Jan 07 2026

Agentic AI: Why Autonomous Systems Redefine Enterprise Risk

Category: AI,AI Governance,Information Security — disc7 @ 1:24 pm

Evolution of Agentic AI

1. Machine Learning

Machine Learning represents the foundation of modern AI, focused on learning patterns from structured data to make predictions or classifications. Techniques such as regression, decision trees, support vector machines, and basic neural networks enable systems to automate well-defined tasks like forecasting, anomaly detection, and image or object recognition. These systems are effective but largely reactive—they operate within fixed boundaries and lack reasoning or adaptability beyond their training data.

2. Neural Networks

Neural Networks expand on traditional machine learning by enabling deeper pattern recognition through layered architectures. Convolutional and recurrent neural networks power image recognition, speech processing, and sequential data analysis. Capabilities such as deep reinforcement learning allow systems to improve through feedback, but decision-making is still task-specific and opaque, with limited ability to explain reasoning or generalize across domains.

3. Large Language Models (LLMs)

Large Language Models introduce reasoning, language understanding, and contextual awareness at scale. Built on transformer architectures and self-attention mechanisms, models like GPT enable in-context learning, chain-of-thought reasoning, and natural language interaction. LLMs can synthesize knowledge, generate code, retrieve information, and support complex workflows, marking a shift from pattern recognition to generalized cognitive assistance.

4. Generative AI

Generative AI extends LLMs beyond text into multimodal creation, including images, video, audio, and code. Capabilities such as diffusion models, retrieval-augmented generation, and multimodal understanding allow systems to generate realistic content and integrate external knowledge sources. These models support automation, creativity, and decision support but still rely on human direction and lack autonomy in planning or execution.

5. Agentic AI

Agentic AI represents the transition from AI as a tool to AI as an autonomous actor. These systems can decompose goals, plan actions, select and orchestrate tools, collaborate with other agents, and adapt based on feedback. Features such as memory, state persistence, self-reflection, human-in-the-loop oversight, and safety guardrails enable agents to operate over time and across complex environments. Agentic AI is less about completing individual tasks and more about coordinating context, tools, and decisions to achieve outcomes.

Key Takeaway

The evolution toward Agentic AI is not a single leap but a layered progression—from learning patterns, to reasoning, to generating content, and finally to autonomous action. As organizations adopt agentic systems, governance, risk management, and human oversight become just as critical as technical capability.

Security and governance lens (AI risk, EU AI Act, NIST AI RMF)

Zero Trust Agentic AI Security: Runtime Defense, Governance, and Risk Management for Autonomous Systems

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Agentic AI, Autonomous syatems, Enterprise Risk Management

Jan 07 2026

7 Essential CISO Capabilities for Board-Level Cyber Risk Oversight

Category: AI Governance,CISO,Data Governance,IT Governance,Risk Assessment,vCISO — disc7 @ 9:19 am

1. Governance Oversight

A CISO must design and operate a security governance model that aligns with corporate governance, regulatory requirements, and the organization’s risk appetite. This ensures security controls are consistent, auditable, and defensible. Without strong governance, organizations face regulatory penalties, audit failures, and fragmented or overlapping controls that create risk instead of reducing it.

2. Cybersecurity Maturity Management

The CISO should continuously assess the organization’s security posture using recognized maturity models such as NIST CSF or ISO 27001, and define a clear target state. This capability enables prioritization of investments and long-term improvement. Lacking maturity management leads to reactive, ad-hoc spending and an inability to justify or sequence security initiatives.

3. Incident Response (Response Readiness)

A core responsibility of the CISO is ensuring the organization is prepared for incidents through tested playbooks, simulations, and war-gaming. Effective response readiness minimizes impact when breaches occur. Without it, detection is slow, downtime is extended, and financial and reputational damage escalates rapidly.

4. Detection, Response & Automation (SOC / SOAR Capability)

The CISO must ensure the organization can rapidly detect threats, alert the right teams, and automate responses where possible. Strong SOC and SOAR capabilities reduce mean time to detect (MTTD) and mean time to respond (MTTR). Weakness here results in undetected breaches, slow manual responses, and delayed forensic investigations.

5. Business & Financial Acumen

A modern CISO must connect cyber risk to business outcomes—revenue, margins, valuation, and enterprise risk. This includes articulating ROI, payback, and value creation. Without this skill, security is viewed purely as a cost center, and investments fail to align with business strategy.

6. Risk Communication

The CISO must translate complex technical risks into clear, business-impact narratives that boards and executives can act on. Effective risk communication enables informed decision-making. When this capability is weak, risks remain misunderstood or hidden until a major incident forces attention.

7. Culture & Cross-Functional Leadership

A successful CISO builds strong security teams, fosters a security-aware culture, and collaborates across IT, legal, finance, product, and operations. Security cannot succeed in silos. Poor leadership here leads to misaligned priorities, weak adoption of controls, and ineffective onboarding of new staff into security practices.

My Opinion: The Three Most Important Capabilities

If forced to prioritize, the top three are:

  1. Risk Communication
    If the board does not understand risk, no other capability matters. Funding, priorities, and executive decisions all depend on how well the CISO communicates risk in business terms.
  2. Governance Oversight
    Governance is the foundation. Without it, security efforts are fragmented, compliance fails, and accountability is unclear. Strong governance enables everything else to function coherently.
  3. Incident Response (Response Readiness)
    Breaches are inevitable. What separates resilient organizations from failed ones is how well they respond. Preparation directly limits financial, operational, and reputational damage.

Bottom line:
Technology matters, but leadership, governance, and communication are what boards ultimately expect from a CISO. Tools support these capabilities—they don’t replace them.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: CISO Capabilities

Jan 05 2026

Deepfakes Cost $25 Million: Why Old-School Verification Still Works

Category: AI,AI Governance,Deepfakes — disc7 @ 9:01 am

A British engineering firm reportedly lost $25 million after an employee joined a video call that appeared to include their CFO. The voice, the face, and the mannerisms all checked out—but it wasn’t actually him. The incident highlights how convincing deepfake technology has become and how easily trust can be exploited.

This case shows that visual and audio cues alone are no longer reliable for verification. AI can now replicate voices and faces with alarming accuracy, making traditional “it looks and sounds right” judgment calls dangerously insufficient, especially under pressure.

Ironically, the most effective countermeasure to advanced AI attacks isn’t more technology—it’s simpler, human-centered controls. When digital signals can be forged, analog verification methods regain their value.

One such method is establishing a “safe word.” This is a randomly chosen word known only to a small, trusted group and never shared via email, chat, or documents. It lives only in human memory.

If an urgent request comes in—whether from a “CEO,” “CFO,” or even a family member—especially involving money or sensitive actions, the response should be to pause and ask for the safe word. An AI can mimic a voice, but it cannot reliably guess a secret it was never trained on.

My opinion: Safe words may sound old-fashioned, but they are practical, low-cost, and highly effective in a world of deepfakes and social engineering. Every finance team—and even families—should treat this as a basic risk control, not a gimmick. In high-risk moments, simple friction can be the difference between trust and a multimillion-dollar loss.

#CyberSecurity #DeepFakes #SocialEngineering #AI #RiskManagement

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Deepfake, Deepfakes and Fraud

Jan 04 2026

AI Governance That Actually Works: Beyond Policies and Promises

Category: AI,AI Governance,AI Guardrails,ISO 42001,NIST CSF — disc7 @ 3:33 pm

1. AI Has Become Core Infrastructure
AI is no longer experimental — it’s now deeply integrated into business decisions and societal functions. With this shift, governance can’t stay theoretical; it must be operational and enforceable. The article argues that combining the NIST AI Risk Management Framework (AI RMF) with ISO/IEC 42001 makes this operationalization practical and auditable.

2. Principles Alone Don’t Govern
The NIST AI RMF starts with the Govern function, stressing accountability, transparency, and trustworthy AI. But policies by themselves — statements of intent — don’t ensure responsible execution. ISO 42001 provides the management-system structure that anchors these governance principles into repeatable business processes.

3. Mapping Risk in Context
Understanding the context and purpose of an AI system is where risk truly begins. The NIST RMF’s Map function asks organizations to document who uses a system, how it might be misused, and potential impacts. ISO 42001 operationalizes this through explicit impact assessments and scope definitions that force organizations to answer difficult questions early.

4. Measuring Trust Beyond Accuracy
Traditional AI metrics like accuracy or speed fail to capture trustworthiness. The NIST RMF expands measurement to include fairness, explainability, privacy, and resilience. ISO 42001 ensures these broader measures aren’t aspirational — they require documented testing, verification, and ongoing evaluation.

5. Managing the Full Lifecycle
The Manage function addresses what many frameworks ignore: what happens after AI deployment. ISO 42001 formalizes post-deployment monitoring, incident reporting and recovery, decommissioning, change management, and continuous improvement — framing AI systems as ongoing risk assets rather than one-off projects.

6. Third-Party & Supply Chain Risk
Modern AI systems often rely on external data, models, or services. Both frameworks treat third-party and supplier risks explicitly — a critical improvement, since risks extend beyond what an organization builds in-house. This reflects growing industry recognition of supply chain and ecosystem risk in AI.

7. Human Oversight as a System
Rather than treating human review as a checkbox, the article emphasizes formalizing human roles and responsibilities. It calls for defined escalation and override processes, competency-based training, and interdisciplinary decision teams — making oversight deliberate, not incidental.

8. Strategic Value of NIST-ISO Alignment
The real value isn’t just technical alignment — it’s strategic: helping boards, executives, and regulators speak a common language about risk, accountability, and controls. This positions organizations to be both compliant with emerging regulations and competitive in markets where trust matters.

9. Trust Over Speed
The article closes with a cultural message: in the next phase of AI adoption, trust will outperform speed. Organizations that operationalize responsibility (through structured frameworks like NIST AI RMF and ISO 42001) will lead, while those that chase innovation without governance risk reputational harm.

10. Practical Implications for Leaders
For AI leaders, the takeaway is clear: you need both risk-management logic and a management system to ensure accountability, measurement, and continuous improvement. Cryptic policies aren’t enough; frameworks must translate into auditable, executive-reportable actions.

