Jan 10 2026

When Security Is Optional—Until It Isn’t

ISO/IEC 27001 is often described as “essential,” but in reality, it remains a voluntary standard rather than a mandatory requirement. Its value depends less on obligation and more on organizational intent.

When leadership genuinely understands how deeply the business relies on information, the importance of managing information risk becomes obvious. In such cases, adopting 27001 is simply a logical extension of good governance.

For informed management teams, information security is not a technical checkbox but a business enabler. They recognize that protecting data protects revenue, reputation, and operational continuity.

In these environments, frameworks like 27001 support disciplined decision-making, accountability, and long-term resilience. The standard provides structure, not bureaucracy.

However, when leadership does not grasp the organization’s information dependency, advocacy often falls on deaf ears. No amount of persuasion will compensate for a lack of awareness.

Pushing too hard in these situations can be counterproductive. Without perceived risk, security efforts are seen as cost, friction, or unnecessary compliance.

Sometimes, the most effective catalyst is experience rather than explanation. A near miss or a real incident often succeeds where presentations and risk registers fail.

Once the business feels tangible pain—financial loss, customer impact, or reputational damage—the conversation changes quickly. Security suddenly becomes urgent and relevant.

That is when security leaders are invited in as problem-solvers, not prophets—stepping forward to help stabilize, rebuild, and guide the organization toward stronger governance and risk management.

My opinion:

This perspective is pragmatic, realistic, and—while a bit cynical—largely accurate in how organizations actually behave.

In an ideal world, leadership would proactively invest in ISO 27001 because they understand information risk as a core business risk. In practice, many organizations only act when risk becomes experiential rather than theoretical. Until there is pain, security feels optional.

That said, waiting for an incident should never be the strategy—it’s simply the pattern we observe. Incidents are expensive teachers, and the damage often exceeds what proactive governance would have cost. From a maturity standpoint, reactive adoption signals weak risk leadership.

The real opportunity for security leaders and vCISOs is to translate information risk into business language before the crisis: revenue impact, downtime, legal exposure, and trust erosion. When that translation lands, 27001 stops being “optional” and becomes a management tool.

Ultimately, ISO 27001 is not about compliance—it’s about decision quality. Organizations that adopt it early tend to be deliberate, resilient, and better governed. Those that adopt it after an incident are often doing damage control.

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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: iso 27001, Real Risk


Jan 09 2026

AI Can Help Our Health — But at What Cost to Privacy?

Category: AI,AI Governance,Information Securitydisc7 @ 8:34 am

Potential risks of sharing medical records with a consumer AI platform


  1. OpenAI recently introduced “ChatGPT Health,” a specialized extension of ChatGPT designed to handle health-related conversations and enable users to link their medical records and wellness apps for more personalized insights. The company says this builds on its existing security framework.
  2. According to OpenAI, the new health feature includes “additional, layered protections” tailored to sensitive medical information — such as purpose-built encryption and data isolation that aims to separate health data from other chatbot interactions.
  3. The company also claims that data shared in ChatGPT Health won’t be used to train its broader AI models, a move intended to keep medical information out of the core model’s training dataset.
  4. OpenAI says millions of users widely ask health and wellness questions on its platform already, which it uses to justify a dedicated space where those interactions can be more contextualized and, allegedly, safer.
  5. Privacy advocates, however, are raising serious concerns. They note that medical records uploaded to ChatGPT Health are no longer protected by HIPAA, the U.S. law that governs how healthcare providers safeguard patients’ private health information.
  6. Experts like Sara Geoghegan from the Electronic Privacy Information Center warn that releasing sensitive health data into OpenAI’s systems removes legal privacy protections and exposes users to risk. Without a law like HIPAA applying to ChatGPT, the company’s own policies are the only thing standing between users and potential misuse.
  7. Critics also caution that OpenAI’s evolving business model, particularly if it expands into personalization or advertising, could create incentives to use health data in ways users don’t expect or fully understand.
  8. Key questions remain unanswered, such as how exactly the company would respond to law enforcement requests for health data and how effectively health data is truly isolated from other systems if policies change.
  9. The feature’s reliance on connected wellness apps and external partners also introduces additional vectors where sensitive information could potentially be exposed or accessed if there’s a breach or policy change.
  10. In summary, while OpenAI pitches ChatGPT Health as an innovation with enhanced safeguards, privacy advocates argue that without robust legal protections and clear transparency, sharing medical records with a consumer AI platform remains risky.


My Opinion

AI has immense potential to augment how people manage and understand their health, especially for non-urgent questions or preparing for doctor visits. But giving any tech company access to medical records without the backing of strong legal protections like HIPAA feels premature and potentially unsafe. Technical safeguards such as encryption and data isolation matter — but they don’t replace enforceable privacy laws that restrict how health data can be used, shared, or disclosed. In healthcare, trust and accountability are paramount, and without those, even well-intentioned tools can expose individuals to privacy risks or misuse of deeply personal information. Until regulatory frameworks evolve to explicitly protect AI-mediated health data, users should proceed with caution and understand the privacy trade-offs they’re making.

