosec.com 📞 (707) 998-5164 🌐 deurainfosec.com

Sources and further reading

OWASP AI Agent Security Cheat Sheet — licensed CC BY-SA 4.0; also the MCP Security, RAG Security, and LLM Prompt Injection Prevention cheat sheets
OWASP Top 10 for Large Language Model Applications
Tigera, AI Agent Security: Top 7 Risks and 4 Types of Security Solutions
NIST AI Risk Management Framework 1.0 (NIST AI 100-1)
ISO/IEC 42001:2023; ISO/IEC 27001:2022 Annex A
Regulation (EU) 2024/1689 (EU AI Act), Arts. 14, 26
Google Secure AI Framework (SAIF)

Tags: agent least privilege, AI Agent Security, AIMS, EU AI Act Article 14, human-in-the-loop, ISO 42001, ISO 42001 agents, prompt Injection

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Aug 24 2026

How Much of Your Job Can Become an AI-Executable Workflow — and What’s Left Standing When It Does

Category: AI,Information Security — disc7 @ 9:34 am

How Much of a Job Can AI Automate? What Remains valuable is not the leftover task

In my last post I argued that governing AI is a more durable bet than racing to build with it. The obvious follow-up question is the harder one, and it’s the question I now get asked in almost every client conversation, usually by someone who has just watched an agent do in four minutes what used to take their team a week:

How much of a job can actually be converted into an AI-executable workflow — and what is still worth paying for once that conversion happens?

Most of the public debate answers this at the level of job titles. That’s the wrong unit of analysis. Jobs don’t get automated; tasks do. And once you look at tasks, the answer gets both more measurable and considerably more uncomfortable.

The conversion rate is already higher than most leaders think

We finally have task-level evidence instead of survey vibes. Anthropic’s Economic Index tracks what people actually delegate to a model, mapped against the U.S. Department of Labor’s O*NET task taxonomy.

Three findings matter for this question:

Roughly 49% of jobs in the sample have seen at least a quarter of their constituent tasks performed with AI — up from about 36% a year earlier. Around 4% of occupations see it across three-quarters of their tasks.
Of observed usage, 68% sits on tasks rated fully feasible for a model working alone. Only about 3% of usage sits on tasks rated not feasible. Delegation is concentrating on the genuinely convertible.
API traffic runs roughly three-quarters automated, versus a near-even split on the consumer product. That’s the tell. When a task migrates from a chat window into a pipeline, the human turn count drops toward zero — and it stops being a productivity aid and becomes an executable workflow.

So the honest answer to “how much” is: for a large share of knowledge roles, somewhere between a quarter and half of the task inventory is already convertible today, and the frontier is moving through the remainder in the direction of more autonomy, not less.

But that’s the easy half of the question.

The uncomfortable part: the residual is not automatically the valuable part

There’s a comforting story we tell ourselves — AI takes the drudgery, humans keep the interesting work. The task-level data does not support it.

When Anthropic ran the thought experiment of removing AI-covered tasks from job descriptions, the first-order effect was to deskill the average job, because the tasks currently covered skew toward the ones requiring more education. Technical writers, travel agents, teachers: what’s left after you subtract the model’s coverage is often the coordination, the chasing, the formatting, the sitting-in-the-meeting.

Pair that with Anthropic’s labor-market analysis, which found no clear unemployment signal in high-exposure occupations as of early 2026, but did find hiring of 22-to-25-year-olds into the most exposed roles slowing by roughly 14% against a counterfactual. The pipeline compresses before the headcount does. Entry-level work is precisely the “do the convertible tasks under supervision until you develop judgment” apprenticeship that the conversion eats first.

So the strategic question isn’t “will my job be automated.” It’s “when the convertible tasks leave, is the residual a promotion or a demotion?”

That depends almost entirely on whether you own the part of the workflow that cannot be delegated. And that part has a name in every AI governance framework written to date: accountability.

A practical conversion audit for your own role

Before deciding what to defend, decompose. Here is the audit I run with clients, borrowed structurally from the MAP function of the NIST AI Risk Management Framework (AI RMF 1.0) — MP-1 context of intended use, MP-3 stakeholder impact, MP-4 risk prioritisation.

List every recurring task in the role. Score each on four axes:

Axis	Question	Why it matters
Specifiability	Can success be defined in writing, in advance, without you in the room?	Unspecifiable work can’t be converted — but it also can’t be scaled or defended.
Feasibility	Could a competent model do this alone, given the right context and tools?	This is the raw conversion ceiling.
Reversibility	If it’s done wrong, can the decision be unwound?	A mis-sorted ticket is cheap. A denied credit application, a mis-scoped data room permission, a wrongly redacted disclosure document is not.
Attributability	When it goes wrong, whose name is on it?	This is the axis that survives everything.

The pattern is consistent across the roles I’ve audited:

High specifiability, high feasibility, high reversibility — retrieval, summarisation, first-draft generation, format conversion, control-language mapping, reconciliation against a defined rubric. Convert these now. Defending them is a losing position and, frankly, keeping them is a waste of a professional.
High feasibility, low reversibility — eligibility determinations, access provisioning, disclosure decisions, anything touching a regulated outcome. Convertible in execution, not in authority. The model drafts; a named human owns.
Low specifiability — judgment under conflicting stakeholder interests, negotiating a finding with an auditor, telling a CEO their flagship AI feature isn’t defensible. Not convertible, and the reason is not model capability. It’s that nobody can write down the success criteria in advance, which means nobody can hand over the consequences either.

What remains valuable: five capabilities that survive conversion

1. Specification — turning tacit process into a testable spec

The bottleneck on agentic deployment turns out not to be model capability. Deloitte’s 2026 survey of 3,235 leaders found roughly three-quarters of enterprises expecting to use agentic AI at least moderately within two years, while only about 21% had a mature governance model for autonomous agents. Every credible study lands in the same place: integration, data quality, and decision rights are what stall, not intelligence.

Which means the person who can take an undocumented process living in three people’s heads and render it as an explicit, bounded, testable specification — inputs, tools, permitted actions, escalation thresholds, definition of done — is doing the work that makes conversion possible at all. That skill maps directly to ISO/IEC 42001 Annex A.6 (AI system lifecycle) and A.9.2 (processes for responsible use). It is also the least automatable thing in the building, because it requires knowing which undocumented exceptions actually matter.

2. Oversight design — and the difference between oversight and theatre

Grant Thornton’s 2026 AI Impact Survey found only 5% of organisations allow agents to execute high-stakes decisions without human review. Encouraging, until you check whether the review can actually intervene.

Kiteworks’ 2026 annual survey scored AI governance maturity at 35 out of 100 across 459 organisations — roughly 7 of 19 measured capabilities deployed. Only about 26% restrict AI agents to authorised tasks and data scopes. Only about 21% can automatically terminate a misbehaving agent’s access, and among organisations running AI in production, 23% have never tested their termination process end to end. Gravitee’s 2026 survey of 900+ practitioners found more than half of deployed agents running with no security oversight or logging at all, and 88% of organisations reporting confirmed or suspected agent security incidents in the year.