Opinion

This article provides a thoughtful and practical bridge between high-level risk principles and real-world governance. NIST’s AI RMF on its own captures what needs to be considered (governance, context, measurement, and management) — a critical starting point for responsible AI risk management. (NIST)

But in many organizations today, abstract frameworks don’t translate into disciplined execution — that gap is exactly where ISO/IEC 42001 can add value by prescribing systematic processes, roles, and continuous improvement cycles. Together, the NIST AI RMF and ISO 42001 form a stronger operational baseline for responsible, auditable AI governance.

In practice, however, the challenge will be in integration — aligning governance systems already in place (e.g., ISO 27001, internal risk programs) with these newer AI standards without creating redundancy or compliance fatigue. The real test of success will be whether organizations can bake these practices into everyday decision-making, not just compliance checklists.

At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.


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Tags: ISO 42001, NIST AI Risk Management Framework, NIST AI RMF

Jan 03 2026

Choosing the Right AI Security Frameworks: A Practical Roadmap for Secure AI Adoption

Category: AI,AI Governance,ISO 42001,Security and privacy Law,Security Compliance — disc7 @ 11:22 pm

Choosing the right AI security framework is becoming a critical decision as organizations adopt AI at scale. No single framework solves every problem. Each one addresses a different aspect of AI risk, governance, security, or compliance, and understanding their strengths helps organizations apply them effectively.

The NIST AI Risk Management Framework (AI RMF) is best suited for managing AI risks across the entire lifecycle—from design and development to deployment and ongoing use. It emphasizes trustworthy AI by addressing security, privacy, safety, reliability, and bias. This framework is especially valuable for organizations that are building or rapidly scaling AI capabilities and need a structured way to identify and manage AI-related risks.

ISO/IEC 42001, the AI Management System (AIMS) standard, focuses on governance rather than technical controls. It helps organizations establish policies, accountability, oversight, and continuous improvement for AI systems. This framework is ideal for enterprises deploying AI across multiple teams or business units and looking to formalize AI governance in a consistent, auditable way.

For teams building AI-enabled applications, the OWASP Top 10 for LLMs and Generative AI provides practical, hands-on security guidance. It highlights common and emerging risks such as prompt injection, data leakage, insecure output handling, and model abuse. This framework is particularly useful for AppSec and DevSecOps teams securing AI interfaces, APIs, and user-facing AI features.

MITRE ATLAS takes a threat-centric approach by mapping adversarial tactics and techniques that target AI systems. It is well suited for threat modeling, red-team exercises, and AI breach simulations. By helping security teams think like attackers, MITRE ATLAS strengthens defensive strategies against real-world AI threats.

From a regulatory perspective, the EU AI Act introduces a risk-based compliance framework for organizations operating in or offering AI services within the European Union. It defines obligations for high-risk AI systems and places strong emphasis on transparency, accountability, and risk controls. For global organizations, this regulation is becoming a key driver of AI compliance strategy.

The most effective approach is not choosing one framework, but combining them. Using NIST AI RMF for risk management, ISO/IEC 42001 for governance, OWASP and MITRE for technical security, and the EU AI Act for regulatory compliance creates a balanced and defensible AI security posture.

At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at https://deurainfosec.com.


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Tags: AI Security Frameworks

Jan 02 2026

No Breach, No Alerts—Still Stolen: When AI Models Are Taken Without Being Hacked

Category: AI,AI Governance,AI Guardrails — disc7 @ 11:11 am

No Breach. No Alerts. Still Stolen: The Model Extraction Problem

1. A company can lose its most valuable AI intellectual property without suffering a traditional security breach. No malware, no compromised credentials, no incident tickets—just normal-looking API traffic. Everything appears healthy on dashboards, yet the core asset is quietly walking out the door.

2. This threat is known as model extraction. It happens when an attacker repeatedly queries an AI model through legitimate interfaces—APIs, chatbots, or inference endpoints—and learns from the responses. Over time, they can reconstruct or closely approximate the proprietary model’s behavior without ever stealing weights or source code.

3. A useful analogy is a black-box expert. If I can repeatedly ask an expert questions and carefully observe their answers, patterns start to emerge—how they reason, where they hesitate, and how they respond to edge cases. Over time, I can train someone else to answer the same questions in nearly the same way, without ever seeing the expert’s notes or thought process.

4. Attackers pursue model extraction for several reasons. They may want to clone the model outright, steal high-value capabilities, distill it into a cheaper version using your model as a “teacher,” or infer sensitive traits about the training data. None of these require breaking in—only sustained access.

5. This is why AI theft doesn’t look like hacking. Your model can be copied simply by being used. The very openness that enables adoption and revenue also creates a high-bandwidth oracle for adversaries who know how to exploit it.

6. The consequences are fundamentally business risks. Competitive advantage evaporates as others avoid your training costs. Attackers discover and weaponize edge cases. Malicious clones can damage your brand, and your IP strategy collapses because the model’s behavior has effectively been given away.

7. The aftermath is especially dangerous because it’s invisible. There’s no breach report or emergency call—just a competitor releasing something “surprisingly similar” months later. By the time leadership notices, the damage is already done.

8. At scale, querying equals learning. With enough inputs and outputs, an attacker can build a surrogate model that is “good enough” to compete, abuse users, or undermine trust. This is IP theft disguised as legitimate usage.

9. Defending against this doesn’t require magic, but it does require intent. Organizations need visibility by treating model queries as security telemetry, friction by rate-limiting based on risk rather than cost alone, and proof by watermarking outputs so stolen behavior can be attributed when clones appear.

My opinion: Model extraction is one of the most underappreciated risks in AI today because it sits at the intersection of security, IP, and business strategy. If your AI roadmap focuses only on performance, cost, and availability—while ignoring how easily behavior can be copied—you don’t really have an AI strategy. Training models is expensive; extracting behavior through APIs is cheap. And in most markets, “good enough” beats “perfect.”

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Tags: AI Models, Hacked

Dec 30 2025

EU AI Act: Why Every Organization Using AI Must Pay Attention

Category: AI,AI Governance — disc7 @ 11:07 am

EU AI Act: Why Every Organization Using AI Must Pay Attention

The EU AI Act is the world’s first major regulation designed to govern how artificial intelligence is developed, deployed, and managed across industries. Approved in June 2024, it establishes harmonized rules for AI use across all EU member states — just as GDPR did for privacy.

Any organization that builds, integrates, or sells AI systems within the European Union must comply — even if they are headquartered outside the EU. That means U.S. and global companies using AI in European markets are officially in scope.

The Act introduces a risk-based regulatory model. AI is categorized across four risk tiers — from unacceptable, which are completely banned, to high-risk, which carry strict controls, limited-risk with transparency requirements, and minimal-risk, which remain largely unregulated.

High-risk AI includes systems governing access to healthcare, finance, employment, critical infrastructure, law enforcement, and essential public services. Providers of these systems must implement rigorous risk management, governance, monitoring, and documentation processes across the entire lifecycle.

Certain AI uses are explicitly prohibited — such as social scoring, biometric emotion recognition in workplaces or schools, manipulative AI techniques, and untargeted scraping of facial images for surveillance.

Compliance obligations are rolling out in phases beginning February 2025, with core high-risk system requirements taking effect in August 2026 and final provisions extending through 2027. Organizations have limited time to assess their current systems and prepare for adherence.

This legislation is expected to shape global AI governance frameworks — much like GDPR influenced worldwide privacy laws. Companies that act early gain an advantage: reduced legal exposure, customer trust, and stronger market positioning.

How DISC InfoSec Helps You Stay Ahead

DISC InfoSec brings 20+ years of security and compliance excellence with a proven multi-framework approach. Whether preparing for EU AI Act, ISO 42001, GDPR, SOC 2, or enterprise governance — we help organizations implement responsible AI controls without slowing innovation.

If your business touches the EU and uses AI — now is the time to get compliant.

📩 Let’s build your AI governance roadmap together.
Reach out: Info@DeuraInfosec.com

Earlier posts covering the EU AI Act

How ISO 42001 Strengthens Alignment With the EU AI Act (Without Replacing Legal Compliance)

Understanding Your AI System’s Risk Level: A Guide to EU AI Act Compliance

Identify the rights of individuals affected by AI systems under the EU AI Act by doing a fundamental rights impact assessment (FRIA)

EU AI Act’s guidelines on ethical AI deployment in a scenario

EU AI Act concerning Risk Management Systems for High-Risk AI

Understanding the EU AI Act: A Risk-Based Framework for Trustworthy AI – Implications for U.S. Organizations

Interpretation of Ethical AI Deployment under the EU AI Act

Aligning with ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act

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Tags: EU AI Act

Dec 26 2025

Why AI-Driven Cybersecurity Frameworks Are Now a Business Imperative

Category: AI,AI Governance,ISO 27k,ISO 42001,NIST CSF,owasp — disc7 @ 8:52 am

A reliable industry context about AI and cybersecurity frameworks from recent market and trend reports. I’ll then give a clear opinion at the end.

1. AI Is Now Core to Cyber Defense
Artificial Intelligence is transforming how organizations defend against digital threats. Traditional signature-based security tools struggle to keep up with modern attacks, so companies are using AI—especially machine learning and behavioral analytics—to detect anomalies, predict risks, and automate responses in real time. This integration is now central to mature cybersecurity programs.

2. Market Expansion Reflects Strategic Adoption
The AI cybersecurity market is growing rapidly, with estimates projecting expansion from tens of billions today into the hundreds of billions within the next decade. This reflects more than hype—organizations across sectors are investing heavily in AI-enabled threat platforms to improve detection, reduce manual workload, and respond faster to attacks.

3. AI Architectures Span Detection to Response
Modern frameworks incorporate diverse AI technologies such as natural language processing, neural networks, predictive analytics, and robotic process automation. These tools support everything from network monitoring and endpoint protection to identity-based threat management and automated incident response.

4. Cloud and Hybrid Environments Drive Adoption
Cloud migrations and hybrid IT architectures have expanded attack surfaces, prompting more use of AI solutions that can scale across distributed environments. Cloud-native AI tools enable continuous monitoring and adaptive defenses that are harder to achieve with legacy on-premises systems.

5. Regulatory and Compliance Imperatives Are Growing
As digital transformation proceeds, regulatory expectations are rising too. Many frameworks now embed explainable AI and compliance-friendly models that help organizations demonstrate legal and ethical governance in areas like data privacy and secure AI operations.