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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: AI Health, ChatGPT Health, privacy concerns


Jan 07 2026

Agentic AI: Why Autonomous Systems Redefine Enterprise Risk

Category: AI,AI Governance,Information Securitydisc7 @ 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 06 2026

The Best Cybersecurity Investment Strategy: Balance Fast Wins with Long-Term Resilience

Cybersecurity Investment Strategy: Investing With Intent

One of the most common mistakes organizations make in cybersecurity is investing in tools and controls without a clear, outcome-driven strategy. Not every security investment delivers value at the same speed, and not all controls produce the same long-term impact. Without prioritization, teams often overspend on complex solutions while leaving foundational gaps exposed.

A smarter approach is to align security initiatives based on investment level versus time to results. This is where a Cybersecurity Investment Strategy Matrix becomes valuable—it helps leaders visualize which initiatives deliver immediate risk reduction and which ones compound value over time. The goal is to focus resources on what truly moves the needle for the business.

Some initiatives provide fast results but require higher investment. Capabilities like EDR/XDR, SIEM and SOAR platforms, incident response readiness, and next-generation firewalls can rapidly improve detection and response. These are often critical for organizations facing active threats or regulatory pressure, but they demand both financial and operational commitment.

Other controls deliver fast results with relatively low investment. Measures such as multi-factor authentication, password managers, security baselines, and basic network segmentation quickly reduce attack surface and prevent common breaches. These are often high-impact, low-friction wins that should be implemented early.

Certain investments are designed for long-term payoff and require significant investment. Zero Trust architectures, DevSecOps, CSPM, identity governance, and DLP programs strengthen security at scale and maturity, but they take time, cultural change, and sustained funding to deliver full value.

Finally, there are long-term, low-investment initiatives that quietly build resilience over time. Security awareness training, vulnerability management, penetration testing, security champions programs, strong documentation, meaningful KPIs, and open-source security tools all improve security posture steadily without heavy upfront costs.

A well-designed cybersecurity investment strategy balances quick wins with long-term capability building. The real question for leadership isn’t what tools to buy, but which three initiatives, if prioritized today, would reduce the most risk and support the business right now.

My opinion: the best cybersecurity investment strategy is a balanced, risk-driven mix of fast wins and long-term foundations, not an “all-in” bet on any single quadrant.

Here’s why:

  1. Start with fast results / low investment (mandatory baseline)
    This should be non-negotiable. Controls like MFA, security baselines, password managers, and basic network segmentation deliver immediate risk reduction at minimal cost. Skipping these while investing in advanced tools is one of the most common (and expensive) mistakes I see.
  2. Add 1–2 fast results / high investment controls (situational)
    Once the basics are in place, selectively invest in high-impact capabilities like EDR/XDR or incident response readiness—only if your threat profile, regulatory exposure, or business criticality justifies it. These tools are powerful, but without maturity, they become noisy and underutilized.
  3. Continuously build long-term / low investment foundations (quiet multiplier)
    Security awareness, vulnerability management, documentation, and KPIs don’t look flashy, but they compound over time. These initiatives increase ROI on every other control you deploy and reduce operational friction.
  4. Delay long-term / high investment initiatives until maturity exists
    Zero Trust, DevSecOps, DLP, and identity governance are excellent goals—but pursuing them too early often leads to stalled programs and wasted spend. These work best when governance, ownership, and basic hygiene are already solid.

Bottom line:
The best strategy is baseline first → targeted protection next → long-term maturity in parallel.
If I had to simplify it:

Fix what attackers exploit most today, while quietly building the capabilities that prevent tomorrow’s failures.

This approach aligns security spend with business risk, avoids tool sprawl, and delivers both immediate and sustained value.

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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: Cybersecurity Investment Strategy


Jan 03 2026

8 Practical Cybersecurity Steps Every Small Business Can Take Today

Category: cyber security,Information Securitydisc7 @ 11:47 am


Many small and medium businesses are attractive targets for cybercriminals because they hold valuable data and often lack extensive IT resources. Threats like ransomware, phishing and business email compromise can disrupt operations, damage reputation, and cause financial loss. Recognizing that no business is too small to be targeted is the first step toward protection.

1. Teach employees to recognize and report phishing attacks. Phishing is one of the primary ways attackers gain access. Regular awareness training helps staff spot suspicious emails, links, and requests, reducing the chance that a click triggers a breach.

2. Require strong passwords across your organization. Weak or reused passwords are easily guessed or brute-forced. Establish a strong password policy and consider tools like password managers so employees can securely store unique credentials.

3. Implement multifactor authentication (MFA). Adding MFA means users must provide more than just a password to access accounts. This extra layer of verification dramatically reduces the odds that attackers can impersonate employees, even if they obtain a password.

4. Keep systems and software up to date. Outdated software often contains known security flaws that attackers exploit. Having regular patching schedules and enabling automatic updates wherever possible keeps your systems protected against many common vulnerabilities.

5. Enable logging and monitoring. Logging system activity gives you visibility into what’s happening on your network. Monitoring logs helps detect suspicious behavior early, so you can respond before an incident becomes a major breach.

6. Back up your business data regularly. Ransomware and other failures can cripple operations if you can’t access critical files. Maintain backups following a reliable strategy—such as the 3-2-1 rule—to ensure you can restore data quickly and resume business functions.

7. Encrypt sensitive data and devices. Encryption transforms your data into unreadable code for anyone without access keys. Applying encryption to data at rest and in transit helps protect information even if a device is lost or a system is compromised.

