Frameworks for AI Security Governance

The National Institute of Standards and Technology (NIST) has released a draft of its “Cybersecurity Framework Profile for Artificial Intelligence” (open for public comment until Jan 30, 2026) to help organizations think about how to strategically adopt AI while addressing emerging cybersecurity risks that stem from AI’s rapid advance. Building on the #NIST Cybersecurity Framework 2.0, the Cyber AI Profile translates well-established risk management concepts into AI-specific cybersecurity considerations, offering a practical reference point as organizations integrate AI into critical systems and confront AI-enabled threats. The Cyber AI Profile centers on three focus areas: • Securing AI systems: identifying cybersecurity challenges when integrating AI into organizational ecosystems and infrastructure. • Conducting AI-enabled cyber defense: identifying opportunities to use AI to enhance cybersecurity, and understanding challenges when leveraging AI to support defensive operations. • Thwarting AI-enabled cyberattacks: building resilience to protect against new AI-enabled threats. The Profile complements existing NIST frameworks (CSF, AI RMF, RMF) by prioritizing AI-specific cybersecurity outcomes rather than creating a standalone regime.

"The rapid evolution and swift adoption of generative AI have prompted governments to keep pace and prepare for future developments and impacts. Policy-makers are considering how generative artificial intelligence (AI) can be used in the public interest, balancing economic and social opportunities while mitigating risks. To achieve this purpose, this paper provides a comprehensive 360° governance framework: 1 Harness past: Use existing regulations and address gaps introduced by generative AI. The effectiveness of national strategies for promoting AI innovation and responsible practices depends on the timely assessment of the regulatory levers at hand to tackle the unique challenges and opportunities presented by the technology. Prior to developing new AI regulations or authorities, governments should: – Assess existing regulations for tensions and gaps caused by generative AI, coordinating across the policy objectives of multiple regulatory instruments – Clarify responsibility allocation through legal and regulatory precedents and supplement efforts where gaps are found – Evaluate existing regulatory authorities for capacity to tackle generative AI challenges and consider the trade-offs for centralizing authority within a dedicated agency 2 Build present: Cultivate whole-of-society generative AI governance and cross-sector knowledge sharing. Government policy-makers and regulators cannot independently ensure the resilient governance of generative AI – additional stakeholder groups from across industry, civil society and academia are also needed. Governments must use a broader set of governance tools, beyond regulations, to: – Address challenges unique to each stakeholder group in contributing to whole-of-society generative AI governance – Cultivate multistakeholder knowledge-sharing and encourage interdisciplinary thinking – Lead by example by adopting responsible AI practices 3 Plan future: Incorporate preparedness and agility into generative AI governance and cultivate international cooperation. Generative AI’s capabilities are evolving alongside other technologies. Governments need to develop national strategies that consider limited resources and global uncertainties, and that feature foresight mechanisms to adapt policies and regulations to technological advancements and emerging risks. This necessitates the following key actions: – Targeted investments for AI upskilling and recruitment in government – Horizon scanning of generative AI innovation and foreseeable risks associated with emerging capabilities, convergence with other technologies and interactions with humans – Foresight exercises to prepare for multiple possible futures – Impact assessment and agile regulations to prepare for the downstream effects of existing regulation and for future AI developments – International cooperation to align standards and risk taxonomies and facilitate the sharing of knowledge and infrastructure"

Shipping AI agents into production without governance is like deploying software without security, logs, or controls. It might work at first. But sooner or later, something breaks - silently. As AI agents move from experiments to real decision-makers, governance becomes infrastructure. This framework breaks AI Governance into the core functions every production-grade agent system needs: - Policy Rules Turn business and regulatory expectations into enforceable agent behavior - defining what agents can do, must avoid, and how they respond in restricted scenarios. - Access Control Limits agents to approved tools, datasets, and systems using identity verification, RBAC, and permission boundaries — preventing accidental or malicious misuse. - Audit Logs Create a full activity trail of agent decisions: what data was accessed, which tools were called, and why actions were taken — making every outcome traceable. - Risk Scoring Evaluates agent actions before execution, assigns risk levels, detects sensitive operations, and blocks unsafe decisions through thresholds and safety scoring. - Data Privacy Protects confidential information using PII detection, encryption, consent management, and retention policies — ensuring agents don’t leak regulated data. - Model Monitoring Tracks real-world agent performance: accuracy, drift, hallucinations, latency, and cost - keeping systems reliable after deployment. - Human Approvals Adds human-in-the-loop controls for high-impact actions, enabling escalation, overrides, and sign-offs when automation alone isn’t enough. - Incident Response Detects failures early and enables rapid containment through alerts, rollbacks, kill switches, and post-incident reporting to prevent repeat issues. The takeaway: AI agents don’t just need intelligence. They need guardrails. Without governance, agents become unpredictable. With governance, they become enterprise-ready. This is how organizations move from experimental AI to trustworthy, compliant, production systems. Save this if you’re building agentic systems. Share it with your platform or ML teams.