6. Integration Challenges Remain
Despite the advantages, adopting AI frameworks isn’t plug-and-play. Organizations face hurdles including high implementation cost, lack of skilled AI security talent, and difficulties integrating new tools with legacy architectures. These challenges can slow deployment and reduce immediate ROI. (Inferred from general market trends)

7. Sophisticated Threats Demand Sophisticated Defenses
AI is both a defensive tool and a capability leveraged by attackers. Adversarial AI can generate more convincing phishing, exploit model weaknesses, and automate aspects of attacks. A robust cybersecurity framework must account for this dual role and include AI-specific risk controls.

8. Organizational Adoption Varies Widely
Enterprise adoption is strong, especially in regulated sectors like finance, healthcare, and government, while many small and medium businesses remain cautious due to cost and trust issues. This uneven adoption means frameworks must be flexible enough to suit different maturity levels. (From broader industry reports)

9. Frameworks Are Evolving With the Threat Landscape
Rather than static checklists, AI cybersecurity frameworks now emphasize continuous adaptation—integrating real-time risk assessment, behavioral intelligence, and autonomous response capabilities. This shift reflects the fact that cyber risk is dynamic and cannot be mitigated solely by periodic assessments or manual controls.

Opinion

AI-centric cybersecurity frameworks represent a necessary evolution in defense strategy, not a temporary trend. The old model of perimeter defense and signature matching simply doesn’t scale in an era of massive data volumes, sophisticated AI-augmented threats, and 24/7 cloud operations. However, the promise of AI must be tempered with governance rigor. Organizations that treat AI as a magic bullet will face blind spots and risks—especially around privacy, explainability, and integration complexity.

Ultimately, the most effective AI cybersecurity frameworks will balance automated, real-time intelligence with human oversight and clear governance policies. This blend maximizes defensive value while mitigating potential misuse or operational failures.

AI Cybersecurity Framework — Summary

AI Cybersecurity framework provides a holistic approach to securing AI systems by integrating governance, risk management, and technical defense across the full AI lifecycle. It aligns with widely-accepted standards such as NIST RMF, ISO/IEC 42001, OWASP AI Security Top 10, and privacy regulations (e.g., GDPR, CCPA).

1️⃣ Govern

Set strategic direction and oversight for AI risk.

  • Goals: Define policies, accountability, and acceptable risk levels
  • Key Controls: AI governance board, ethical guidelines, compliance checks
  • Outcomes: Approved AI policies, clear governance structures, documented risk appetite

2️⃣ Identify

Understand what needs protection and the related risks.

  • Goals: Map AI assets, data flows, threat landscape
  • Key Controls: Asset inventory, access governance, threat modeling
  • Outcomes: Risk register, inventory map, AI threat profiles

3️⃣ Protect

Implement safeguards for AI data, models, and infrastructure.

  • Goals: Prevent unauthorized access and protect model integrity
  • Key Controls: Encryption, access control, secure development lifecycle
  • Outcomes: Hardened architecture, encrypted data, well-trained teams

4️⃣ Detect

Find signs of attack or malfunction in real time.

  • Goals: Monitor models, identify anomalies early
  • Key Controls: Logging, threat detection, model behavior monitoring
  • Outcomes: Alerts, anomaly reports, high-quality threat intelligence

5️⃣ Respond

Act quickly to contain and resolve security incidents.

  • Goals: Minimize damage and prevent escalation
  • Key Controls: Incident response plans, investigations, forensics
  • Outcomes: Detailed incident reports, corrective actions, improved readiness

6️⃣ Recover

Restore normal operations and reduce the chances of repeat incidents.

  • Goals: Service continuity and post-incident improvement
  • Key Controls: Backup and recovery, resilience testing
  • Outcomes: Restored systems and lessons learned that enhance resilience

Cross-Cutting Principles

These safeguards apply throughout all phases:

  • Ethics & Fairness: Reduce bias, ensure transparency
  • Explainability & Interpretability: Understand model decisions
  • Human-in-the-Loop: Oversight and accountability remain essential
  • Privacy & Security: Protect data by design

AI-Specific Threats Addressed

  • Adversarial attacks (poisoning, evasion)
  • Model theft and intellectual property loss
  • Data leakage and inference attacks
  • Bias manipulation and harmful outcomes

Overall Message

This framework ensures trustworthy, secure, and resilient AI operations by applying structured controls from design through incident recovery—combining cybersecurity rigor with ethical and responsible AI practices.

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps

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Tags: AI-Driven Cybersecurity Frameworks

Dec 25 2025

LLMs Are a Dead End: LeCun’s Break From Meta and the Future of AI

Category: AI,AI Governance,AI Guardrails — disc7 @ 3:24 pm

Yann LeCun — a pioneer of deep learning and Meta’s Chief AI Scientist — has left the company after shaping its AI strategy and influencing billions in investment. His departure is not a routine leadership change; it signals a deeper shift in how he believes AI must evolve.

LeCun is one of the founders of modern neural networks, a Turing Award recipient, and a core figure behind today’s deep learning breakthroughs. His work once appeared to be a dead end, yet it ultimately transformed the entire AI landscape.

Now, he is stepping away not to retire or join another corporate giant, but to create a startup focused on a direction Meta does not support. This choice underscores a bold statement: the current path of scaling Large Language Models (LLMs) may not lead to true artificial intelligence.

He argues that LLMs, despite their success, are fundamentally limited. They excel at predicting text but lack real understanding of the world. They cannot reason about physical reality, causality, or genuine intent behind events.

According to LeCun, today’s LLMs possess intelligence comparable to an animal — some say a cat — but even the cat has an advantage: it learns through real-world interaction rather than statistical guesswork.

His proposed alternative is what he calls World Models. These systems will learn like humans and animals do — by observing environments, experimenting, predicting outcomes, and refining internal representations of how the world works.

This approach challenges the current AI industry narrative that bigger models and more data alone will produce smarter, safer AI. Instead, LeCun suggests that a completely different foundation is required to achieve true machine intelligence.

Yet Meta continues investing enormous resources into scaling LLMs — the very AI paradigm he believes is nearing its limits. His departure raises an uncomfortable question about whether hype is leading strategic decisions more than science.

If he is correct, companies pushing ever-larger LLMs could face a major reckoning when progress plateaus and expectations fail to materialize.

My Opinion

LLMs are far from dead — they are already transforming industries and productivity. But LeCun highlights a real concern: scaling alone cannot produce human-level reasoning. The future likely requires a combination of both approaches — advanced language systems paired with world-aware learning. Instead of a dead end, this may be an inflection point where the AI field transitions toward deeper intelligence grounded in understanding, not just prediction.

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Tags: LLM, Yann LeCun

Dec 22 2025

Securing Generative AI Usage in the Browser to Prevent Data Leakage

Category: AI,AI Governance,AI Governance Tools — disc7 @ 9:14 am

Here’s a rephrased and summarized version of the linked article organized into nine paragraphs, followed by my opinion at the end.

1️⃣ The Browser Has Become the Main AI Risk Vector
Modern workers increasingly use generative AI tools directly inside the browser, pasting emails, business files, and even source code into online AI assistants. Because traditional enterprise security tools weren’t built to monitor or understand this behavior, sensitive data often flows out of corporate control without detection.

2️⃣ Blocking AI Isn’t Realistic
Simply banning generative AI usage isn’t a workable solution. These tools offer productivity gains that employees and organizations find valuable. The article argues the real focus should be on securing how and where AI tools are used inside the browser session itself.

3️⃣ Understanding the Threat Model
The article outlines why browser-based AI interactions are uniquely risky: users routinely paste whole documents and proprietary data into prompt boxes, upload confidential files, and interact with AI extensions that have broad permission scopes. These behaviors create a threat surface that legacy defenses like firewalls and traditional DLP simply can’t see.

4️⃣ Policy Is the Foundation of Security
A strong security policy is described as the first step. Organizations should categorize which AI tools are sanctioned versus restricted and define what data types should never be entered into generative AI, such as financial records, regulated personal data, or source code. Enforcement matters: policies must be backed by browser-level controls, not just user guidance.

5️⃣ Isolation Reduces Risk Without Stopping Productivity
Instead of an all-or-nothing approach, teams can isolate risky workflows. For example, separate browser profiles or session controls can keep general AI usage away from sensitive internal applications. This lets employees use AI where appropriate while limiting accidental data exposure.

6️⃣ Data Controls at the Browser Edge
Technical data controls are critical to enforce policy. These include monitoring copy/paste actions, drag-and-drop events, and file uploads at the browser level before data ever reaches an external AI service. Tiered enforcement — from warnings to hard blocks — helps balance security with usability.

7️⃣ Managing AI Extensions Is Essential
Many AI-powered browser extensions require broad permissions — including read/modify page content — which can become covert data exfiltration channels if left unmanaged. The article emphasizes classifying and restricting such extensions based on risk.

8️⃣ Identity and Account Hygiene
Tying all sanctioned AI interactions back to corporate identities through single sign-on improves visibility and accountability. It also helps prevent situations where personal accounts or mixed browser contexts leak corporate data.

9️⃣ Visibility and Continuous Improvement
Lastly, strong telemetry — tracking what AI tools are accessed, what data is entered, and how often policy triggers occur — is essential to refine controls over time. Analytics can highlight risky patterns and help teams adjust policies and training for better outcomes.

My Opinion

This perspective is practical and forward-looking. Instead of knee-jerk bans on AI — which employees will circumvent — the article realistically treats the browser as the new security perimeter. That aligns with broader industry findings showing that browser-mediated AI usage is a major exfiltration channel and traditional security tools often miss it entirely.

However, implementing the recommended policies and controls isn’t trivial. It demands new tooling, tight integration with identity systems, and continuous monitoring, which many organizations struggle with today. But the payoff — enabling secure AI usage without crippling productivity — makes this a worthy direction to pursue. Secure AI adoption shouldn’t be about fear or bans, but about governance, visibility, and informed risk management.

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Tags: AI Browser, Data leakage

Dec 19 2025

ShareVault Achieves ISO 42001 Certification: Leading AI Governance in Virtual Data Rooms

Category: AI,AI Governance,ISO 42001 — disc7 @ 1:57 pm

ISO 42001 Certification by Leading AI Governance in Virtual Data Rooms

When your clients trust you with their most sensitive M&A documents, financial records, and confidential deal information, every security and compliance decision matters. ShareVault has taken a significant step beyond traditional data room security by achieving ISO 42001 certification—the international standard for AI management systems.