8. Report cyber incidents and share threat information. If an incident occurs, reporting it to agencies like CISA helps the broader business community stay informed about emerging threats and may provide access to additional guidance or alerts.


Taken together, these steps create a practical cybersecurity foundation for your business. Start with basics like employee training and MFA, then build up to backups, encryption, and incident reporting to strengthen your resiliency against evolving threats.

Source: You Can Protect Your Business from Online Threats (CISA)

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Tags: Cybersecurity for SMBs


Dec 30 2025

From Regulation to Revenue: The Power of Strong Security Compliance

Category: Information Securitydisc7 @ 8:15 am


Compliance today isn’t just about checking boxes — it’s directly tied to business survival and stakeholder trust.

Organizations now face intense scrutiny from clients, regulators, and supply chain partners. With reputations and revenue on the line, getting compliance right the first time is essential.

DISC InfoSec has been leading that mission since 2002, supporting businesses across industries in achieving and sustaining certification.

Our team includes seasoned specialists with over 20 years of practical experience in security and compliance.

We specialize in multi-framework strategies — including ISO 27001, ISO 42001, GDPR, SOC 2, PCI, and HIPAA — allowing companies to streamline efforts and reduce operational costs.

AI is rapidly reshaping how organizations operate—but without strong oversight, it introduces serious regulatory, ethical, and operational challenges.

ISO 42001 delivers a structured governance framework to ensure AI is developed and used responsibly. It focuses on key safeguards such as bias mitigation, transparency, accountability, and ongoing performance monitoring—especially vital for high-risk sectors like defense, healthcare, and finance.

100% Certification Success: Why Businesses Trust DISC InfoSec

This approach is why we have a 100% client certification success rate with zero exceptions. Every organization we support passes.

From global enterprises to early-stage innovators, we help build security programs that protect contracts, strengthen customer confidence, and ultimately fuel business growth.

When the stakes are high and compliance is mission-critical, you deserve a partner who delivers results — every time.


Partner with DISC InfoSec to secure your compliance roadmap and safeguard your business advantage.
📩 Contact: Info@DeuraInfoSec.com
🔐 www.DeuraInfoSec.com


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Dec 29 2025

12 Pillars of Cybersecurity

Category: cyber security,Information Securitydisc7 @ 9:56 am


12 Pillars of Cybersecurity — Simplified Overview — Start by getting the basics right — it’s the foundation of every effective security program.

1️⃣ Disaster Recovery
Disaster Recovery ensures organizations can quickly restore systems and data after a disruptive event such as ransomware, hardware failure, or natural disasters. A well-designed plan includes data backups, documented recovery procedures, and resilience testing so the business can continue operating with minimal downtime.

2️⃣ Authentication
Authentication verifies that users are who they claim to be. Strong password policies, secure login controls, and multifactor authentication (MFA) help prevent unauthorized access to critical systems, reducing the risk of credential theft and account compromise.

3️⃣ Authorization
Authorization determines what authenticated users are allowed to do. Properly managed access roles and least-privilege principles ensure individuals only access the information needed for their job, minimizing internal misuse and breach exposure.

4️⃣ Encryption
Encryption protects sensitive data by making it unreadable to unauthorized entities. Whether data is stored or in transit, encryption standards like TLS help maintain confidentiality and integrity, even if attackers intercept it.

5️⃣ Vulnerability Management
This includes identifying weaknesses in applications, systems, or configurations before attackers exploit them. Regular scanning, patching, and proactive remediation are essential to stay ahead of constantly emerging threats.

6️⃣ Audit & Compliance
Audit and compliance confirm that cybersecurity controls meet legal, industry, and internal requirements. Through continuous monitoring, reporting, and assessments, organizations strengthen governance and reduce regulatory risk.

7️⃣ Network Security
Network security protects communication flowing between devices and systems. Firewalls, intrusion detection, segmentation, and DNS security reduce unauthorized access and lateral movement inside the network.

8️⃣ Terminal (Endpoint) Security
Endpoints—like laptops, servers, and mobile devices—must be protected from malware and misuse. Tools such as EDR (Endpoint Detection & Response), encryption, and device control help secure data where employees work every day.

9️⃣ Emergency Response
Incident Response and business continuity actions are triggered when a cyberattack occurs. Quick detection, containment, and communication limit damage and accelerate recovery while maintaining stakeholder trust.

🔟 Container Security
Containerized workloads, used heavily in cloud environments, require specialized protections. Securing container images, runtime behavior, and orchestration platforms prevents vulnerabilities from spreading rapidly across applications.

1️⃣1️⃣ API Security
APIs are now core to digital integrations, making them a prime target for attackers. Secure authentication, encryption, rate limiting, and runtime monitoring protect data shared between systems and prevent unauthorized access.

1️⃣2️⃣ Third-Party / Vendor Management
Vendors introduce additional risk since their systems may connect to yours. Risk assessments, clear security expectations, and continuous monitoring help ensure third-party access doesn’t become the weakest link.


⭐ Expert Opinion

These 12 pillars offer a strong foundational framework — but cybersecurity only works when measurements, monitoring, and automation continuously improve these controls. With attackers advancing faster every year, organizations must treat cybersecurity as an adaptable lifecycle, not a one-time checklist. Prioritized risk-based implementation and skilled oversight remain the keys to real cyber resilience.