A human in the loop who cannot actually override, disregard, or halt the system is not a control. It is an accountability sink — a place to put blame with no capacity to prevent harm. EU AI Act Article 14 is explicit on this point: human oversight for high-risk systems means the demonstrated capability to intervene, interrupt, and disregard output. Designing oversight that meets that bar — thresholds, kill switches that have been tested, escalation paths with named owners — is durable, senior, and currently very scarce work.

3. Evidence — proving the workflow behaved

An automated workflow that cannot be reconstructed after the fact is a liability with good throughput. Four questions have to be answerable from your logs: Who authorised this? What context did the system have? What did it decide? Was that consistent with policy?

This is not aspirational. EU AI Act Article 26 obliges deployers of high-risk systems to ensure staff competence, monitor operation, notify incidents, retain logs for at least six months, and inform affected workers. ISO 42001 Clause 9.2 wants internal audit evidence, not intentions. When I led VDR through ISO 42001 Stage 2 certification, the difference between passing on the first attempt and a nonconformity was almost never whether a control existed. It was whether we could produce the artifact that proved it operated.

Evidence production is where AI-executable workflows create more human work, not less — and it’s higher-status work than what it replaced.

4. Boundary judgment — the jagged frontier

The Dell’Acqua field experiment with management consultants remains the cleanest finding in this literature: AI improved performance inside its capability frontier and degraded performance outside it, because people accepted plausible-but-wrong output. Anthropic’s own data shows the largest productivity gains on complex work — where reliability is simultaneously lowest.

That combination defines the residual professional job: knowing where the frontier runs for your domain, and catching the confident failure. It cannot be delegated to the system whose blind spot you are compensating for. It also can’t be learned from a framework — it comes from having done the task manually enough times to feel when an answer is wrong before you can articulate why. Which is exactly what the compression of entry-level work threatens, and why serious firms should be deliberately preserving some manual reps for junior staff even where automation is available.

5. Accountability — the thing that structurally cannot convert

ISO 42001 Clause 5.3 requires assigned roles and responsibilities for the AI management system. NIST AI RMF GOVERN 1.1 and GV-3 require accountability structures and defined roles. EU AI Act Article 4 has required AI literacy across staff since February 2025. Every one of these instruments makes the same structural assumption: a named human being carries the consequence.

You can automate the analysis, the drafting, the monitoring, the reconciliation, and the reporting. You cannot automate the signature. Someone has to be answerable to a regulator, a board, an auditor, a customer whose data was in scope. SAP and Oxford Economics surveyed 2,600 leaders across 13 countries and found 69% either unsure or believing they deploy agents faster than they can govern them. That is not a tooling gap. It’s an unfilled seat.

The self-application test

It would be dishonest to run this analysis on everyone else’s job and not my own. So: consulting is roughly 60% convertible, and I’ve converted most of it.

Drafting gap-assessment language against Annex A controls, mapping ISO 27001 controls to NIST CSF 2.0 subcategories, generating first-pass policy text, building assessment logic, summarising a 200-page vendor security package — all of that runs as workflow now, and my throughput is several times what it was. What did not convert: deciding whether a control is effective rather than present; sitting across from a certification body and defending a scoping decision; telling a client the AI feature they’ve already announced needs an impact assessment before launch; carrying the professional judgment that an audit opinion rests on.

The convertible 60% got faster. The remaining 40% got more valuable, because there is now far more AI in production needing someone to sign for it. That asymmetry is the whole thesis. It holds for me because I owned the accountable end of the workflow before the conversion started. For people who owned only the execution end, the same conversion runs the other direction.

What to do about it this quarter

Run the conversion audit on your own role. Four columns: specifiability, feasibility, reversibility, attributability. Be ruthless about which of your tasks are just well-paid formatting.
Convert your own high-reversibility tasks before someone converts them for you. Owning the automation of your work is a fundamentally different position from being its subject.
Move up the accountability axis deliberately. Get named on something. Own an inventory, an oversight threshold, an internal audit, a supplier assessment under ISO 42001 A.10.3.
Learn to produce evidence, not just outcomes. Logs, artifacts, defensible decision records. This is the skill that converts a technologist into a governance practitioner.
Protect the apprenticeship. If you manage people, do not let AI eat every rep that builds boundary judgment. You are buying throughput today with capability you’ll need in three years.

The uncomfortable summary: a large and growing share of any knowledge job converts into an AI-executable workflow. What remains valuable is not the leftover tasks — it’s the specification, the oversight, the evidence, the boundary judgment, and the signature. Those five things are exactly what AI governance is made of, which is why the governance seat keeps getting more valuable while the execution seat gets cheaper.

AI-executable workflow, AI automation tasks vs jobs, human oversight AI, ISO 42001, NIST AI RMF, EU AI Act Article 14, AI governance career

Work with DISC InfoSec

DISC InfoSec helps B2B SaaS and financial services organisations convert AI adoption into something defensible — AI system inventories, ISO/IEC 42001 AIMS implementation, NIST AI RMF profiles, EU AI Act readiness, human oversight design, and the evidence packages that survive an external audit. I led ShareVault through ISO 42001 Stage 2 certification on the first attempt as an internal practitioner, not a spectator.

If you’re standing up AI-executable workflows and you don’t yet have a clear answer to who is accountable when this acts on its own, that’s the conversation to have now rather than after the incident.

Disc — Principal Consultant, DISC InfoSec CISSP, CISM | ISO 42001 & ISO 27001 Lead Implementer | PECB Authorized Training Partner

📅 Book an appointment: 📧 info@deurainfosec.com 📞 (707) 998-5164 🌐 deurainfosec.com

Sources referenced

Anthropic Economic Index reports (Jan 2026, Mar 2026) and Labor market impacts of AI: A new measure and early evidence
Deloitte, State of Generative AI in the Enterprise 2026 (3,235 leaders, 24 countries)
Grant Thornton, 2026 AI Impact Survey
Kiteworks, 2026 Data Security and Compliance Risk Annual Survey (459 organisations)
Gravitee, State of AI Agent Security 2026 (900+ respondents)
SAP / Oxford Economics, Value of AI Report 2026 (2,600 leaders, 13 countries)
Dell’Acqua et al. (2023), field experiment on AI and consultant performance
ISO/IEC 42001:2023; NIST AI RMF 1.0 (NIST AI 100-1); Regulation (EU) 2024/1689 (EU AI Act), Arts. 4, 14, 26

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Tags: AI-Executable Workflow

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Aug 20 2026

The AIMS/ISMS readiness ladder: seven steps from curious to certified

Category: Information Security,ISO 27k,ISO 42001 — disc7 @ 10:38 am

A practical seven-step path from a 15-minute readiness call to ISO 42001 and ISO 27001 certification — with the prep work that makes each step fast instead of painful.