Why Financial Services and M&A Professionals Should Care

If you’re a deal advisor, investment banker, or private equity professional, you’re increasingly relying on AI-powered features in your virtual data room—intelligent document indexing, automated redaction suggestions, smart search capabilities, and analytics that surface insights from thousands of documents.

But how do you know these AI capabilities are managed responsibly? How can you be confident that:

  • AI systems won’t introduce bias into document classification or search results?
  • Algorithms processing sensitive financial data meet rigorous security standards?
  • Your confidential deal information isn’t being used to train AI models?
  • AI-driven recommendations are explainable and auditable for regulatory scrutiny?

ISO 42001 provides the answers. This comprehensive framework addresses AI-specific risks that traditional information security standards like ISO 27001 don’t fully cover.

ShareVault’s Commitment to AI Governance Excellence

ShareVault recognized early that as AI capabilities become more sophisticated in virtual data rooms, clients need assurance that goes beyond generic “we take security seriously” statements. The financial services and legal professionals who rely on ShareVault for billion-dollar transactions deserve verifiable proof of responsible AI management.

That commitment led ShareVault to pursue ISO 42001 certification—joining a select group of pioneers implementing the world’s first AI management system standard.

Building Trust Through Independent Verification

ShareVault engaged DISC InfoSec as an independent internal auditor specifically for ISO 42001 compliance. This wasn’t a rubber-stamp exercise. DISC InfoSec brought deep expertise in both AI governance frameworks and information security, conducting rigorous assessments of:

  • AI system lifecycle management – How ShareVault develops, deploys, monitors, and updates AI capabilities
  • Data governance for AI – Controls ensuring training data quality, protection, and appropriate use
  • Algorithmic transparency – Documentation and explainability of AI decision-making processes
  • Risk management – Identification and mitigation of AI-specific risks like bias, hallucinations, and unexpected outputs
  • Human oversight – Ensuring appropriate human involvement in AI-assisted processes

The internal audit process identified gaps, drove remediation efforts, and prepared ShareVault for external certification assessment—demonstrating a genuine commitment to AI governance rather than superficial compliance.

Certification Achieved: A Leadership Milestone

In 2025, ShareVault successfully completed both the Stage 1 and Stage 2 audits conducted by SenSiba, an accredited certification body. The Stage 1 audit validated ShareVault’s comprehensive documentation, policies, and procedures. The Stage 2 audit, completed in December 2025, examined actual implementation—verifying that controls operate effectively in practice, risks are actively managed, and continuous improvement processes function as designed.

ShareVault is now ISO 42001 certified—one of the first virtual data room providers to achieve this distinction. This certification reflects genuine leadership in responsible AI deployment, independently verified by external auditors with no stake in the outcome.

For financial services professionals, this means ShareVault’s AI governance approach has been rigorously assessed and certified against international standards, providing assurance that extends far beyond vendor claims.

What This Means for Your Deals

When you’re managing a $500 million acquisition or handling sensitive financial restructuring documents, you need more than promises about AI safety. ShareVault’s ISO 42001 certification provides tangible, verified assurance:

For M&A Advisors: Confidence that AI-powered document analytics won’t introduce errors or biases that could impact deal analysis or due diligence findings.

For Investment Bankers: Assurance that confidential client information processed by AI features remains protected and isn’t repurposed for model training or shared across clients.

For Legal Professionals: Auditability and explainability of AI-assisted document review and classification—critical when facing regulatory scrutiny or litigation.

For Private Equity Firms: Verification that AI capabilities in your deal rooms meet institutional-grade governance standards your LPs and regulators expect.

Why Industry Leadership Matters

The financial services industry faces increasing regulatory pressure regarding AI usage. The EU AI Act, SEC guidance on AI in financial services, and evolving state-level AI regulations all point toward a future where AI governance isn’t optional—it’s required.

ShareVault’s achievement of ISO 42001 certification demonstrates foresight that benefits clients in two critical ways:

Today: You gain immediate, certified assurance that AI capabilities in your data room meet rigorous governance standards, reducing your own AI-related risk exposure.

Tomorrow: As regulations tighten, you’re already working with a provider whose AI governance framework is certified against international standards, simplifying your own compliance efforts and protecting your competitive position.

The Bottom Line

For financial services and M&A professionals who demand the highest standards of security and compliance, ShareVault’s ISO 42001 certification represents more than a technical achievement—it’s independently verified proof of commitment to earning and maintaining your trust.

The rigorous process of implementation, independent internal auditing by DISC InfoSec, and successful completion of both Stage 1 and Stage 2 assessments by SenSiba demonstrates that ShareVault’s AI capabilities are deployed with certified safeguards, transparency, and accountability.

As deals become more complex and AI capabilities more sophisticated, partnering with a certified virtual data room provider that has proven its AI governance leadership isn’t just prudent—it’s essential to protecting your clients, your reputation, and your firm.

ShareVault’s investment in ISO 42001 certification means you can leverage powerful AI capabilities in your deal rooms with confidence that responsible management practices are independently certified and continuously maintained.

Ready to experience a virtual data room where AI innovation meets certified governance? Contact ShareVault to learn how ISO 42001-certified AI management protects your most sensitive transactions.

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Tags: ISO 42001 certificate, Sharevault

Dec 16 2025

A Simple 4-Step Path to ISO 42001 for SMBs

Category: AI,AI Governance,ISO 42001 — disc7 @ 9:49 am

A Simple 4-Step Path to ISO 42001 for SMBs

Practical AI Governance for Compliance, Risk, and Security Leaders

Artificial Intelligence is moving fast—but regulations, customer expectations, and board-level scrutiny are moving even faster. ISO/IEC 42001 gives organizations a structured way to govern AI responsibly, securely, and in alignment with laws like the EU AI Act.

For SMBs, the good news is this: ISO 42001 does not require massive AI programs or complex engineering changes. At its core, it follows a clear four-step process that compliance, risk, and security teams already understand.

Step 1: Define AI Scope and Governance Context

The first step is understanding where and how AI is used in your business. This includes internally developed models, third-party AI tools, SaaS platforms with embedded AI, and even automation driven by machine learning.

For SMBs, this step is about clarity—not perfection. You define:

  • What AI systems are in scope
  • Business objectives and constraints
  • Regulatory, contractual, and ethical expectations
  • Roles and accountability for AI decisions

This mirrors how ISO 27001 defines ISMS scope, making it familiar for security and compliance teams.

Step 2: Identify and Assess AI Risks

Once AI usage is defined, the focus shifts to risk identification and impact assessment. Unlike traditional cyber risk, AI introduces new concerns such as bias, model drift, lack of explainability, data misuse, and unintended outcomes.

In this step, organizations:

  • Identify AI-specific risks across the lifecycle
  • Evaluate business, legal, and security impact
  • Consider affected stakeholders (customers, employees, regulators)
  • Prioritize risks based on likelihood and severity

This step aligns closely with enterprise risk management and can be integrated into existing risk registers.

Step 3: Implement AI Controls and Lifecycle Management

With risks prioritized, the organization selects practical governance and security controls. ISO 42001 does not prescribe one-size-fits-all solutions—it focuses on proportional controls based on risk.

Typical activities include:

  • AI policies and acceptable use guidelines
  • Human oversight and approval checkpoints
  • Data governance and model documentation
  • Secure development and vendor due diligence
  • Change management for AI updates

For SMBs, this is about leveraging existing ISO 27001, SOC 2, or NIST-aligned controls and extending them to cover AI.

Step 4: Monitor, Audit, and Improve

AI governance is not a one-time exercise. The final step ensures continuous monitoring, review, and improvement as AI systems evolve.

This includes:

  • Ongoing performance and risk monitoring
  • Internal audits and management reviews
  • Incident handling and corrective actions
  • Readiness for certification or regulatory review

This step closes the loop and ensures AI governance stays aligned with business growth and regulatory change.

Why This Matters for SMBs

Regulators and customers are no longer asking if you use AI—they’re asking how you govern it. ISO 42001 provides a defensible, auditable framework that shows due diligence without slowing innovation.

How DISC InfoSec Can Help

DISC InfoSec helps SMBs implement ISO 42001 quickly, pragmatically, and cost-effectively—especially if you’re already aligned with ISO 27001, SOC 2, or NIST. We translate AI risk into business language, reuse what you already have, and guide you from scoping to certification readiness.

👉 Talk to DISC InfoSec to build AI governance that satisfies regulators, reassures customers, and supports safe AI adoption—without unnecessary complexity.

Tufte_iso42001_pdf

ISO-42k-four-steps-process-diagram-2025-12-16-002218Download

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Tags: 4-Step Path to ISO 42001

Dec 15 2025

How ISO 42001 Strengthens Alignment With the EU AI Act (Without Replacing Legal Compliance)

Category: AI,AI Governance,AI Guardrails,ISO 42001 — disc7 @ 11:16 am

— What ISO 42001 Is and Its Purpose
ISO 42001 is a new international standard for AI governance and management systems designed to help organizations systematically manage AI-related risks and regulatory requirements. Rather than acting as a simple checklist, it sets up an ongoing framework for defining obligations, understanding how AI systems are used, and establishing controls that fit an organization’s specific risk profile. This structure resembles other ISO management system standards (such as ISO 27001) but focuses on AI’s unique challenges.

— ISO 42001’s Role in Structured Governance
At its core, ISO 42001 helps organizations build consistent AI governance practices. It encourages comprehensive documentation, clear roles and responsibilities, and formalized oversight—essentials for accountable AI development and deployment. This structured approach aligns with the EU AI Act’s broader principles, which emphasize accountability, transparency, and risk-based management of AI systems.

— Documentation and Risk Management Synergies
Both ISO 42001 and the EU AI Act call for thorough risk assessments, lifecycle documentation, and ongoing monitoring of AI systems. Implementing ISO 42001 can make it easier to maintain records of design choices, testing results, performance evaluations, and risk controls, which supports regulatory reviews and audits. This not only creates a stronger compliance posture but also prepares organizations to respond with evidence if regulators request proof of due diligence.

— Complementary Ethical and Operational Practices
ISO 42001 embeds ethical principles—such as fairness, non-discrimination, and human oversight—into the organizational governance culture. These values closely match the normative goals of the EU AI Act, which seeks to prevent harm and bias from AI systems. By internalizing these principles at the management level, organizations can more coherently translate ethical obligations into operational policies and practices that regulators expect.