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Dec 12 2025

When a $3K “cybersecurity gap assessment” reveals you don’t actually have cybersecurity to assess…

Category: Information Security,ISO 27k,vCISOdisc7 @ 8:51 am

When a $3K “cybersecurity gap assessment” reveals you don’t actually have cybersecurity to assess…

A prospect just reached out wanting to pay me $3,000 to assess their ISO 27001 readiness.

Here’s how that conversation went:

Me: “Can you share your security policies and procedures?” Them: “We don’t have any.”

Me: “How about your latest penetration test, vulnerability scans, or cloud security assessments?” Them: “Nothing.”

Me: “What about your asset inventory, vendor register, or risk assessments?” Them: “We haven’t done those.”

Me: “Have you conducted any vendor security due diligence or data privacy reviews?” Them: “No.”

Me: “Let’s try HR—employee contracts, job descriptions, onboarding/offboarding procedures?” Them: “It’s all ad hoc. Nothing formal.”


Here’s the problem: You can’t assess what doesn’t exist.

It’s like subscribing to a maintenance plan for an appliance you don’t own yet

The reality? Many organizations confuse “having IT systems” with “having cybersecurity.” They’re running business-critical operations with zero security foundation—no documentation, no testing, no governance.

What they actually need isn’t an assessment. It’s a security program built from the ground up.

ISO 27001 compliance isn’t a checkbox exercise. It requires: âś“ Documented policies and risk management processes âś“ Regular security testing and validation âś“ Asset and vendor management frameworks âś“ HR security controls and awareness training

If you’re in this situation, here’s my advice: Don’t waste money on assessments. Invest in building foundational security controls first. Then assess.

What’s your take? Have you encountered organizations confusing security assessment with security implementation?

#CyberSecurity #ISO27001 #InfoSec #RiskManagement #ISMS

DISC InfoSec blog post on ISO 27k

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Get in touch if you want a thorough evaluation of how your environment aligns with ISO 27001 or ISO 42001 requirements.

Tags: iso 27001, ISO 27001 gap assessment


Dec 10 2025

ISO 42001 and the Business Imperative for AI Governance

Category: AI,AI Governance,Information Security,ISO 42001disc7 @ 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

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

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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 08 2025

Why Security Consultants Rely on Burp Suite Professional for Web App Assessments

Here are some of the main benefits of using Burp Suite Professional — specifically from the perspective of a professional services consultant doing security assessments, penetration testing, or audits for clients. I highlight where Burp Pro gives real value in a professional consulting context.

✅ Why consultants often prefer Burp Suite Professional

  • Comprehensive, all-in-one toolkit for web-app testing
    Burp Pro bundles proxying, crawling/spidering, vulnerability scanning, request replay/manipulation, fuzzing/brute forcing, token/sequence analysis, and more — all in a single product. This lets a consultant perform full-scope web application assessments without needing to stitch together many standalone tools.
  • Automated scanning + manual testing — balanced for real-world audits
    As a consultant you often need to combine speed (to scan large or complex applications) and depth (to manually investigate subtle issues or business-logic flaws). Burp Pro’s automated scanner quickly highlights many common flaws (e.g. SQLi, XSS, insecure configs), while its manual tools (proxy, repeater, intruder, etc.) allow fine-grained verification and advanced exploitation.
  • Discovery of “hidden” or non-obvious issues / attack surfaces
    The crawler/spider + discovery features help map out a target application’s entire attack surface — including hidden endpoints, unlinked pages or API endpoints — which consultants need to find when doing thorough security reviews.
  • Flexibility for complex or modern web apps (APIs, SPAs, WebSockets, etc.)
    Many modern applications use single-page frameworks, APIs, WebSockets, token-based auth, etc. Burp Pro supports testing these complex setups (e.g. handling HTTPS, WebSockets, JSON APIs), enabling consultants to operate effectively even on modern, dynamic web applications.
  • Extensibility and custom workflows tailored to client needs
    Through the built-in extension store (the “BApp Store”), and via scripting/custom plugins, consultants can customize Burp Pro to fit the unique architecture or threat model of a client’s environment — which is crucial in professional consulting where every client is different.
  • Professional-grade reporting & audit deliverables
    Consultants often need to deliver clear, structured, prioritized vulnerability reports to clients or stakeholders. Burp Pro supports detailed reporting, with evidence, severity, context — making it easier to communicate findings and remediation steps.
  • Efficiency and productivity: saves time and resources
    By automating large parts of scanning and combining multiple tools in one, Burp Pro helps consultants complete engagements faster — freeing time for deeper manual analysis, more clients, or more thorough work.
  • Up-to-date detection logic and community / vendor support
    As new web-app vulnerabilities and attack vectors emerge, Burp Pro (supported by its vendor and community) gets updates and new detection logic — which helps consultants stay current and offer reliable security assessments.

🚨 React2Shell detection is now available in Burp Suite Professional & Burp Suite DAST.