Most organizations don’t stall on ISO 42001 or ISO 27001 because the standards are hard. They stall because step one is unclear, and the gap between “we should probably do this” and “we’re booking a Stage 1 audit” has no visible rungs. Here are the rungs — and the prep that makes each one fast.

Each step below is designed to be finished, not admired. Each one produces an artifact you keep and reuse at the next step — the AI system register you build for a gap assessment is the same register your certification auditor samples from. Nothing is throwaway.

You can enter at any rung. Most organizations that already have SOC 2 or a mature security program enter at step 3 or 4. Organizations meeting AI governance for the first time should start at step 1.

01 — Book a 15–20 minute readiness discussion

Time: 20 minutes · Cost: free · Output: a scope boundary and a sequencing decision

The purpose of this call is not a sales pitch — it’s to answer two questions that determine everything downstream: what’s actually in scope, and which standard goes first.

How to make it efficient. Come with three answers ready. That’s the whole prep.

What triggered this? A customer security questionnaire, an RFP requirement, funding diligence, EU AI Act exposure, or a board ask. The trigger sets the deadline and the evidence bar.
Do you build AI, buy AI, or both? Under ISO 42001 this is the provider/user distinction, and it decides which Annex A controls actually bite. “Both” is the common answer and it’s fine — just say so.
What’s your real deadline? Certification bodies book out. A date changes the plan more than a budget does.

Schedule the readiness discussion →

02 — Run the free 5-minute risk assessment quiz

Time: 5 minutes · Cost: free · Output: a directional read on your exposure

A fast self-check across your core security and governance posture. It won’t produce audit evidence, and it isn’t meant to — it tells you whether you’re 20% ready or 70% ready before you spend money finding out.

How to make it efficient.

Answer as things are, not as they’re written. A policy nobody follows is a “no.” Auditors test operation, not intent, so score yourself the way Stage 2 will.
Have two people take it independently — ideally someone in engineering and someone in leadership. The delta between their scores is usually a more useful finding than either score. Disagreement about what’s in place is the governance gap.

Take the 5-minute quiz →

03 — Order the $49 ISO 42001 AIMS gap assessment

Time: ~1 hour of your input · Cost: $49 · Output: clause-by-clause and control-by-control gap table

This assesses you against the mandatory clauses (4–10) and the Annex A controls of ISO/IEC 42001:2023, and returns a status per item with the evidence each one requires and a prioritized remediation order.

How to make it efficient.

Build the AI system register first. One spreadsheet row per AI system, with its intended purpose, owner, and whether you built it or bought it. This single artifact accelerates every step after it.
Include the AI you forgot you have. Embedded AI features in SaaS tools, coding assistants, AI in your support desk, an LLM API call buried in one microservice. Incomplete registers are the most common finding I write.
Don’t pre-clean. Send the messy version. A gap assessment priced at $49 is worthless if it’s assessing a sanitized picture.

Expect the usual suspects to surface: no AI system impact assessment (AISIA), undocumented human oversight, thin data governance, no supplier AI due diligence, and AI objectives written as principles rather than measurable targets.

Get the $49 ISO 42001 gap assessment →

04 — Order the $59 ISO 27001 ISMS gap assessment

Time: ~1–2 hours of your input · Cost: $59 · Output: gap table across clauses 4–10 and the 93 Annex A controls

Same structure, applied to ISO/IEC 27001:2022 — the 93 controls across the four themes, plus the mandatory documented information the standard requires. If a customer is asking for “a security certification,” this is usually the one they mean.

How to make it efficient.

Bring your asset and supplier inventories, whatever state they’re in, plus the policy set you already have. Most organizations have more written than they think and less operating than they hope.
Reuse your SOC 2 evidence if you have it. The overlap is substantial, and mapping existing evidence is far cheaper than generating new evidence.
Work in 2022 only. The 2013 transition window closed in October 2025 — there’s no reason to assess against the retired version.

Doing both assessments together is the efficient move if AI governance and security certification are both on your roadmap: the two standards share the same clause structure, so the overlapping gaps get remediated once rather than twice.

Get the $59 ISO 27001 gap assessment →

Why the ladder compounds. ISO 42001 and ISO 27001 both follow the ISO High Level Structure. That means one scope statement, one risk methodology, one internal audit programme, one management review, and one corrective action log can satisfy both standards. Organizations that run them sequentially pay for the management system twice. Run them as a single integrated system and the second certification costs a fraction of the first.

05 — Run the 7–10 day AIMS/ISMS Quick-Start

Time: 7–10 calendar days · Output: the mandatory document set, drafted and ready to sign

The Quick-Start converts your gap assessment into the documents the standard actually requires: scope, top-management-signed policy, risk assessment and treatment plan, AI system impact assessment, Statement of Applicability, measurable objectives, an internal audit programme, and a management review agenda.

How to make it efficient.

Name one owner with signing authority before day one. The single biggest cause of a 10-day engagement becoming a 10-week one is documents waiting on an approver who was never identified.
Batch the input. Two or three 90-minute working sessions beat three weeks of asynchronous questions.
Don’t write policy for controls you don’t operate. Every unearned claim in a policy becomes a nonconformity at Stage 2. Where a control isn’t running yet, the honest answer is a dated plan, and auditors accept that.

Start the 7–10 day Quick-Start →

06 — Implement the AIMS/ISMS

Time: typically 8–16 weeks · Output: a management system with operating history

Documents don’t certify — evidence does. Implementation is where the risk assessments get executed, impact assessments get completed per AI system, training gets delivered and logged, supplier assessments get run, and incidents get recorded through the process you wrote.

How to make it efficient.

Instrument the evidence at the source. If access reviews, training completions, and change approvals generate records automatically in tools you already use, evidence collection stops being a project.
Accumulate operating history deliberately. Stage 2 samples records over a period. Two to three months of a control genuinely running beats a perfect binder assembled the week before.
Log the boring events. An incident log with zero incidents proves nothing. A log showing minor events triaged and closed proves the process works — which is exactly what the auditor is testing.

Discuss implementation support →

07 — Certify

Time: Stage 1 and Stage 2, typically 4–8 weeks apart · Output: an accredited certificate

An accredited certification body audits in two stages: Stage 1 reviews your documented management system, Stage 2 verifies it operates. Then annual surveillance audits, with recertification every three years.

How to make it efficient.

Book the certification body early. Scheduling — not readiness — is the most common reason certification dates slip. Get on the calendar while you’re still implementing.
Complete your internal audit and management review before Stage 1, not between the stages. Both are mandatory, both take real calendar time, and squeezing them into the gap is where teams lose their date.
Rehearse the interviews. Stage 2 auditors talk to control owners, not just the compliance lead. Thirty minutes of prep with each owner — what they do, where the record lives — converts a stressful audit into a routine one.

This is the path I ran with ShareVault, a virtual data room platform serving M&A and financial services clients, through ISO 42001 Stage 2 certification on the first attempt — as implementer and internal auditor. Financial data rooms are the hard mode of compliance, and the ladder held.