— Not a Legal Substitute for Compliance Obligations
Importantly, ISO 42001 is not a legal guarantee of EU AI Act compliance on its own. The standard remains voluntary and, as of now, is not formally harmonized under the AI Act, meaning certification does not automatically confer “presumption of conformity.” The Act includes highly specific requirements—such as risk class registration, mandated reporting timelines, and prohibitions on certain AI uses—that ISO 42001’s management-system focus does not directly satisfy. ISO 42001 provides the infrastructure for strong governance, but organizations must still execute legal compliance activities in parallel to meet the letter of the law.

— Practical Benefits Beyond Compliance
Even though it isn’t a standalone compliance passport, adopting ISO 42001 offers many practical benefits. It can streamline internal AI governance, improve audit readiness, support integration with other ISO standards (like security and quality), and enhance stakeholder confidence in AI practices. Organizations that embed ISO 42001 can reduce risk of missteps, build stronger evidence trails, and align cross-functional teams for both ethical practice and regulatory readiness.

My Opinion
ISO 42001 is a valuable foundation for AI governance and a strong enabler of EU AI Act compliance—but it should be treated as the starting point, not the finish line. It helps organizations build structured processes, risk awareness, and ethical controls that align with regulatory expectations. However, because the EU AI Act’s requirements are detailed and legally enforceable, organizations must still map ISO-level controls to specific Act obligations, maintain live evidence, and fulfill procedural legal demands beyond what ISO 42001 specifies. In practice, using ISO 42001 as a governance backbone plus tailored compliance activities is the most pragmatic and defensible approach.

Emerging Tools & Frameworks for AI Governance & Security Testing

Free ISO 42001 Compliance Checklist: Assess Your AI Governance Readiness in 10 Minutes

AI Governance Tools: Essential Infrastructure for Responsible AI

Bridging the AI Governance Gap: How to Assess Your Current Compliance Framework Against ISO 42001

ISO 27001 Certified? You’re Missing 47 AI Controls That Auditors Are Now Flagging

Understanding Your AI System’s Risk Level: A Guide to EU AI Act Compliance

Building an Effective AI Risk Assessment Process

ISO/IEC 42001: The New Blueprint for Trustworthy and Responsible AI Governance

AI Governance Gap Assessment tool

AI Governance Quick Audit

How ISO 42001 & ISO 27001 Overlap for AI: Lessons from a Security Breach

ISO 42001:2023 Control Gap Assessment – Your Roadmap to Responsible AI Governance

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Tags: AI Governance, ISO 42001

Dec 10 2025

ISO 42001 and the Business Imperative for AI Governance

Category: AI,AI Governance,Information Security,ISO 42001 — disc7 @ 12:45 pm

1. Regulatory Compliance Has Become a Minefield—With Real Penalties

Regulatory Compliance Has Become a Minefield—With Real Penalties

Organizations face an avalanche of overlapping AI regulations (EU AI Act, GDPR, HIPAA, SOX, state AI laws) with zero tolerance for non-compliance. The EU AI Act explicitly recognizes ISO 42001 as evidence of conformity—making certification the fastest path to regulatory defensibility. Without systematic AI governance, companies face six-figure fines, contract terminations, and regulatory scrutiny.

2. Vendor Questionnaires Are Killing Deals

Every enterprise RFP now includes AI governance questions. Procurement teams demand documented proof of bias mitigation, human oversight, and risk management frameworks. Companies without ISO 42001 or equivalent certification are being disqualified before technical evaluations even begin. Lost deals aren’t hypothetical—they’re happening every quarter.

3. Boards Demand AI Accountability—Security Teams Can’t Deliver Alone

C-suite executives face personal liability for AI failures. They’re demanding comprehensive AI risk management across 7 critical impact categories (safety, fundamental rights, legal compliance, reputational risk). But CISOs and compliance officers lack AI-specific expertise to build these frameworks from scratch. Generic security controls don’t address model drift, training data contamination, or algorithmic bias.

4. The “DIY Governance” Death Spiral

Organizations attempting in-house ISO 42001 implementation waste 12-18 months navigating 18 specific AI controls, conducting risk assessments across 42+ scenarios, establishing monitoring systems, and preparing for third-party audits. Most fail their first audit and restart at 70% budget overrun. They’re paying the certification cost twice—plus the opportunity cost of delayed revenue.

5. “Certification Theater” vs. Real Implementation—And They Can’t Tell the Difference

Companies can’t distinguish between consultants who’ve read the standard vs. those who’ve actually implemented and passed audits in production environments. They’re terrified of paying for theoretical frameworks that collapse under audit scrutiny. They need proven methodologies with documented success—not PowerPoint governance.

6. High-Risk Industry Requirements Are Non-Negotiable

Financial services (credit scoring, AML), healthcare (clinical decision support), and legal firms (judicial AI) face sector-specific AI regulations that generic consultants can’t address. They need consultants who understand granular compliance scenarios—not surface-level AI ethics training.

DISC Turning AI Governance Into Measurable Business Value

  • Compressed timelines (6-9 months )
  • First-audit pass rates (avoiding remediation costs)
  • Revenue protection (winning contracts that require certified AI governance)
  • Regulatory defensibility (documented evidence that satisfies auditors and regulators)
  • Pioneer-practitioner expertise (ShareVault implementation proves you’ve solved problems they’re facing)

DISC Infosec implementation experience transforms their consultant from “compliance consultant” to “business risk eliminator.”

AI Governance Gap Assessment tool

  1. 15 questions
  2. Instant maturity score 
  3. Detailed PDF report 
  4. Top 3 priority gaps

Click  below to open an AI Governance Gap Assessment in your browser or click the image on the left side to start assessment.

ai_governance_assessment-v1.5Download

Built by AI governance experts. Used by compliance leaders.

Dec 08 2025

Emerging Tools & Frameworks for AI Governance & Security Testing

Category: AI,AI Governance,AI Governance Tools,AI Guardrails,Information Security — disc7 @ 10:22 am

garak — LLM Vulnerability Scanner / Red-Teaming Kit

  • garak (Generative AI Red-teaming & Assessment Kit) is an open-source tool aimed specifically at testing Large Language Models and dialog systems for AI-specific vulnerabilities: prompt injection, jailbreaks, data leakage, hallucinations, toxicity, etc.
  • It supports many LLM sources: Hugging Face models, OpenAI APIs, AWS Bedrock, local ggml models, etc.
  • Typical usage is via command line, making it relatively easy to incorporate into a Linux/pen-test workflow.
  • For someone interested in “governance,” garak helps identify when an AI system violates safety, privacy or compliance expectations before deployment.

BlackIce — Containerized Toolkit for AI Red-Teaming & Security Testing

  • BlackIce is described as a standardized, containerized red-teaming toolkit for both LLMs and classical ML models. The idea is to lower the barrier to entry for AI security testing by packaging many tools into a reproducible Docker image.
  • It bundles a curated set of open-source tools (as of late 2025) for “Responsible AI and Security testing,” accessible via a unified CLI interface — akin to how Kali bundles network-security tools.
  • For governance purposes: BlackIce simplifies running comprehensive AI audits, red-teaming, and vulnerability assessments in a consistent, repeatable environment — useful for teams wanting to standardize AI governance practices.

LibVulnWatch — Supply-Chain & Library Risk Assessment for AI Projects

  • While not specific to LLM runtime security, LibVulnWatch focuses on evaluating open-source AI libraries (ML frameworks, inference engines, agent-orchestration tools) for security, licensing, supply-chain, maintenance and compliance risks.
  • It produces governance-aligned scores across multiple domains, helping organizations choose safer dependencies and keep track of underlying library health over time.
  • For an enterprise building or deploying AI: this kind of tool helps verify that your AI stack — not just the model — meets governance, audit, and risk standards.

Giskard (open-source / enterprise) — LLM Red-Teaming & Monitoring for Safety/Compliance

  • Giskard offers LLM vulnerability scanning and red-teaming capabilities (prompt injection, data leakage, unsafe behavior, bias, etc.) via both an open-source library and an enterprise “Hub” for production-grade systems.
  • It supports “black-box” testing: you don’t need internal access to the model — as long as you have an API or interface, you can run tests.
  • For AI governance, Giskard helps in evaluating compliance with safety, privacy, and fairness standards before and after deployment.

🔧 What This Means for Kali Linux / Pen-Test-Oriented Workflows

  • The emergence of tools like garak, BlackIce, and Giskard shows that AI governance and security testing are becoming just as “testable” as traditional network or system security. For people familiar with Kali’s penetration-testing ecosystem — this is a familiar, powerful shift.
  • Because they are Linux/CLI-friendly and containerizable (especially BlackIce), they can integrate neatly into security-audit pipelines, continuous-integration workflows, or red-team labs — making them practical beyond research or toy use.
  • Using a supply-chain-risk tool like LibVulnWatch alongside model-level scanners gives a more holistic governance posture: not just “Is this LLM safe?” but “Is the whole AI stack (dependencies, libraries, models) reliable and auditable?”

⚠️ A Few Important Caveats (What They Don’t Guarantee)

  • Tools like garak and Giskard attempt to find common issues (jailbreaks, prompt injection, data leakage, harmful outputs), but cannot guarantee absolute safety or compliance — because many risks (e.g. bias, regulatory compliance, ethics, “unknown unknowns”) depend heavily on context (data, environment, usage).
  • Governance is more than security: It includes legal compliance, privacy, fairness, ethics, documentation, human oversight — many of which go beyond automated testing.
  • AI-governance frameworks are still evolving; even red-teaming tools may lag behind novel threat types (e.g. multi-modality, chain-of-tool-calls, dynamic agentic behaviors).

🎯 My Take / Recommendation (If You Want to Build an AI-Governance Stack Now)

If I were you and building or auditing an AI system today, I’d combine these tools:

  • Start with garak or Giskard to scan model behavior for injection, toxicity, privacy leaks, etc.
  • Use BlackIce (in a container) for more comprehensive red-teaming including chaining tests, multi-tool or multi-agent flows, and reproducible audits.
  • Run LibVulnWatch on your library dependencies to catch supply-chain or licensing risks.
  • Complement that with manual reviews, documentation, human-in-the-loop audits and compliance checks (since automated tools only catch a subset of governance concerns).