The critical React/Next.js vulnerability (CVE-2025-55182 / 66478) is circulating fast. You can already detect

🎯 What this enables in a Consulting / Professional Services Context

Using Burp Suite Professional allows a consultant to:

  • Provide comprehensive security audits covering a broad attack surface — from standard web pages to APIs, dynamic front-ends, and even modern client-side logic.
  • Combine fast automated scanning with deep manual review, giving confidence that both common and subtle or business-logic vulnerabilities are identified.
  • Deliver clear, actionable reports and remediation guidance — a must when working with clients or stakeholders who need to understand risk and prioritize fixes.
  • Adapt quickly to different client environments — thanks to extensions, custom workflows, and configurability.
  • Scale testing work: for example, map and scan large applications efficiently, then focus consultant time on validating and exploiting deeper issues rather than chasing basic ones.
  • Maintain a professional standard of work — many clients expect usage of recognized tools, reproducible evidence, and thorough testing, all of which Burp Pro supports.

✅ Summary — Pro version pays off in consulting work

For a security consultant, Burp Suite Professional isn’t just a “nice to have” — it often becomes a core piece of the toolset. Its mix of automation, manual flexibility, extensibility, and reporting makes it highly suitable for professional-grade penetration testing, audits, and security assessments. While there are other tools out there, the breadth and polish of Burp Pro tends to make it “default standard” in many consulting engagements.

At DISC InfoSec, we provide comprehensive security audits that cover your entire digital attack surface — from standard web pages to APIs, dynamic front-ends, and even modern client-side logic. Our expert team not only identifies vulnerabilities but also delivers a tailored mitigation plan designed to reduce risks and provide assurance against potential security incidents. With DISC InfoSec, you gain the confidence that your applications and data are protected, while staying ahead of emerging threats.

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Tags: BURP Pro, Burp Suite Professional, DISC InfoSec, React2Shell


Dec 05 2025

Are AI Companies Protecting Humanity? The Latest Scorecard Says No

The article reports on a new “safety report card” assessing how well leading AI companies are doing at protecting humanity from the risks posed by powerful artificial-intelligence systems. The report was issued by Future of Life Institute (FLI), a nonprofit that studies existential threats and promotes safe development of emerging technologies.

This “AI Safety Index” grades companies based on 35 indicators across six domains — including existential safety, risk assessment, information sharing, governance, safety frameworks, and current harms.

In the latest (Winter 2025) edition of the index, no company scored higher than a “C+.” The top-scoring companies were Anthropic and OpenAI, followed by Google DeepMind.

Other firms, including xAI, Meta, and a few Chinese AI companies, scored D or worse.

A key finding is that all evaluated companies scored poorly on “existential safety” — which covers whether they have credible strategies, internal monitoring, and controls to prevent catastrophic misuse or loss of control as AI becomes more powerful.

Even though companies like OpenAI and Google DeepMind say they’re committed to safety — citing internal research, safeguards, testing with external experts, and safety frameworks — the report argues that public information and evidence remain insufficient to demonstrate real readiness for worst-case scenarios.

For firms such as xAI and Meta, the report highlights a near-total lack of evidence about concrete safety investments beyond minimal risk-management frameworks. Some companies didn’t respond to requests for comment.

The authors of the index — a panel of eight independent AI experts including academics and heads of AI-related organizations — emphasize that we’re facing an industry that remains largely unregulated in the U.S. They warn this “race to the bottom” dynamic discourages companies from prioritizing safety when profitability and market leadership are at stake.

The report suggests that binding safety standards — not voluntary commitments — may be necessary to ensure companies take meaningful action before more powerful AI systems become a reality.

The broader context: as AI systems play larger roles in society, their misuse becomes more plausible — from facilitating cyberattacks, enabling harmful automation, to even posing existential threats if misaligned superintelligent AI were ever developed.

In short: according to the index, the AI industry still has a long way to go before it can be considered truly “safe for humanity,” even among its most prominent players.


My Opinion

I find the results of this report deeply concerning — but not surprising. The fact that even the top-ranked firms only get a “C+” strongly suggests that current AI safety efforts are more symbolic than sufficient. It seems like companies are investing in safety only at a surface level (e.g., statements, frameworks), but there’s little evidence they are preparing in a robust, transparent, and enforceable way for the profound risks AI could pose — especially when it comes to existential threats or catastrophic misuse.

The notion that an industry with such powerful long-term implications remains essentially unregulated feels reckless. Voluntary commitments and internal policies can easily be overridden by competitive pressure or short-term financial incentives. Without external oversight and binding standards, there’s no guarantee safety will win out over speed or profits.

That said, the fact that the FLI even produces this index — and that two firms get a “C+” — shows some awareness and effort towards safety. It’s better than nothing. But awareness must translate into real action: rigorous third-party audits, transparent safety testing, formal safety requirements, and — potentially — regulation.

In the end, I believe society should treat AI much like we treat high-stakes technologies such as nuclear power: with caution, transparency, and enforceable safety norms. It’s not enough to say “we care about safety”; firms must prove they can manage the long-term consequences, and governments and civil society need to hold them accountable.