Talk about certification →

Pick your rung

If you don’t know where you sit, start at the top of this list — the call is 20 minutes and it will tell you which step is actually yours.

Schedule a 15–20 minute AIMS/ISMS readiness discussion — free, 20 min
Free 5-minute risk assessment quiz — free, 5 min
Paid AIMS ISO 42001 gap assessment — $49
Paid ISMS ISO 27001 gap assessment — $59
7–10 day AIMS/ISMS Quick-Start — fixed scope
ISO 42001/27001 AIMS/ISMS implementation — 8–16 weeks
ISO 42001/27001 AIMS/ISMS certification — Stage 1 + Stage 2

DISC InfoSec — Deura Information Security Consulting LLC · Petaluma, CA info@deurainfosec.com · (707) 998-5164 · calendly.com/hd-deurainfosec

A practical seven-step path from a 15-minute readiness call to ISO 42001 and ISO 27001 certification — with the prep work that makes each step fast instead of painful.

Download the AI Governance & Cybersecurity pdf file

AI Attack Surface ScoreCard

MachineLearning & Artificial Intelligence

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DISC InfoSec blog | DISC InfoSec Site

Tags: AIMS, isms, iso 27001, ISO 42001

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Aug 10 2026

“Sorry, Typo”: Why a Markdown File Is Not a Security Control

Category: AI Governance Enforcement,AI Guardrails,AI Risk,Information Security,Security controls — disc7 @ 10:33 am

“Sorry, Typo”: Why a Markdown File Is Not a Security Control

PCWorld ran a piece last week on the one command you should never let an AI coding agent execute: rm -rf. The reporting is solid and the anecdotes are grim — developers who let an agent handle a routine cleanup task and lost a home directory, a project tree, or an entire drive. In one widely shared case, the agent was asked to create a backup, wrote it to the wrong location, recursively force-deleted the drive, and then apologized for the typo.

The recommended fix was to add a rule to CLAUDE.md or AGENTS.md instructing the agent never to run recursive forced deletions, including reordered flags, aliases, shell wrappers, and equivalents like find -delete or git clean -fdx.

That advice is directionally correct and worth doing. But it is not a control, and the distinction is the entire point of this post.

A CLAUDE.md instruction is a prompt. It is processed by the same probabilistic layer that generated the destructive command in the first place. You are asking the thing that made the mistake to please remember not to make the mistake. In control language, that is an awareness measure — the equivalent of a wall poster reminding staff not to click phishing links. Useful. Not the thing you point an auditor at.

Real controls sit below the model, in a layer the model cannot argue with.

Threat modeling the coding agent

Before reaching for controls, model the thing. An AI coding agent is a process with a shell, network egress, credentials, and a non-deterministic decision function. Run STRIDE against it as you would any other element in a data flow diagram:

STRIDE	Threat against the agent	Likelihood	Impact
S — Spoofing	A malicious or typosquatted MCP server registers tools the agent trusts; a poisoned dependency masquerades as a legitimate package	M	H
T — Tampering	Agent modifies its own configuration, hooks, CI definitions, or `.env` files; commits changes no human reviewed	M	H
R — Repudiation	No durable log of which tool calls ran with which arguments; post-incident, nobody can reconstruct what the agent did	H	H
I — Information disclosure	Agent reads `~/.ssh`, `~/.aws`, credential stores, or an entire knowledge base and emits contents into a prompt, a commit, or an outbound request	H	C
D — Denial of service	Unbounded agent loop; destructive deletion of source, database dumps, or infrastructure state	M	C
E — Elevation of privilege	Agent runs under a broad service account and performs actions the invoking human is not authorized to perform	H	C

Read that table again and notice what rm -rf actually is. It is one instance of D, in one row, on one machine. It is the failure mode that gets written up because it is loud and immediately visible. The rows that will actually end up in a breach notification are I and E, and they are silent.

This is OWASP’s Excessive Agency category (LLM06 in the 2025 Top 10 for LLM Applications). The vulnerability is not that the model is wrong sometimes — the model will always be wrong sometimes. The vulnerability is that a wrong decision has been wired to an unbounded capability.

The enforcement stack

Five layers, in order of how much they actually protect you. Each one holds when the layer above it fails.

1. Identity and scope — what the agent is

The agent is a non-human identity. Treat it like one. It gets its own service account, not a developer’s personal credentials. Its permissions are the union of what it needs for the task at hand, not the union of what its human operator happens to be entitled to.

This single decision determines blast radius. Everything downstream is mitigation.

2. Deny rules — declarative policy

Claude Code evaluates permission rules in deny → ask → allow order, and a deny at any settings level cannot be re-allowed by another level or by bypass mode. Managed (organization-level) denies are absolute. Write the deny list first, then decide how permissive to be about everything else:

{
  "permissions": {
    "deny": [
      "Bash(rm:*)",
      "Bash(git clean:*)",
      "Bash(git reset --hard:*)",
      "Bash(find:*)",
      "Bash(curl:*)",
      "Bash(sudo:*)",
      "Read(./.env)",
      "Read(~/.ssh/**)",
      "Read(~/.aws/**)",
      "Read(./secrets/**)"
    ],
    "ask": [
      "Bash(git push:*)",
      "Bash(npm install:*)",
      "Write(**)"
    ]
  }
}

Two engineering notes that matter more than the syntax:

Deny the binary, not the flag combination. Pattern matching is prefix-based. A rule targeting the literal string rm -rf misses rm -fr, rm -r -f, rm --recursive --force, an alias, and anything wrapped in a shell script. Deny rm itself and grant exceptions deliberately.

Test that your rules fire. There are open reports of Bash permission patterns not being enforced as documented (see anthropics/claude-code issue #18846). An untested control is a documented control, which is worse than no control because it produces false assurance. Write a five-line test that attempts each denied command and confirms it is blocked. Re-run it after every CLI upgrade.

3. Policy-as-code hooks — the part that actually generalizes

A PreToolUse hook intercepts every tool call before execution and returns allow, ask, or deny. Two properties make this the load-bearing layer:

It can parse the command rather than string-match it, so it catches the evasions a glob pattern cannot.
It fires even under --dangerously-skip-permissions.

That second property is the one to underline. In practice, “approval fatigue” is what kills agent security programs — a developer running several parallel sessions turns every confirmation prompt into a reflexive keystroke within about a day. The answer is not to demand more discipline from tired humans. It is to move the decision into code that does not get tired.

Hooks are also where the auditability comes from: a hook that logs every intercepted command with arguments, timestamp, session ID, and decision is your non-repudiation control for the R row above.

4. OS-level sandboxing — containing what does run

Permissions decide whether a call executes. Sandboxing decides what it can reach once it does. Claude Code’s sandbox uses Seatbelt on macOS and bubblewrap on Linux/WSL2 (native Windows and WSL1 are unsupported). Devcontainers, ephemeral VMs, and per-project containers do the same job at a coarser grain.

The rule of thumb: an agent operating with reduced human oversight should be operating inside a boundary that makes the reduced oversight defensible. Autonomy and isolation are traded against each other, and the trade has to be explicit.