🧠 AI Governance & Security Lab Stack (2024–2025)

1️⃣ LLM Vulnerability Scanning & Red-Teaming (Core Layer)

These are your “nmap + metasploit” equivalents for LLMs.

garak (NVIDIA)

  • Automated LLM red-teaming
  • Tests for jailbreaks, prompt injection, hallucinations, PII leaks, unsafe outputs
  • CLI-driven → perfect for Kali workflows
    ✅ Baseline requirement for AI audits

Giskard (Open Source / Enterprise)

  • Structured LLM vulnerability testing (multi-turn, RAG, tools)
  • Bias, reliability, hallucination, safety checks
    ✅ Strong governance reporting angle

promptfoo

  • Prompt, RAG, and agent testing framework
  • CI/CD friendly, regression testing
    ✅ Best for continuous governance

AutoRed

  • Automatically generates adversarial prompts (no seeds)
  • Excellent for discovering unknown failure modes
    ✅ Advanced red-team capability

RainbowPlus

  • Evolutionary adversarial testing (quality + diversity)
  • Better coverage than brute-force prompt testing
    ✅ Research-grade robustness testing

2️⃣ Benchmarks & Evaluation Frameworks (Evidence Layer)

These support objective governance claims.

HarmBench

  • Standardized harm/safety benchmark
  • Measures refusal correctness, bypass resistance
    ✅ Great for board-level reporting

OpenAI / Anthropic Safety Evals (Open Specs)

  • Industry-accepted evaluation criteria
    ✅ Aligns with regulator expectations

HELM / BIG-Bench (Selective usage)

  • Model behavior benchmarking
    ⚠️ Use carefully — not all metrics are governance-relevant

3️⃣ Prompt Injection & Agent Security (Runtime Protection)

This is where most AI systems fail in production.

LlamaFirewall

  • Runtime enforcement for tool-using agents
  • Prevents prompt injection, tool abuse, unsafe actions
    ✅ Critical for agentic AI

NeMo Guardrails

  • Rule-based and model-assisted controls
    ✅ Good for compliance-driven orgs

Rebuff

  • Prompt-injection detection & prevention
    ✅ Lightweight, practical defense

4️⃣ Infrastructure & Deployment Security (Kali-Adjacent)

This is often ignored — and auditors will catch it.

AI-Infra-Guard (Tencent)

  • Scans AI frameworks, MCP servers, model infra
  • Includes jailbreak testing + infra CVEs
    ✅ Closest thing to “Nessus for AI”

Trivy

  • Container + dependency scanning
    ✅ Use on AI pipelines and inference containers

Checkov

  • IaC scanning (Terraform, Kubernetes, cloud AI services)
    ✅ Cloud AI governance

5️⃣ Supply Chain & Model Provenance (Governance Backbone)

Auditors care deeply about this.

LibVulnWatch

  • AI/ML library risk scoring
  • Licensing, maintenance, vulnerability posture
    ✅ Perfect for vendor risk management

OpenSSF Scorecard

  • OSS project security maturity
    ✅ Mirror SBOM practices

Model Cards / Dataset Cards (Meta, Google standards)

  • Manual but essential
    ✅ Regulatory expectation

6️⃣ Data Governance & Privacy Risk

AI governance collapses without data controls.

Presidio

  • PII detection/anonymization
    ✅ GDPR, HIPAA alignment

Microsoft Responsible AI Toolbox

  • Error analysis, fairness, interpretability
    ✅ Human-impact governance

WhyLogs

  • Data drift & data quality monitoring
    ✅ Operational governance

7️⃣ Observability, Logging & Auditability

If it’s not logged, it doesn’t exist to auditors.

OpenTelemetry (LLM instrumentation)

  • Trace model prompts, outputs, tool calls
    ✅ Explainability + forensics

LangSmith / Helicone

  • LLM interaction logging
    ✅ Useful for post-incident reviews

8️⃣ Policy, Controls & Governance Mapping (Human Layer)

Tools don’t replace governance — they support it.

ISO/IEC 42001 Control Mapping

  • AI management system
    ✅ Enterprise governance standard

NIST AI RMF

  • Risk identification & mitigation
    ✅ US regulator alignment

DASF / AICM (AI control models)

  • Control-oriented governance
    ✅ vCISO-friendly frameworks

🔗 How This Fits into Kali Linux

Kali doesn’t yet ship AI governance tools by default — but:

  • ✅ Almost all of these run on Linux
  • ✅ Many are CLI-based or Dockerized
  • ✅ They integrate cleanly with red-team labs
  • ✅ You can easily build a custom Kali “AI Governance profile”

My recommendation:
Create:

  • A Docker compose stack for garak + Giskard + promptfoo
  • A CI pipeline for prompt & agent testing
  • A governance evidence pack (logs + scores + reports)

Map each tool to ISO 42001 / NIST AI RMF controls

below is a compact, actionable mapping that connects the ~10 tools we discussed to ISO/IEC 42001 clauses (high-level AI management system requirements) and to the NIST AI RMF Core functions (GOVERN / MAP / MEASURE / MANAGE).
I cite primary sources for the standards and each tool so you can follow up quickly.

Notes on how to read the table
• ISO 42001 — I map to the standard’s high-level clauses (Context (4), Leadership (5), Planning (6), Support (7), Operation (8), Performance evaluation (9), Improvement (10)). These are the right level for mapping tools into an AI Management System. Cloud Security Alliance+1
• NIST AI RMF — I use the Core functions: GOVERN / MAP / MEASURE / MANAGE (the AI RMF core and its intended outcomes). Tools often map to multiple functions. NIST Publications
• Each row: tool → primary ISO clauses it supports → primary NIST functions it helps with → short justification + source links.

Tool → ISO 42001 / NIST AI RMF mapping

1) Giskard (open-source + platform)

  • ISO 42001: 7 Support (competence, awareness, documented info), 8 Operation (controls, validation & testing), 9 Performance evaluation (testing/metrics). Cloud Security Alliance+1
  • NIST AI RMF: MEASURE (testing, metrics, evaluation), MAP (identify system behavior & risks), MANAGE (remediation actions). NIST Publications+1
  • Why: Giskard automates model testing (bias, hallucination, security checks) and produces evidence/metrics used in audits and continuous evaluation. GitHub

2) promptfoo (prompt & RAG test suite / CI integration)

  • ISO 42001: 7 Support (documented procedures, competence), 8 Operation (validation before deployment), 9 Performance evaluation (continuous testing). Cloud Security Alliance
  • NIST AI RMF: MEASURE (automated tests), MANAGE (CI/CD enforcement, remediation), MAP (describe prompt-level risks). GitHub+1
  • Why: promptfoo provides automated prompt tests, integrates into CI (pre-deployment gating) and produces test artifacts for governance traceability. GitHub+1

3) AI-Infra-Guard (Tencent A.I.G)

  • ISO 42001: 6 Planning (risk assessment), 7 Support (infrastructure), 8 Operation (secure deployment), 9 Performance evaluation (vulnerability scanning reports). Cloud Security Alliance+1
  • NIST AI RMF: MAP (asset & infrastructure risk mapping), MEASURE (vulnerability detection, CVE checks), MANAGE (remediation workflows). NIST Publications+1
  • Why: A.I.G scans AI infra, fingerprints components, and includes jailbreak evaluation — key for supply-chain and infra controls. GitHub

4) LlamaFirewall (runtime guardrail / agent monitor)

  • ISO 42001: 8 Operation (runtime controls / enforcement), 7 Support (monitoring tooling), 9 Performance evaluation (runtime monitoring metrics). Cloud Security Alliance+1
  • NIST AI RMF: MANAGE (runtime risk controls), MEASURE (monitoring & detection), MAP (runtime threat vectors). NIST Publications+1
  • Why: LlamaFirewall is explicitly designed as a last-line runtime guardrail for agentic systems — enforcing policies and detecting task-drift/prompt injection at runtime. arXiv

5) LibVulnWatch (supply-chain & lib risk assessment)

  • ISO 42001: 6 Planning (risk assessment), 7 Support (SBOMs, supplier controls), 8 Operation (secure build & deploy), 9 Performance evaluation (dependency health). Cloud Security Alliance+1
  • NIST AI RMF: MAP (supply-chain mapping & dependency inventory), MEASURE (vulnerability & license metrics), MANAGE (mitigation/prioritization). NIST Publications+1
  • Why: LibVulnWatch performs deep, evidence-backed evaluations of AI/ML libraries (CVEs, SBOM gaps, licensing) — directly supporting governance over the supply chain. arXiv+1

6) AutoRed / RainbowPlus (automated adversarial prompt generation & evolutionary red-teaming)

  • ISO 42001: 8 Operation (adversarial testing), 9 Performance evaluation (benchmarks & stress tests), 10 Improvement (feed results back to controls). Cloud Security Alliance
  • NIST AI RMF: MEASURE (adversarial performance metrics), MAP (expose attack surface), MANAGE (prioritize fixes based on attack impact). NIST Publications+2arXiv+2
  • Why: These tools expand coverage of red-team tests (free-form and evolutionary adversarial prompts), surfacing edge failures and jailbreaks that standard tests miss. arXiv+1

7) Meta SecAlign (safer model / model-level defenses)

  • ISO 42001: 8 Operation (safe model selection/deployment), 6 Planning (risk-aware model selection), 7 Support (model documentation). Cloud Security Alliance+1
  • NIST AI RMF: MAP (model risk characteristics), MANAGE (apply safer model choices / mitigations), MEASURE (evaluate defensive effectiveness). NIST Publications+1
  • Why: A “safer” model built to resist manipulation maps directly to operational and planning controls where the organization chooses lower-risk building blocks. arXiv

8) HarmBench (benchmarks for safety & robustness testing)

  • ISO 42001: 9 Performance evaluation (standardized benchmarks), 8 Operation (validation against benchmarks), 10 Improvement (continuous improvement from results). Cloud Security Alliance
  • NIST AI RMF: MEASURE (standardized metrics & benchmarks), MAP (compare risk exposure across models), MANAGE (feed measurement results into mitigation plans). NIST Publications
  • Why: Benchmarks are the canonical way to measure and compare model trustworthiness and to demonstrate compliance in audits. arXiv

9) Collections / “awesome” lists (ecosystem & resource aggregation)

  • ISO 42001: 5 Leadership & 7 Support (policy, competence, awareness — guidance & training resources). Cloud Security Alliance
  • NIST AI RMF: GOVERN (policy & stakeholder guidance), MAP (inventory of recommended tools & practices). NIST Publications
  • Why: Curated resources help leadership define policy, identify tools, and set organizational expectations — foundational for any AI management system. Cyberzoni.com

Quick recommendations for operationalizing the mapping

  1. Create a minimal mapping table inside your ISMS (ISO 42001) that records: tool name → ISO clause(s) it supports → NIST function(s) it maps to → artifact(s) produced (reports, SBOMs, test results). This yields audit-ready evidence. (ISO42001 + NIST suggestions above).
  2. Automate evidence collection: integrate promptfoo / Giskard into CI so that each deployment produces test artifacts (for ISO 42001 clause 9).
  3. Supply-chain checks: run LibVulnWatch and AI-Infra-Guard periodically to populate SBOMs and vulnerability dashboards (helpful for ISO 7 & 6).
  4. Runtime protections: embed LlamaFirewall or runtime monitors for agentic systems to satisfy operational guardrail requirements.
  5. Adversarial coverage: schedule periodic automated red-teaming using AutoRed / RainbowPlus / HarmBench to measure resilience and feed results into continual improvement (ISO clause 10).