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Tags: AI Safety, AI Scorecard


Dec 02 2025

Governance & Security for AI Plug-Ins – vCISO Playbook

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)

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

Tags: AI Plug-Ins, vCISO


Dec 01 2025

ISO 42001 + ISO 27001: Unified Governance for Secure and Responsible AI

Category: AI Governance,Information Security,ISO 27k,ISO 42001disc7 @ 2:35 pm

AIMS to ISMS

As organizations increasingly adopt AI technologies, integrating an Artificial Intelligence Management System (AIMS) into an existing Information Security Management System (ISMS) is becoming essential. This approach aligns with ISO/IEC 42001:2023 and ensures that AI risks, governance needs, and operational controls blend seamlessly with current security frameworks.

The document emphasizes that AI is no longer an isolated technology—its rapid integration into business processes demands a unified framework. Adding AIMS on top of ISMS avoids siloed governance and ensures structured oversight over AI-driven tools, models, and decision workflows.

Integration also allows organizations to build upon the controls, policies, and structures they already have under ISO 27001. Instead of starting from scratch, they can extend their risk management, asset inventories, and governance processes to include AI systems. This reduces duplication and minimizes operational disruption.

To begin integration, organizations should first define the scope of AIMS within the ISMS. This includes identifying all AI components—LLMs, ML models, analytics engines—and understanding which teams use or develop them. Mapping interactions between AI systems and existing assets ensures clarity and complete coverage.

Risk assessments should be expanded to include AI-specific threats such as bias, adversarial attacks, model poisoning, data leakage, and unauthorized “Shadow AI.” Existing ISO 27005 or NIST RMF processes can simply be extended with AI-focused threat vectors, ensuring a smooth transition into AIMS-aligned assessments.

Policies and procedures must be updated to reflect AI governance requirements. Examples include adding AI-related rules to acceptable use policies, tagging training datasets in data classification, evaluating AI vendors under third-party risk management, and incorporating model versioning into change controls. Creating an overarching AI Governance Policy helps tie everything together.

Governance structures should evolve to include AI-specific roles such as AI Product Owners, Model Risk Managers, and Ethics Reviewers. Adding data scientists, engineers, legal, and compliance professionals to ISMS committees creates a multidisciplinary approach and ensures AI oversight is not handled in isolation.

AI models must be treated as formal assets in the organization. This means documenting ownership, purpose, limitations, training datasets, version history, and lifecycle management. Managing these through existing ISMS change-management processes ensures consistent governance over model updates, retraining, and decommissioning.

Internal audits must include AI controls. This involves reviewing model approval workflows, bias-testing documentation, dataset protection, and the identification of Shadow AI usage. AI-focused audits should be added to the existing ISMS schedule to avoid creating parallel or redundant review structures.

Training and awareness programs should be expanded to cover topics like responsible AI use, prompt safety, bias, fairness, and data leakage risks. Practical scenarios—such as whether sensitive information can be entered into public AI tools—help employees make responsible decisions. This ensures AI becomes part of everyday security culture.


Expert Opinion (AI Governance / ISO Perspective)

Integrating AIMS into ISMS is not just efficient—it’s the only logical path forward. Organizations that already operate under ISO 27001 can rapidly mature their AI governance by extending existing controls instead of building a separate framework. This reduces audit fatigue, strengthens trust with regulators and customers, and ensures AI is deployed responsibly and securely. ISO 42001 and ISO 27001 complement each other exceptionally well, and organizations that integrate early will be far better positioned to manage both the opportunities and the risks of rapidly advancing AI technologies.

10-page ISO 42001 + ISO 27001 AI Risk Scorecard PDF

The 47 AI specific Controls You’re Missing…

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Tags: AIMS, isms


Nov 20 2025

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

🚨 If you’re ISO 27001 certified and using AI, you have 47 control gaps.

And auditors are starting to notice.

Here’s what’s happening right now:

→ SOC 2 auditors asking “How do you manage AI model risk?” (no documented answer = finding)

→ Enterprise customers adding AI governance sections to vendor questionnaires

→ EU AI Act enforcement starting in 2025 → Cyber insurance excluding AI incidents without documented controls

ISO 27001 covers information security. But if you’re using:

  • Customer-facing chatbots
  • Predictive analytics
  • Automated decision-making
  • Even GitHub Copilot

You need 47 additional AI-specific controls that ISO 27001 doesn’t address.

I’ve mapped all 47 controls across 7 critical areas: âś“ AI System Lifecycle Management âś“ Data Governance for AI âś“ Model Risk & Testing âś“ Transparency & Explainability âś“ Human Oversight & Accountability âś“ Third-Party AI Management
âś“ AI Incident Response

Full comparison guide → iso_comparison_guide

#AIGovernance #ISO42001 #ISO27001 #SOC2 #Compliance

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Tags: AI controls, ISo 27001 Certified


Nov 15 2025

Security Isn’t Important… Until It Is

Category: CISO,Information Security,Security Awareness,vCISOdisc7 @ 1:19 pm

🔥 Truth bomb from a experience: You can’t make companies care about security.

Most don’t—until they get burned.

Security isn’t important… until it suddenly is. And by then, it’s often too late. Just ask the businesses that disappeared after a cyberattack.

Trying to convince someone it matters? Like telling your friend to eat healthy—they won’t care until a personal wake-up call hits.

Here’s the smarter play: focus on the people who already value security. Show them why you’re the one who can solve their problems. That’s where your time actually pays off.

Your energy shouldn’t go into preaching; it should go into actionable impact for those ready to act.