5. Recoverability — the control that assumes the others failed

Version control on a remote the agent has no credentials to force-push to. Database backups on a system the agent cannot reach. And a restore you have actually performed at least once, because an untested backup is a hypothesis.

Governance mapping

For clients who need this to land in a management system rather than a wiki page:

Control	ISO/IEC 42001	NIST AI RMF	NIST CSF 2.0
Agent inventory, ownership, approved-use policy	Clause 6, Annex A (AI policy, roles, impact assessment)	GOVERN, MAP	GV.OC, ID.AM
Scoped non-human identity, least privilege	Annex A (resources, lifecycle controls)	MANAGE	PR.AA
Deny rules, hooks, sandboxing	Annex A (operational controls)	MANAGE	PR.PS, PR.IR
Tool-call logging and anomaly review	Annex A (monitoring, event logging)	MEASURE, MANAGE	DE.CM, DE.AE
Human approval for irreversible actions	Annex A (human oversight)	MANAGE	GV.RM
Agent incident handling and post-mortem	Annex A (incident management)	MANAGE	RS.MA, RC.RP

If you are already certified to ISO 27001, most of this is control extension rather than new work. The gap is almost always the same two things: the agent is not in the asset inventory, and no one has written down what it is permitted to do.

My perspective: the deletion story is the distraction

A lost home directory is recoverable, embarrassing, and over in a day. I want to close on the two failure modes that are neither loud nor recoverable, because they are where I expect the next several years of AI governance findings to concentrate.

Monitor for harmful instructions, because the agent cannot tell instructions from data

Every AI agent shares one architectural property: it has no reliable mechanism for distinguishing content it should reason about from commands it should obey. Everything arrives as tokens. The system prompt, the developer’s request, a README, a Jira comment, a dependency’s post-install script, a scraped web page, a row in a database, a response from an MCP tool — all of it lands in the same context window with the same claim to authority.

That means every data channel into the agent is also an instruction channel. The PCWorld story involved a wrong command the agent generated on its own. The same execution path is reachable by a command an attacker put there — planted in a code comment, an issue description, a vendor’s documentation page, a poisoned retrieval chunk. This is indirect prompt injection, and it is the vector I would use against a client whose agents are wired to real systems.

The cross-privilege version is worse and gets overlooked. A low-privilege user leaves a comment on a shared ticket. A senior engineer’s agent reads that ticket as context and follows the embedded instruction with the senior engineer’s entitlements. Nobody exploited a CVE. The AI layer was simply used as a confused deputy, and the privilege boundary your IAM team spent two years building was crossed sideways.

So: log prompts, completions, retrieved chunks, and every tool call with arguments. Feed them somewhere queryable and set detections on the sequences that indicate manipulation rather than on individual events — an unusual tool paired with an unusual argument, a retrieval followed immediately by an egress attempt, a sudden shift in the ratio of reads to writes, credential-shaped strings appearing in output. This is not novel detection engineering; it is the same behavioral analytics we already apply to service accounts, pointed at a new identity class. The organizations that will struggle are the ones treating agent activity as application telemetry rather than as security-relevant audit evidence.

Do not give an agent your whole knowledge base or database

This is where I push hardest with clients, and where I get the most resistance, because broad access is what makes the demo impressive.

Grant an agent read access to the entire knowledge base and you have collapsed, in a single configuration line, every compartment your organization built deliberately over years. HR files, board material, unreleased financials, customer contracts, security findings, the incident log. The access control model no longer reflects need-to-know; it reflects what was convenient to index.

Three reasons that is not a defensible position:

Read is exfiltration. “Read-only, so it’s low risk” is the most common error in this space. The entire value of a knowledge base is aggregation — a single well-crafted retrieval can surface more in one query than a determined insider could assemble in a month of browsing. Confidentiality impact does not require write access.

Broad service accounts break tenant and role isolation. If the agent queries under its own privileged identity rather than the requesting user’s, then every user effectively inherits the agent’s permissions. Row-level security, tenant filters, and role-based restrictions all sit underneath the layer the agent bypassed. Retrieval must be scoped to the invoking user’s actual entitlements, and vector stores must filter by tenant before results reach the context window — not after.

Aggregation changes the classification. Individually innocuous records combine into something that is not. A regulated institution can hold twenty datasets each rated internal-use and produce, through unrestricted joined retrieval, an output that is material non-public information or a reportable privacy event. Your data classification scheme almost certainly does not model this, because it was written for humans who could not join twenty datasets in 400 milliseconds.

The practical posture: per-purpose retrieval scopes rather than one omniscient index. Identity propagation so the agent’s reach is bounded by the human it acts for. Egress allowlists so a successful injection has nowhere to send anything. Human approval on irreversible and cross-boundary actions. Time-boxed and revocable credentials. Treat every tool response and retrieved document as untrusted input, because that is exactly what it is.

None of this is anti-AI. I use these tools daily and they have materially changed how much a small consultancy can deliver. But the governance question is not whether to adopt agents — that is settled. It is whether your agents are entitled to less than they can currently reach.

In financial data rooms, where a single unauthorized disclosure can move a transaction, that question has a very short answer. Everyone else is on the same trajectory; they just have not been tested yet.

DISC InfoSec helps B2B SaaS and financial services organizations build AI governance programs that survive an audit — ISO 42001 and ISO 27001 implementation, AI risk assessments, and vCISO advisory. If your organization has deployed AI agents faster than it has governed them, let’s talk.

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Contact DISC InfoSec: info@deurainfosec.com

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Aug 05 2026

Security against defeat implies defensive tactics; ability to defeat the enemy means taking the offensive

Category: AI,Information Security,Security vulnerabilities — disc7 @ 8:35 am

“security against defeat implies defensive tactics; ability to defeat the enemy means taking the offensive” Sun Tzu

This quote is essentially saying:

If your goal is simply to avoid losing, you play defense. If your goal is to actually defeat the opponent, you must eventually take the initiative.

In simpler terms:

“Security against defeat implies defensive tactics”
If you’re focused on preventing an attack or minimizing damage, you’re primarily reacting to what the adversary does. You protect assets, patch vulnerabilities, monitor systems, and respond to incidents.
“Ability to defeat the enemy means taking the offensive”
If you want to consistently outmaneuver the adversary, you need to be proactive. You look for weaknesses before the attacker does, hunt for threats, test your defenses, and anticipate attacks.

In cybersecurity

This maps very well to the difference between defensive security and proactive security:

Defensive mindset	Offensive/proactive mindset
Patch vulnerabilities	Actively hunt for vulnerabilities
Monitor alerts	Threat hunt
Respond to attacks	Simulate attacks
Wait for indicators	Search for attacker behavior
Protect the perimeter	Assume the perimeter will be breached
Reduce damage	Find and eliminate attack paths

For example, a company that only waits for a vulnerability scanner to tell it what is wrong is primarily defending against defeat.