Download 👇 AI Governance Tool Mapping

AI_Governance_Tools_ISO42001_NIST_AIRMF_MappingDownload

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At DISC InfoSec, our AI Governance services go beyond traditional security. We help organizations ensure legal compliance, privacy, fairness, ethics, proper documentation, and human oversight — addressing the full spectrum of responsible AI practices, many of which cannot be achieved through automated testing alone.

Tags: AI Governance, AI Governance & Security Testing

Dec 04 2025

What ISO 42001 Looks Like in Practice: Insights From Early Certifications

Category: AI,AI Governance,AI Guardrails,ISO 42001,vCISO — disc7 @ 8:59 am

What is ISO/IEC 42001:2023

  • ISO 42001 (published December 2023) is the first international standard dedicated to how organizations should govern and manage AI systems — whether they build AI, use it, or deploy it in services.
  • It lays out what the authors call an Artificial Intelligence Management System (AIMS) — a structured governance and management framework that helps companies reduce AI-related risks, build trust, and ensure responsible AI use.

Who can use it — and is it mandatory

  • Any organization — profit or non-profit, large or small, in any industry — that develops or uses AI can implement ISO 42001.
  • For now, ISO 42001 is not legally required. No country currently mandates it.
  • But adopting it proactively can make future compliance with emerging AI laws and regulations easier.

What ISO 42001 requires / how it works

  • The standard uses a “high-level structure” similar to other well-known frameworks (like ISO 27001), covering organizational context, leadership, planning, support, operations, performance evaluation, and continual improvement.
  • Organizations need to: define their AI-policy and scope; identify stakeholders and expectations; perform risk and impact assessments (on company level, user level, and societal level); implement controls to mitigate risks; maintain documentation and records; monitor, audit, and review the AI system regularly; and continuously improve.
  • As part of these requirements, there are 38 example controls (in the standard’s Annex A) that organizations can use to reduce various AI-related risks.

Why it matters

  • Because AI is powerful but also risky (wrong outputs, bias, privacy leaks, system failures, etc.), having a formal governance framework helps companies be more responsible and transparent when deploying AI.
  • For organizations that want to build trust with customers, regulators, or partners — or anticipate future AI-related regulations — ISO 42001 can serve as a credible, standardized foundation for AI governance.

My opinion

I think ISO 42001 is a valuable and timely step toward bringing some order and accountability into the rapidly evolving world of AI. Because AI is so flexible and can be used in many different contexts — some of them high-stakes — having a standard framework helps organizations think proactively about risk, ethics, transparency, and responsibility rather than scrambling reactively.

That said — because it’s new and not yet mandatory — its real-world impact depends heavily on how widely it’s adopted. For it to become meaningful beyond “nice to have,” regulators, governments, or large enterprises should encourage or require it (or similar frameworks). Until then, it will likely be adopted mostly by forward-thinking companies or those dealing with high-impact AI systems.

🔎 My view: ISO 42001 is a meaningful first step — but (for now) best seen as a foundation, not a silver bullet

I believe ISO 42001 represents a valuable starting point for bringing structure, accountability, and risk awareness to AI development and deployment. Its emphasis on governance, impact assessment, documentation, and continuous oversight is much needed in a world where AI adoption often runs faster than regulation or best practices.

That said — given its newness, generality, and the typical resource demands — I see it as necessary but not sufficient. It should be viewed as the base layer: useful for building internal discipline, preparing for regulatory demands, and signaling commitment. But to address real-world ethical, social, and technical challenges, organizations likely need additional safeguards — e.g. context-specific controls, ongoing audits, stakeholder engagement, domain-specific reviews, and perhaps even bespoke governance frameworks tailored to the type of AI system and its use cases.

In short: ISO 42001 is a strong first step — but real responsible AI requires going beyond standards to culture, context, and continuous vigilance.

✅ Real-world adopters of ISO 42001

IBM (Granite models)

  • IBM became “the first major open-source AI model developer to earn ISO 42001 certification,” for its “Granite” family of open-source language models.
  • The certification covers the management system for development, deployment, and maintenance of Granite — meaning IBM formalized policies, governance, data practices, documentation, and risk controls under AIMS (AI Management System).
  • According to IBM, the certification provides external assurance of transparency, security, and governance — helping enterprises confidently adopt Granite in sensitive contexts (e.g. regulated industries).

Infosys

  • Infosys — a global IT services and consulting company — announced in May 2024 that it had received ISO 42001:2023 certification for its AI Management System.
  • Their certified “AIMS framework” is part of a broader set of offerings (the “Topaz Responsible AI Suite”), which supports clients in building and deploying AI responsibly, with structured risk mitigations and accountability.
  • This demonstrates that even big consulting companies, not just pure-AI labs, see value in adopting ISO 42001 to manage AI at scale within enterprise services.

JAGGAER (Source-to-Pay / procurement software)

  • JAGGAER — a global player in procurement / “source-to-pay” software — announced that it achieved ISO 42001 certification for its AI Management System in June 2025.
  • For JAGGAER, the certification reflects a commitment to ethical, transparent, secure deployment of AI within its procurement platform.
  • This shows how ISO 42001 can be used not only by AI labs or consultancy firms, but by business-software companies integrating AI into domain-specific applications.

🧠 My take — promising first signals, but still early days

These early adopters make a strong case that ISO 42001 can work in practice across very different kinds of organizations — not just AI-native labs, but enterprises, service providers, even consulting firms. The variety and speed of adoption (multiple firms in 2024–2025) demonstrate real momentum.

At the same time — adoption appears selective, and for many companies, the process may involve minimal compliance effort rather than deep, ongoing governance. Because the standard and the ecosystem (auditors, best-practice references, peer case studies) are both still nascent, there’s a real risk that ISO 42001 becomes more of a “badge” than a strong guardrail.

In short: I see current adoptions as proof-of-concepts — promising early examples showing how ISO 42001 could become an industry baseline. But for it to truly deliver on safe, ethical, responsible AI at scale, we’ll need: more widespread adoption across sectors; shared transparency about governance practices; public reporting on outcomes; and maybe supplementary audits or domain-specific guidelines (especially for high-risk AI uses).

Most organizations think they’re ready for AI governance — until ISO/IEC 42001 shines a light on the gaps. With 47 new AI-specific controls, this standard is quickly becoming the global expectation for responsible and compliant AI deployment. To help teams get ahead, we built a free ISO 42001 Compliance Checklist that gives you a readiness score in under 10 minutes, plus a downloadable gap report you can share internally. It’s a fast way to validate where you stand today and what you’ll need to align with upcoming regulatory and customer requirements. If improving AI trust, risk posture, and audit readiness is on your roadmap, this tool will save your team hours.

https://blog.deurainfosec.com/free-iso-42001-compliance-checklist-assess-your-ai-governance-readiness-in-10-minutes/

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Tags: ISO 42001

Dec 03 2025

Why Auditing AI Is Critical for Responsible and Secure Adoption

Category: AI,AI Governance,Internal Audit — disc7 @ 1:51 pm

Managing AI Risks Through Strong Governance, Compliance, and Internal Audit Oversight

  1. Organizations are adopting AI at a rapid pace, and many are finding innovative ways to extract business value from these technologies. As AI capabilities expand, so do the risks that must be properly understood and managed.
  2. Internal audit teams are uniquely positioned to help organizations deploy AI responsibly. Their oversight ensures AI initiatives are evaluated with the same rigor applied to other critical business processes.
  3. By participating in AI governance committees, internal audit can help set standards, align stakeholders, and bring clarity to how AI is adopted across the enterprise.
  4. A key responsibility is identifying the specific risks associated with AI systems—whether ethical, technical, regulatory, or operational—and determining whether proper controls are in place to address them.
  5. Internal audit also plays a role in interpreting and monitoring evolving regulations. As governments introduce new AI-specific rules, companies must demonstrate compliance, and auditors help ensure they are prepared.
  6. Several indicators signal growing AI risk within an organization. One major warning sign is the absence of a formal AI risk management framework or any consistent evaluation of AI initiatives through a risk lens.
  7. Another risk indicator arises when new regulations create uncertainty about whether the company’s AI practices are compliant—raising concerns about gaps in oversight or readiness.
  8. Organizations without a clear AI strategy, or those operating multiple isolated AI projects, may fail to realize the intended benefits. Fragmentation often leads to inefficiencies and unmanaged risks.
  9. If AI initiatives continue without centralized governance, the organization may lose visibility into how AI is used, making it difficult to maintain accountability, consistency, and compliance.

Potential Impacts of Failing to Audit AI (Summary)

  • The organization may face regulatory violations, fines, or enforcement actions.
  • Biased or flawed AI outputs could damage the company’s reputation.
  • Operational disruptions may occur if AI systems fail or behave unpredictably.
  • Weak AI oversight can result in financial losses.
  • Unaddressed vulnerabilities in AI systems could lead to cybersecurity incidents.