We believed we were ahead on AI. Clear policies. Approved vendors. Strong controls. Then we discovered widespread use of unapproved AI tools across teams. It looked like a governance failure. It wasn’t. It was an operating model failure. Across industries, nearly half of AI users operate outside official systems. Not out of defiance, but urgency. When organizations restrict tools without providing viable alternatives, innovation doesn’t stop. It decentralizes. That creates three enterprise risks: → Data exposure: sensitive information entering unmanaged systems → Decision risk: AI outputs influencing customers or operations without oversight → Competitive risk: experimentation happening in silos instead of compounding knowledge Shadow AI is not the disease. It’s a signal that governance and innovation are misaligned. The real question for CXOs: How do we enable AI at scale without increasing enterprise risk? A CXO Framework for Governing AI at Scale 1. Provide a Secure Enterprise Environment Prohibition fails. Offer a compliant AI environment where: → Data remains protected → Permissions mirror identity systems → Usage is auditable Make the secure path the easiest path. 2. Formalize an AI Center of Excellence Your “shadow” users are early adopters. Pair them with IT and security to: → Evaluate tools → Define standards → Scale best practices Turn experimentation into enterprise capability. 3. Accelerate Tool Review AI moves faster than traditional procurement. Implement: → 48–72 hour preliminary reviews → Risk-based approval tiers Speed is now part of governance. 4. Capture Institutional Knowledge AI scales when workflows are shared. Incentivize: → Documented prompts → Reusable automations The advantage is knowledge compounding. 5. Require Human Oversight AI can hallucinate. External-facing outputs require human verification. Automation should enhance judgment, not replace it. 6. Define Data Guardrails Clarify: → What data is permitted → What is prohibited Most leaks stem from ambiguity, not intent. 7. Control AI Agents Through Identity As AI agents act across systems, they must inherit: → Human-equivalent permissions → Audit visibility Autonomy without controls multiplies risk. 8. Treat Governance as Infrastructure Governance is not a brake. It is traction. Clear boundaries allow confident experimentation. The Strategic Reality Boards are asking: → How is AI governed? → What is the exposure? → Where is the ROI? Blocking tools may ease short-term anxiety. But it increases long-term competitive risk. The organizations that win will: → Govern intelligently → Institutionalize learning → Align AI with enterprise architecture Shadow AI isn’t a compliance failure. It’s a signal your operating model must evolve. Want a high-res copy of this infographic? Get is here: https://lnkd.in/gevFM-eu Save this for future reference.