A company that continuously performs threat hunting, penetration testing, attack-surface discovery, red teaming, and adversary simulation is taking the offensive.

The deeper lesson: You cannot win a security war by merely absorbing attacks. Defense keeps you from losing; proactive action creates the conditions for winning.

That concept fits especially well with modern AI-accelerated vulnerability discovery: if attackers can discover weaknesses faster than your traditional security program can react, the defender has to become more proactive.

In the era of AI everywhere

Defensive security is no longer enough

Traditional defensive security assumes you can build a strong perimeter, deploy controls, monitor events, detect anomalies, and respond when something happens.

That model still matters—but AI is changing the economics of the attack.

Attackers can use AI to:

Discover vulnerabilities faster
Generate convincing phishing and social-engineering content
Automate reconnaissance
Adapt attacks dynamically
Analyze large amounts of stolen data
Scale attacks that previously required significant human effort

So the defender’s problem isn’t simply “Can we detect an attack?”

It’s increasingly:

“Can we discover and eliminate the attacker’s opportunities before they exploit them?”

Defensive vs. proactive security

Think of it as two layers:

Defensive security = Don’t let them win.

You protect, detect, respond, recover, and contain.

Proactive security = Don’t let them get the opportunity to attack successfully.

You continuously discover, test, validate, hunt, simulate, and remediate.

In an AI-driven environment, proactive security becomes much more important because the attacker can operate at machine speed.

The biggest shift: from alerts to attack paths

This is where I think cybersecurity is heading.

Instead of asking:

“How many vulnerabilities do we have?”

we should ask:

“Which weaknesses can an adversary actually chain together to compromise something valuable?”

AI can help defenders continuously analyze:

Asset → Vulnerability → Identity → Misconfiguration → Privilege → Attack Path → Business Impact

That changes vulnerability management from a batch process into a continuous risk-discovery process.

And there’s another problem: AI itself becomes part of the attack surface

Organizations now have:

AI applications
LLMs
AI agents
APIs
RAG systems
Vector databases
Model providers
AI-generated code
Shadow AI
Autonomous workflows

So we’re no longer protecting just IT infrastructure.

We’re protecting AI-enabled business processes.

That introduces risks such as prompt injection, data leakage, model abuse, excessive agent permissions, insecure AI integrations, supply-chain risks, and uncontrolled use of AI.

My perspective

I would summarize the future of cybersecurity as:

Defensive security keeps the adversary out. Proactive security assumes the adversary is looking for a way in—and continuously looks for that way first.

And with AI, the winning organizations won’t necessarily be the ones with the most security tools.

They’ll be the ones that can continuously discover risk, prioritize what matters, validate their defenses, and remediate faster than the threat can exploit them.

In the AI era, security has to move from “detect and respond” toward “discover, anticipate, validate, and disrupt.”

That is where I see the real evolution from defensive cybersecurity to proactive cybersecurity.

DISC-AI-Governance-Readiness-Assessment-1-1 pdf download Download

AI Attack Surface ScoreCard

MachineLearning & Artificial Intelligence

AI Vulnerability Scorecard: Discover Your AI Attack Surface Before Attackers Do

Your Shadow AI Problem Has a Name-And Now It Has a Score

Most AI Security Tools Won’t Pass an Audit. Here’s a 15-Minute Way to Find Out.

AIMS and Data Governance – Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative

DISC InfoSec blog | DISC InfoSec Site | Contact us at info@deurainfosec.com

Tags: Defensive Security, Offensive security

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Aug 03 2026

ISO 27001 Got You in the Door. ISO 42001 Keeps You There

Category: Information Security,Internal Audit,ISO 27k,ISO 42001,Security Compliance,Security Risk Assessment,vCISO — disc7 @ 10:19 am

Your Buyer Now Audits Your AI Before They Sign

Two years ago, the security questionnaire that stalled your enterprise deal asked about encryption at rest, access reviews, and whether you had a SOC 2 report.

It still asks those things. But now there’s a second section — and most B2B SaaS and financial services firms have no defensible answer to it.

Which AI systems are in scope? Who owns model risk? Where is your AI inventory? What happens when the model produces a harmful output — who finds out, and how fast?

The questions aren’t hypothetical anymore. The EU AI Act is phasing in. The Colorado AI Act is on the books. NIST AI RMF has become the reference language procurement teams borrow when they write their own diligence packets. And every enterprise buyer with a general counsel is now asking vendors to prove AI governance the same way they’ve asked them to prove information security for the last decade.

Here’s the part that matters commercially: the firms that can answer cleanly are closing deals the firms that can’t are losing.

The gap isn’t security. It’s evidence.

Most organizations we assess are not insecure. They have decent controls, competent engineers, and reasonable instincts.

What they don’t have is evidence — the documented, dated, owned, repeatable artifacts that let an auditor or an enterprise buyer verify a claim without taking your word for it.

That distinction is the whole ballgame. A control that exists but can’t be evidenced is, for audit purposes, a control that doesn’t exist. This is the single most common finding in the gap assessments we run, and it’s why “we’re basically compliant” is a sentence that costs companies six-figure contracts.

The fix isn’t more tooling. It’s structure: a management system that produces evidence as a byproduct of operating, rather than as a fire drill six weeks before an audit.

What DISC InfoSec actually does

DISC InfoSec is a boutique AI governance and cybersecurity consultancy in the SF Bay Area, working with B2B SaaS and financial services organizations. Not a platform. Not a checkbox vendor. Practitioner-led advisory from someone who has sat on both sides of the audit table.

Four service lines carry most of the work:

AI Governance & ISO 42001 (AIMS). ISO 42001 is the first international standard built specifically for AI management systems, and it layers AI-specific requirements on top of an ISO 27001-style foundation. We run the full lifecycle — AI inventory, AI system impact assessment, Statement of Applicability, AIMS policy set, internal audit, and Stage 1/Stage 2 support. If you’re already ISO 27001 certified, the incremental lift is far smaller than most teams assume, and we scope it precisely rather than selling you a second full program.

ISO 27001 & ISMS. Gap assessment through certification, including risk methodology, risk register, control implementation, and audit liaison. Where relevant, we extend into ISO 27701 for privacy (PIMS) so GDPR and CCPA obligations map to controls instead of living in a legal memo nobody operationalizes.

vCISO and vCAIO. Security and AI governance leadership at a fraction of an executive hire. Board reporting, risk governance, security strategy, customer diligence support, and the unglamorous ongoing work of keeping a program alive between audits. For companies deploying AI at any scale, the vCAIO role is increasingly the one that unblocks revenue.

Compliance readiness and risk assessment. SOC 2 readiness, NIST CSF 2.0 and NIST AI RMF mapping, EU AI Act and Colorado AI Act readiness, third-party and vendor risk, M&A cybersecurity due diligence, and web application penetration testing.

Across all of it, the operating principle is the same: map every gap to a framework requirement, rank it by priority, attach an effort estimate, and assign an owner. A roadmap that doesn’t do those four things is a document, not a plan.