My Opinion

Auditing AI is no longer optional—it is becoming a foundational part of digital governance. Without structured oversight, AI can expose organizations to reputational damage, operational failures, regulatory penalties, and security weaknesses. A strong AI audit function ensures transparency, accountability, and resilience. In my view, organizations that build mature AI auditing capabilities early will not only avoid risk but also gain a competitive edge by deploying trustworthy, well-governed AI at scale.

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Governance in The Age of Gen AI: A Director’s Handbook on Gen AI

Tags: AI Internal Audit

Dec 02 2025

Why Practical Reliability is the New Competitive Edge in AI

Category: AI,AI Governance — disc7 @ 1:47 pm

The Road to Enterprise AGI: Why Reliability Matters More Than Intelligence

1️⃣ Why Practical Reliability Matters

  • Many current AI systems — especially large language models (LLMs) and multimodal models — are non-deterministic: the same prompt can produce different outputs at different times.
  • For enterprises, non-determinism is a huge problem:
    • Compliance & auditability: Industries like finance, healthcare, and regulated manufacturing require traceable, reproducible decisions. An AI that gives inconsistent advice is essentially unusable in these contexts.
    • Risk management: If AI recommendations are unpredictable, companies can’t reliably integrate them into business-critical workflows.
    • Integration with existing systems: ERP, CRM, legal review systems, and automation pipelines need predictable outputs to function smoothly.

Murati’s research at Thinking Machines Lab directly addresses this. By working on deterministic inference pipelines, the goal is to ensure AI outputs are reproducible, reducing operational risk for enterprises. This moves generative AI from “experimental assistant” to a trusted tool. (a tool called Tinker that automates the creation of custom frontier AI models)

2️⃣ Enterprise Readiness

  • Security & Governance Integration: Enterprise adoption requires AI systems that comply with security policies, privacy standards, and governance rules. Murati emphasizes creating auditable, controllable AI.
  • Customization & Human Alignment: Businesses need AI that can be configured for specific workflows, tone, or operational rules — not generic “off-the-shelf” outputs. Thinking Machines Lab is focusing on human-aligned AI, meaning the system can be tailored while maintaining predictable behavior.
  • Operational Reliability: Enterprise-grade software demands high uptime, error handling, and predictable performance. Murati’s approach suggests that her AI systems are being designed with industrial-grade reliability, not just research demos.

3️⃣ The Competitive Edge

  • By tackling reproducibility and reliability at the inference level, her startup is positioning itself to serve companies that cannot tolerate “creative AI outputs” that are inconsistent or untraceable.
  • This is especially critical in sectors like:
    • Healthcare: AI-assisted diagnoses need predictable outputs.
    • Finance & Insurance: Risk modeling and automated compliance checks cannot fluctuate unpredictably.
    • Regulated Manufacturing & Energy: Decision-making and operational automation must be deterministic to meet safety standards.

Murati isn’t just building AI that “works,” she’s building AI that can be safely deployed in regulated, risk-sensitive environments. This aligns strongly with InfoSec, vCISO, and compliance priorities, because it makes AI audit-ready, predictable, and controllable — moving it from a curiosity or productivity tool to a reliable enterprise asset. In Short Building Trustworthy AGI: Determinism, Governance, and Real-World Readiness…

Murati’s Thinking Machines in Talks for $50 Billion Valuation

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Governance in The Age of Gen AI: A Director’s Handbook on Gen AI

Tags: AI Governance, Determinism, Deterministic AI, Murati, Thinking Machines Lab

Dec 02 2025

Governance & Security for AI Plug-Ins – vCISO Playbook

Category: AI Governance,Information Security,Security playbook,VCAIO,vCISO — disc7 @ 12:48 pm

In a recent report, researchers at Cato Networks revealed that the “Skills” plug‑in feature of Claude — the AI system developed by Anthropic — can be trivially abused to deploy ransomware.

The exploit involved taking a legitimate, open‑source plug‑in (a “GIF Creator” skill) and subtly modifying it: by inserting a seemingly harmless function that downloads and executes external code, the modified plug‑in can pull in a malicious script (in this case, ransomware) without triggering warnings.

When a user installs and approves such a skill, the plug‑in gains persistent permissions: it can read/write files, download further code, and open outbound connections, all without any additional prompts. That “single‑consent” permission model creates a dangerous consent gap.

In the demonstration by Cato Networks researcher Inga Cherny, they didn’t need deep technical skill — they simply edited the plug‑in, re-uploaded it, and once a single employee approved it, ransomware (specifically MedusaLocker) was deployed. Cherny emphasized that “anyone can do it — you don’t even have to write the code.”

Microsoft and other security watchers have observed that MedusaLocker belongs to a broader, active family of ransomware that has targeted numerous organizations globally, often via exploited vulnerabilities or weaponized tools.

This event marks a disturbing evolution in AI‑related cyber‑threats: attackers are moving beyond simple prompt‑based “jailbreaks” or phishing using generative AI — now they’re hijacking AI platforms themselves as delivery mechanisms for malware, turning automation tools into attack vectors.

It’s also a wake-up call for corporate IT and security teams. As more development teams adopt AI plug‑ins and automation workflows, there’s a growing risk that something as innocuous as a “productivity tool” could conceal a backdoor — and once installed, bypass all typical detection mechanisms under the guise of “trusted” software.

Finally, while the concept of AI‑driven attacks has been discussed for some time, this proof‑of-concept exploit shifts the threat from theoretical to real. It demonstrates how easily AI systems — even those with safety guardrails — can be subverted to perform malicious operations when trust is misplaced or oversight is lacking.

🧠 My Take

This incident highlights a fundamental challenge: as we embrace AI for convenience and automation, we must not forget that the same features enabling productivity can be twisted into attack vectors. The “single‑consent” permission model underlying many AI plug‑ins seems especially risky — once that trust is granted, there’s little transparency about what happens behind the scenes.

In my view, organizations using AI–enabled tools should treat them like any other critical piece of infrastructure: enforce code review, restrict who can approve plug‑ins, and maintain strict operational oversight. For people like you working in InfoSec and compliance — especially in small/medium businesses like wineries — this is a timely reminder: AI adoption must be accompanied by updated governance and threat models, not just productivity gains.

Below is a checklist of security‑best practices (for companies and vCISOs) to guard against misuse of AI plug‑ins — could be a useful to assess your current controls.

https://www.wired.com/story/the-era-of-ai-generated-ransomware-has-arrived

Safeguard organizational assets by managing risks associated with AI plug-ins (e.g., Claude Skills, GPT Tools, other automation plug-ins)

AI-Plug-In-Security-Best-Practices-for-RansomwareDownload

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Governance in The Age of Gen AI: A Director’s Handbook on Gen AI

Tags: AI Plug-Ins, vCISO

Dec 02 2025

Lawyers Can’t Delegate Accountability: The Coming AI Responsibility Reckoning

Category: AI,AI Governance — disc7 @ 10:44 am

  1. The legal profession is facing a pivotal turning point because AI tools — from document drafting and research to contract review and litigation strategy — are increasingly integrated into day-to-day legal work. The core question arises: when AI messes up, who is accountable? The author argues: the lawyer remains accountable.
  2. Courts and bar associations around the world are enforcing this principle strongly: they are issuing sanctions when attorneys submit AI-generated work that fabricates citations, invents case law, or misrepresents “AI-generated” arguments as legitimate.
  3. For example, in a 2023 case (Mata v. Avianca, Inc.), attorneys used an AI to generate research citing judicial opinions that didn’t exist. The court found this conduct inexcusable and imposed financial penalties on the lawyers.
  4. In another case from 2025 (Frankie Johnson v. Jefferson S. Dunn), lawyers filed motions containing entirely fabricated legal authority created by generative AI. The court’s reaction was far more severe: the attorneys received public reprimands, and their misconduct was referred for possible disciplinary proceedings — even though their firm avoided sanctions because it had institutional controls and AI-use policies in place.
  5. The article underlines that the shift to AI in legal work does not change the centuries-old principles of professional responsibility. Rules around competence, diligence, and confidentiality remain — but now lawyers must also acquire enough “AI literacy.” That doesn’t mean they must become ML engineers; but they should understand AI’s strengths and limits, know when to trust it, and when to independently verify its outputs.
  6. Regarding confidentiality, when lawyers use AI tools, they must assess the risk that client-sensitive data could be exposed — for example, accidentally included in AI training sets, or otherwise misused. Using free or public AI tools for confidential matters is especially risky.
  7. Transparency and client communication also become more important. Lawyers may need to disclose when AI is being used in the representation, what type of data is processed, and how use of AI might affect cost, work product, or confidentiality. Some forward-looking firms include AI-use policies upfront in engagement letters.
  8. On a firm-wide level, supervisory responsibilities still apply. Senior attorneys must ensure that any AI-assisted work by junior lawyers or staff meets professional standards. That includes establishing governance: AI-use policies, training, review protocols, oversight of external AI providers.
  9. Many larger law firms are already institutionalizing AI governance — setting up AI committees, defining layered review procedures (e.g. verifying AI-generated memos against primary sources, double-checking clauses, reviewing briefs for “hallucinations”).
  10. The article’s central message: AI may draft documents or assist in research, but the lawyer must answer. Technology can assist, but it cannot assume human professional responsibility. The “algorithm may draft — the lawyer is accountable.”

My Opinion

I think this article raises a crucial and timely point. As AI becomes more capable and tempting as a tool for legal work, the risk of over-reliance — or misuse — is real. The documented sanctions show that courts are no longer tolerant of unverified AI-generated content. This is especially relevant given the “black-box” nature of many AI models and their propensity to hallucinate plausible but false information.

For the legal profession to responsibly adopt AI, the guidelines described — AI literacy, confidentiality assessment, transparent client communication, layered review — aren’t optional luxuries; they’re imperative. In other words: AI can increase efficiency, but only under strict governance, oversight, and human responsibility.

Given my background in information security and compliance — and interest in building services around risk, governance and compliance — this paradigm resonates. It suggests that as AI proliferates (in law, security, compliance, consulting, etc.), there will be increasing demand for frameworks, policies, and oversight mechanisms ensuring trustworthy use. Designing such frameworks might even become a valuable niche service.

“The AI Accountability Reckoning: Why Lawyers Cannot Delegate Professional Responsibility to Algorithms” by Jean Gan — along with my opinion at the end.

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Governance in The Age of Gen AI: A Director’s Handbook on Gen AI

Tags: AI Accountability, AI Responsibility

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