⏳ Remember: people only take security seriously when they decide it’s worth it. Your job is to be ready when that moment comes.

Opinion:
This perspective is spot-on. Security adoption isn’t about persuasion; it’s about timing and alignment. The most effective consultants succeed not by preaching to the uninterested, but by identifying those who already recognize risk and helping them act decisively.

#CyberSecurity #vCISO #RiskManagement #AI #CyberResilience #SecurityStrategy #Leadership #Infosec

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Oct 28 2025

AI Governance Quick Audit

Open it in any web browser (Chrome, Firefox, Safari, Edge)

Complete the 10-question audit

Get your score and recommendations

10 comprehensive AI governance questionsReal-time progress trackingInteractive scoring system4 maturity levels (Initial, Emerging, Developing, Advanced) ✅ Personalized recommendationsComplete response summaryProfessional design with animations

Click 👇 below to open an AI Governance Quick Audit in your browser or click the image above.

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Click the ISO 42001 Awareness Quiz — it will open in your browser in full-screen mode

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Protect your AI systems — make compliance predictable.
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Tags: AI Governance Quick Audit


Oct 28 2025

InfoSec Policy Assistance

Category: AI,Information Securitydisc7 @ 10:11 am

Chatbot for a specific use case (policy Q&A, phishing training, etc.)

Click 👇 below to open an InfoSec-Chatbot in your browser or click the image above.

Open it in any web browser

Features:

  • ✅ Password & Authentication Policy Q&A
  • ✅ Data Classification guidance
  • ✅ Acceptable Use Policy
  • ✅ Security Incident Reporting procedures
  • ✅ Remote Work security guidelines
  • ✅ BYOD policy information
  • ✅ Interactive typing indicator
  • ✅ Quick prompt buttons
  • ✅ Severity indicators (Critical, High, Medium, Info)
  • ✅ Fully responsive design
  • ✅ Self-contained (no external dependencies)

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Click the ISO 42001 Awareness Quiz — it will open in your browser in full-screen mode

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Protect your AI systems — make compliance predictable.
Expert ISO-42001 readiness for small & mid-size orgs. Get a AI Risk vCISO-grade program without the full-time cost. Think of AI risk like a fire alarm—our register tracks risks, scores impact, and ensures mitigations are in place before disaster strikes.

Check out our earlier posts on AI-related topics: AI topic

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | Security Risk Assessment Services | Mergers and Acquisition Security

Tags: Chatbot, Infosec Cahtbot


Oct 17 2025

Deploying Agentic AI Safely: A Strategic Playbook for Technology Leaders

Category: AI,AI Governance,AI Guardrails,Information Securitydisc7 @ 11:16 am

McKinsey’s playbook, “Deploying Agentic AI with Safety and Security,” outlines a strategic approach for technology leaders to harness the potential of autonomous AI agents while mitigating associated risks. These AI systems, capable of reasoning, planning, and acting without human oversight, offer transformative opportunities across various sectors, including customer service, software development, and supply chain optimization. However, their autonomy introduces novel vulnerabilities that require proactive management.

The playbook emphasizes the importance of understanding the emerging risks associated with agentic AI. Unlike traditional AI systems, these agents function as “digital insiders,” operating within organizational systems with varying levels of privilege and authority. This autonomy can lead to unintended consequences, such as improper data exposure or unauthorized access to systems, posing significant security challenges.

To address these risks, the playbook advocates for a comprehensive AI governance framework that integrates safety and security measures throughout the AI lifecycle. This includes embedding control mechanisms within workflows, such as compliance agents and guardrail agents, to monitor and enforce policies in real time. Additionally, human oversight remains crucial, with leaders focusing on defining policies, monitoring outliers, and adjusting the level of human involvement as necessary.

The playbook also highlights the necessity of reimagining organizational workflows to accommodate the integration of AI agents. This involves transitioning to AI-first workflows, where human roles are redefined to steer and validate AI-driven processes. Such an approach ensures that AI agents operate within the desired parameters, aligning with organizational goals and compliance requirements.

Furthermore, the playbook underscores the importance of embedding observability into AI systems. By implementing monitoring tools that provide insights into AI agent behaviors and decision-making processes, organizations can detect anomalies and address potential issues promptly. This transparency fosters trust and accountability, essential components in the responsible deployment of AI technologies.

In addition to internal measures, the playbook advises technology leaders to engage with external stakeholders, including regulators and industry peers, to establish shared standards and best practices for AI safety and security. Collaborative efforts can lead to the development of industry-wide frameworks that promote consistency and reliability in AI deployments.

The playbook concludes by reiterating the transformative potential of agentic AI when deployed responsibly. By adopting a proactive approach to risk management and integrating safety and security measures into every phase of AI deployment, organizations can unlock the full value of these technologies while safeguarding against potential threats.

My Opinion:

The McKinsey playbook provides a comprehensive and pragmatic approach to deploying agentic AI technologies. Its emphasis on proactive risk management, integrated governance, and organizational adaptation offers a roadmap for technology leaders aiming to leverage AI’s potential responsibly. In an era where AI’s capabilities are rapidly advancing, such frameworks are essential to ensure that innovation does not outpace the safeguards necessary to protect organizational integrity and public trust.