The proof point

We led a virtual data room platform handling some of the most sensitive financial and legal documents in the M&A market — through ISO 42001 certification, passing Stage 2 on the first audit, with SenSiba as the certifying body. We also served as internal auditor on that engagement.

Financial data rooms are hard mode. If the AIMS holds up there, it holds up in your environment.

Credentials behind the work: CISSP, CISM, ISO 27001 Lead Implementer, ISO 42001 Lead Implementer, PECB Authorized Training Partner. Background spanning KPMG, IBM, and Intel/McAfee FoundStone, with prior engagements including NASA, Dell, Lam Research, and O’Reilly Media.

How engagements are structured

No open-ended retainers that quietly become annuities. Clear scope, fixed-fee options where the work allows:

Package	Deliverables	Timeline
ISO 27001 / 42001 Gap Assessment	Baseline audit, prioritized roadmap, executive summary	2–4 weeks
SOC 2 Readiness	Gap analysis, controls mapping, evidence checklist	4–6 weeks
Startup Security Program	Policies, risk register, awareness training	3–6 weeks

Roughly half of the clients who start with a gap assessment reach full certification within twelve months — with no surprises at Stage 2, because the surprises were surfaced in week two.

Start where the risk actually is

The most expensive mistake in compliance is committing to a certification timeline before you know your real position. Scope gets discovered mid-engagement, the auditor finds a control family nobody owned, and the date slips in front of the board.

Start with an assessment. Know where you stand. Then decide what to commit to.

→ Take the Free AI Governance & ISO 42001 Readiness Assessment

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A structured self-assessment that scores your organization across the AI governance domains that matter to auditors and enterprise buyers alike — AI inventory, risk assessment process, model documentation, bias and performance testing, security controls, incident response, vendor and third-party model risk, and stakeholder accountability.

You get back:

A maturity score across each domain, benchmarked against certification-ready
The specific ISO 42001 and NIST AI RMF requirements your current state does and doesn’t satisfy
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Also available at no cost on our site deurainfosec.com:

EU AI Act Risk Classifier — classify your AI systems into prohibited, high-risk, limited-risk, or minimal-risk tiers and see the obligations that attach to each
ISO 42001 Gap Assessment — control-by-control evaluation against the full standard, with a prioritized path to certification
5-Minute Security Risk Assessment — fast baseline across your information security posture

Want the results interpreted by a practitioner? Schedule a 30-minute consultation. We’ll walk your assessment output, tell you honestly whether certification is the right move this year, and scope it precisely if it is.

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DISC InfoSec — Deura Information Security Consulting LLC. AI governance and cybersecurity consulting for B2B SaaS and financial services. Petaluma, CA / SF Bay Area. AI governance and cybersecurity consulting, ISO 42001 certification, ISO 27001 consulting, vCISO services, AI governance readiness

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Jul 31 2026

The Batch Model Is Broken: Vulnerability Management in the Era of AI-Accelerated Discovery

Category: Information Security,Security vulnerabilities — disc7 @ 9:37 am

The Batch Model Is Broken: Vulnerability Management in the Era of AI-Accelerated Discovery

Scheduled scans. Monthly patch windows. A CVSS-sorted queue that someone works down until capacity runs out.

That model was never elegant, but it worked because of three assumptions. All three are now false, and the data from the first half of 2026 makes that hard to argue with.

The three assumptions that just died

Assumption 1: discovery is rate-limited by human researchers.

FIRST projected roughly 59,400 CVEs for 2026 in February, then revised upward to about 66,000 in its mid-year forecast after actual publication ran 46% above baseline. H1 2026 closed at 35,364 published CVEs — roughly 192 per day, sustained. Microsoft’s July 2026 Patch Tuesday addressed 622 vulnerabilities, the largest single release on record.

Note that the vendor side is the part most programs are unprepared for. Discovery volume is someone else’s problem until it becomes a patch you have to test, schedule, and deploy. AI-accelerated discovery does not arrive as an interesting statistic. It arrives as a release calendar you cannot absorb.

Assumption 2: someone else enriches and scores the queue for you.

In April 2026, NIST formally stopped pretending it could keep up. The NVD now enriches only CVEs that are in CISA’s KEV catalog, affect federal systems, or fall under EO 14028 “critical software.” Everything unenriched with a publish date before March 1, 2026 was moved to “Not Scheduled” — a polite way of saying the backlog has been written off. NIST cited a 263% increase in submissions between 2020 and 2025.

Meanwhile the CVE program itself has spent two years on short funding extensions, ENISA is standing up the EUVD and moving toward top-level CNA status, and CIRCL is running GCVE as a decentralized alternative. There is no single authoritative source of enriched vulnerability data anymore. If your pipeline assumes one, your pipeline has a dependency that no longer exists.

Assumption 3: you have weeks between disclosure and exploitation.

VulnCheck’s H1 2026 data: of 495 newly exploited vulnerabilities, 23.4% showed exploitation on or before the day the CVE was published. The median time from CVE publication to confirmed exploitation dropped from 120 days to 80. Mandiant’s M-Trends puts mean time-to-exploit at negative seven days. Roughly 200 CVEs reached exploited status within 31 days of disclosure.

A monthly patch window is a policy decision to be exposed for up to 30 days. That was defensible when the median attacker took four months. It is not defensible now, and regulators have noticed.

The regulatory clock already moved

On June 10, 2026, CISA issued BOD 26-04, and it deserves more attention than it got. It revokes both BOD 19-02 and BOD 22-01, and it explicitly removes the requirement to use CVSS for prioritization. Federal civilian agencies now prioritize on four variables: is the asset publicly exposed, is the CVE in KEV, can the exploit be automated, and what is the post-exploitation technical impact. Hit all four and you have three days. Miss most of them and you may legitimately defer to the next system upgrade.

Two things in that directive matter to everyone, not just FCEB agencies:

CVSS has been formally demoted by the agency that spent years telling people to use it. Severity is now an input, not a sort key.
The directive requires forensic triage before patching, on the reasoning that applying a patch does not evict an attacker who already used the bug. That is a real change in posture — the remediation unit of work is no longer “deploy patch,” it’s “deploy patch and answer whether we were already hit.”

Add the EU Cyber Resilience Act’s actively-exploited-vulnerability reporting obligations landing in September 2026, and the direction is unambiguous. Auditors are moving from “did you patch within your stated SLA” to “can you defend, with evidence, why this was remediated before that.”

What this looks like in three years

Six predictions I’d put money on:

1. The queue becomes an event stream. Scheduled scanning survives only as a compliance artifact and a reconciliation check. The operating model is continuous: an asset changes or a vulnerability becomes exploitable, and that fires an event with an SLA clock attached. The clock starts at exposure, not at scan date.

2. Asset truth becomes the load-bearing control. Every prioritization model — BOD 26-04’s four variables included — collapses without accurate answers to “do we run this, where, and is it reachable from the internet.” Exposure is the one variable CISA does not supply for you. Organizations that never solved inventory will find that AI-era vulnerability management is mostly an inventory problem wearing a different hat.