Agentic AI: Navigating Risks and Security Challenges: A Beginner’s Guide to Understanding the New Threat Landscape of AI Agents

 

“AI is already the single largest uncontrolled channel for corporate data exfiltration—bigger than shadow SaaS or unmanaged file sharing.”

Click the ISO 42001 Awareness Quiz — it will open in your browser in full-screen mode

iso42001_quizDownload

Protect your AI systems — make compliance predictable.
Expert ISO-42001 readiness for small & mid-size orgs. Get a AI Risk vCISO-grade program without the full-time cost.

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Check out our earlier posts on AI-related topics: AI topic

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | Security Risk Assessment Services | Mergers and Acquisition Security

Tags: AI Agents, AI Playbook, AI safty


Oct 16 2025

AI Infrastructure Debt: Cisco Report Highlights Risks and Readiness Gaps for Enterprise AI Adoption

Category: AI,AI Governance,AI Guardrails,Information Securitydisc7 @ 4:55 pm

A recent Cisco report highlights a critical issue in the rapid adoption of artificial intelligence (AI) technologies by enterprises: the growing phenomenon of “AI infrastructure debt.” This term refers to the accumulation of technical gaps and delays that arise when organizations attempt to deploy AI on systems not originally designed to support such advanced workloads. As companies rush to integrate AI, many are discovering that their existing infrastructure is ill-equipped to handle the increased demands, leading to friction, escalating costs, and heightened security vulnerabilities.

The study reveals that while a majority of organizations are accelerating their AI initiatives, a significant number lack the confidence that their systems can scale appropriately to meet the demands of AI workloads. Security concerns are particularly pronounced, with many companies admitting that their current systems are not adequately protected against potential AI-related threats. Weaknesses in data protection, access control, and monitoring tools are prevalent, and traditional security measures that once safeguarded applications and users may not extend to autonomous AI systems capable of making independent decisions and taking actions.

A notable aspect of the report is the emphasis on “agentic AI”—systems that can perform tasks, communicate with other software, and make operational decisions without constant human supervision. While these autonomous agents offer significant operational efficiencies, they also introduce new attack surfaces. If such agents are misconfigured or compromised, they can propagate issues across interconnected systems, amplifying the potential impact of security breaches. Alarmingly, many organizations have yet to establish comprehensive plans for controlling or monitoring these agents, and few have strategies for human oversight once these systems begin to manage critical business operations.

Even before the widespread deployment of agentic AI, companies are encountering foundational challenges. Rising computational costs, limited data integration capabilities, and network strain are common obstacles. Many organizations lack centralized data repositories or reliable infrastructure necessary for large-scale AI implementations. Furthermore, security measures such as encryption, access control, and tamper detection are inconsistently applied, often treated as separate add-ons rather than being integrated into the core infrastructure. This fragmented approach complicates the identification and resolution of issues, making it more difficult to detect and contain problems promptly.

The concept of AI infrastructure debt underscores the gradual accumulation of these technical deficiencies. Initially, what may appear as minor gaps in computing resources or data management can evolve into significant weaknesses that hinder growth and expose organizations to security risks. If left unaddressed, this debt can impede innovation and erode trust in AI systems. Each new AI model, dataset, and integration point introduces potential vulnerabilities, and without consistent investment in infrastructure, it becomes increasingly challenging to understand where sensitive information resides and how it is protected.

Conversely, organizations that proactively address these infrastructure gaps are better positioned to reap the benefits of AI. The report identifies a group of “Pacesetters”—companies that have integrated AI readiness into their long-term strategies by building robust infrastructures and embedding security measures from the outset. These organizations report measurable gains in profitability, productivity, and innovation. Their disciplined approach to modernizing infrastructure and maintaining strong governance frameworks provides them with the flexibility to scale operations and respond to emerging threats effectively.

In conclusion, the Cisco report emphasizes that the value derived from AI is heavily contingent upon the strength and preparedness of the underlying systems that support it. For most enterprises, the primary obstacle is not the technology itself but the readiness to manage it securely and at scale. As AI continues to evolve, organizations that plan, modernize, and embed security early will be better equipped to navigate the complexities of this transformative technology. Those that delay addressing infrastructure debt may find themselves facing escalating technical and financial challenges in the future.

Opinion: The concept of AI infrastructure debt serves as a crucial reminder for organizations to adopt a proactive approach in preparing their systems for AI integration. Neglecting to modernize infrastructure and implement comprehensive security measures can lead to significant vulnerabilities and hinder the potential benefits of AI. By prioritizing infrastructure readiness and security, companies can position themselves to leverage AI technologies effectively and sustainably.

Everyone wants AI, but few are ready to defend it

Data for AI: Data Infrastructure for Machine Intelligence

“AI is already the single largest uncontrolled channel for corporate data exfiltration—bigger than shadow SaaS or unmanaged file sharing.”

Click the ISO 42001 Awareness Quiz — it will open in your browser in full-screen mode

iso42001_quizDownload

Protect your AI systems — make compliance predictable.
Expert ISO-42001 readiness for small & mid-size orgs. Get a AI Risk vCISO-grade program without the full-time cost.

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Check out our earlier posts on AI-related topics: AI topic

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | Security Risk Assessment Services | Mergers and Acquisition Security

Tags: AI Infrastructure Debt


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