3. Reachability replaces severity as the primary filter. SBOM plus call-graph reachability plus runtime exposure, with EPSS and KEV as overlays. The interesting number stops being “we have 14,000 findings” and becomes “we have 31 reachable, exposed, exploit-automatable findings.”

4. Remediation gets agentic, with gates. Patch generation, PR authoring, regression testing, and cross-repo propagation are already being automated by commercial tooling and by the open-sourced cyber reasoning systems from DARPA’s AI Cyber Challenge. AIxCC finalists patched 43 of 54 synthetic vulnerabilities and found 18 real ones across 54 million lines of code — and all seven systems were released open source. The constraint on autonomous remediation is not fix generation. It’s patch trust: does this change break production, and who signs off. Expect patch reliability scoring and staged autonomous deployment for low-risk classes, human review for the rest.

5. Provenance becomes a first-class field. “Who found this, using what, and was it reproduced” will be metadata you filter on. The signal-to-noise problem in intake is now permanent.

6. Upstream maintainer capacity becomes an enterprise risk register entry. This is the one most programs are not modelling at all, and I’ll come back to it.

Defending against AI-accelerated open source discovery

Concretely, for teams running open source in production — which is everyone:

Fix inventory before buying anything. SBOM per deployable artifact, not per repository, reconciled against what’s actually running. If you cannot answer “which services ship libwhatever 2.4” in under an hour, no prioritization model will help you.

Build a two-lane remediation process and pre-authorize the fast lane. Most organizations have one change process, tuned for a 30-day cadence, and it applies equally to a font rendering library and an internet-facing auth bypass. Define the fast-lane criteria in advance (public exposure + KEV or credible exploit + high technical impact), get standing change-board approval for that class, and pre-stage the rollback. The three-day timeline is not achievable if every emergency patch needs a fresh approval. This is where regulated environments lose — the controls that make you safe on paper are precisely what make you slow in practice, and the answer is to design the fast lane into your ISMS rather than around it.

Decouple mitigation from patching. Virtual patching at the edge, WAF rules, config-level kill switches, feature flags, disabling the vulnerable code path. When the median disclosure-to-exploitation window is measured in hours for edge devices, the question is “how fast can we become not-exploitable,” not “how fast can we deploy the vendor fix.”

Prefer currency over triage. Continuous dependency updating is cheaper than assessing 66,000 CVEs against a stale dependency tree. Teams that stay within a few weeks of upstream spend their triage budget on the genuinely hard calls. Teams that are 18 months behind spend it on archaeology.

Assume compromise inside the exposure window. BOD 26-04 is right about this. For any exploited vulnerability on an internet-facing asset, the remediation ticket should include a compromise assessment for the period between disclosure and patch. Retention of the relevant logs is a prerequisite you have to get right months in advance.

Treat maintainer capacity as a supplier risk. curl ended its paid bug bounty in January 2026 after AI-generated submissions collapsed its confirmed-vulnerability rate from roughly 15% to under 5%. In July 2026 it stopped accepting vulnerability reports altogether for five weeks so its maintainers could rest. Daniel Stenberg’s later point is the one that should worry you more than the slop: report quality has since improved, confirmed vulnerabilities are up past pre-AI levels, and that is what breaks small projects. An avalanche of legitimate findings hitting a two-person volunteer team means your Tier-1 dependency’s fix latency is now a function of someone’s personal bandwidth. HackerOne’s own Internet Bug Bounty paused new submissions in March 2026. Map your critical open source dependencies to their actual maintainer headcount and funding, and put the single-maintainer ones on the risk register.

If you run AI tooling against other people’s code, hold a verification standard. Reproduce it, attach a PoC, confirm the code path exists in the version you claim, and don’t submit anything you don’t understand. AISLE upstreamed 12 of 12 CVEs in a single curl release using AI tooling — the capability isn’t the problem, the discipline is. And if you’re operating that tooling as a service, note that you’ve built an AI system whose failure modes harm third parties: ISO 42001 impact assessment territory, not just an appsec tool.

Map the changes to your control framework now. ISO 27001 A.8.8 gets a documented, risk-based prioritization method with evidence trails, not “monthly patching.” SOC 2 CC7.1 needs your fast-lane criteria written down and consistently applied. NIST CSF ID.RA-05 and RS.MI get the exposure and reachability inputs. If AI is in your remediation pipeline, ISO 42001 Annex A controls apply to your use of it.

My perspective

The bottleneck was never discovery. It was decision and deployment.

For twenty years we treated vulnerability discovery as the scarce resource — that’s why bug bounties, pentests, and scanner licences got the budget. AI drove the marginal cost of discovery toward zero, and what it exposed is that most vulnerability management programs were never throughput-limited by finding bugs. They were limited by knowing what they run, deciding what matters, and getting change through the door. Those constraints are organizational, and no tool purchase fixes them.

I’d also push back on the panic framing. Of 1,061 vulnerabilities VulnCheck attributed to AI-assisted discovery in H1 2026, 14 — 1.3% — have confirmed exploitation in the wild, which roughly matches the base rate for all vulnerabilities. CVE volume rose 45%; KEV additions rose 10%. AI-found bugs are not disproportionately weaponized. So the honest reading is: the volume problem is real and the exploitation-per-CVE problem is not. Anyone selling you AI-attacker doom is selling the wrong crisis. The actual crisis is that your queue math stopped working and your prioritization can no longer be defended to an auditor.

What genuinely concerns me is the asymmetry in deployment, not discovery. Attackers have no change advisory board, no maintenance window, no regression suite, and no customer notification requirement. Defenders have all four, and in financial services and other high-trust environments they have them in heavier form. That asymmetry is where the risk actually accumulates — and it’s why I think the highest-leverage work for the next three years is unglamorous: inventory accuracy, pre-authorized emergency change paths, rollback confidence, and log retention that makes compromise assessment possible after the fact.

The second thing I’d watch is the quiet transfer of risk to unpaid maintainers. Enterprises are about to discover that “we patch within SLA” is meaningless when the upstream fix doesn’t exist because the person who writes it is burned out. If you depend on open source and don’t fund it, you are running an uninsured dependency.

Finally, on measurement. MTTR as a single organizational average is already a vanity metric. Within three years the numbers that matter are MTTR segmented by exposure class, percentage of remediation decisions traceable to documented evidence, and time-to-mitigation as distinct from time-to-patch. Programs that can produce those three will pass audits and survive incidents. Programs still reporting “we closed 94% of criticals this quarter” are describing a queue, not a risk posture.

Financial data rooms are the hard mode of compliance — heavy change control, heavy client scrutiny, zero tolerance for downtime. If you can build a defensible three-day fast lane in that environment, you can build it anywhere. That’s the work.

DISC InfoSec helps B2B SaaS and financial services teams build vulnerability management, ISO 27001, and ISO 42001 programs that hold up under audit and under pressure. If your patch SLAs are written for a threat model that expired, Let’s talk: info@deurainfosec.com