✴ AI Governance Blueprint via ISO Standards – The 4-Legged Stool✴ ➡ ISO42001: The Foundation for Responsible AI #ISO42001 is dedicated to AI governance, guiding organizations in managing AI-specific risks like bias, transparency, and accountability. Focus areas include: ✅Risk Management: Defines processes for identifying and mitigating AI risks, ensuring systems are fair, robust, and ethically aligned. ✅Ethics and Transparency: Promotes policies that encourage transparency in AI operations, data usage, and decision-making. ✅Continuous Monitoring: Emphasizes ongoing improvement, adapting AI practices to address new risks and regulatory updates. ➡#ISO27001: Securing the Data Backbone AI relies heavily on data, making ISO27001’s information security framework essential. It protects data integrity through: ✅Data Confidentiality and Integrity: Ensures data protection, crucial for trustworthy AI operations. ✅Security Risk Management: Provides a systematic approach to managing security risks and preparing for potential breaches. ✅Business Continuity: Offers guidelines for incident response, ensuring AI systems remain reliable. ➡ISO27701: Privacy Assurance in AI #ISO27701 builds on ISO27001, adding a layer of privacy controls to protect personally identifiable information (PII) that AI systems may process. Key areas include: ✅Privacy Governance: Ensures AI systems handle PII responsibly, in compliance with privacy laws like GDPR. ✅Data Minimization and Protection: Establishes guidelines for minimizing PII exposure and enhancing privacy through data protection measures. ✅Transparency in Data Processing: Promotes clear communication about data collection, use, and consent, building trust in AI-driven services. ➡ISO37301: Building a Culture of Compliance #ISO37301 cultivates a compliance-focused culture, supporting AI’s ethical and legal responsibilities. Contributions include: ✅Compliance Obligations: Helps organizations meet current and future regulatory standards for AI. ✅Transparency and Accountability: Reinforces transparent reporting and adherence to ethical standards, building stakeholder trust. ✅Compliance Risk Assessment: Identifies legal or reputational risks AI systems might pose, enabling proactive mitigation. ➡Why This Quartet? Combining these standards establishes a comprehensive compliance framework: 🥇1. Unified Risk and Privacy Management: Integrates AI-specific risk (ISO42001), data security (ISO27001), and privacy (ISO27701) with compliance (ISO37301), creating a holistic approach to risk mitigation. 🥈 2. Cross-Functional Alignment: Encourages collaboration across AI, IT, and compliance teams, fostering a unified response to AI risks and privacy concerns. 🥉 3. Continuous Improvement: ISO42001’s ongoing improvement cycle, supported by ISO27001’s security measures, ISO27701’s privacy protocols, and ISO37301’s compliance adaptability, ensures the framework remains resilient and adaptable to emerging challenges.

🤖 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞’𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 – 𝐛𝐮𝐭 𝐡𝐚𝐫𝐝𝐥𝐲 𝐚𝐧𝐲𝐨𝐧𝐞 𝐢𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲. 🔐 As a CISO, I see the rapid rollout of AI tools across organizations. But what often gets overlooked are the unique security risks these systems introduce. Unlike traditional software, AI systems create entirely new attack surfaces like: ⚠️ 𝐃𝐚𝐭𝐚 𝐩𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠: Just a few manipulated data points can alter model behavior in subtle but dangerous ways. ⚠️ 𝐏𝐫𝐨𝐦𝐩𝐭 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧: Malicious inputs can trick models into revealing sensitive data or bypassing safeguards. ⚠️ 𝐒𝐡𝐚𝐝𝐨𝐰 𝐀𝐈: Unofficial tools used without oversight can undermine compliance and governance entirely. We urgently need new ways of thinking and structured frameworks to embed security from the very beginning. 📘 A great starting point is the new 𝐒𝐀𝐈𝐋 (𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐈 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞) Framework whitepaper by Pillar Security. It provides actionable guidance for integrating security across every phase of the AI lifecycle from planning and development to deployment and monitoring. 🔍 𝐖𝐡𝐚𝐭 𝐈 𝐩𝐚𝐫𝐭𝐢𝐜𝐮𝐥𝐚𝐫𝐥𝐲 𝐯𝐚𝐥𝐮𝐞: ✅ More than 𝟕𝟎 𝐀𝐈-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐫𝐢𝐬𝐤𝐬, mapped and categorized ✅ A clear phase-based structure: Plan – Build – Test – Deploy – Operate – Monitor ✅ Alignment with current standards like ISO 42001, NIST AI RMF and the OWASP Top 10 for LLMs 👉 Read the full whitepaper here: https://lnkd.in/ebtbztQC How are you approaching AI risk in your organization? Have you already started implementing a structured AI security framework? #AIsecurity #CISO #SAILframework #SecureAI #Governance #MLops #Cybersecurity #AIrisks

The Cloud Security Alliance just published my framework for governing AI agents. It's called the Agentic Trust Framework. And here's why it matters: Every AI agent in your environment can reason, learn, and take action on its own. Your security framework was built for humans who follow rules. Traditional security assumes: ✔️ Predictable user behavior ✔️ Deterministic system rules ✔️ Binary access decisions ✔️ Trust established once AI agents break every one of these assumptions. Every. Single. One. Don't stop building AI agents. But it's important you're considering a few things to keep them secure. I built a governance model around five questions every organization must answer for every agent: ✔️ Who are you? (Identity) ✔️ What are you doing? (Behavior) ✔️ What are you eating and serving? (Data Governance) ✔️ Where can you go? (Segmentation) ✔️ What if you go rogue? (Incident Response) Plus a maturity model where agents earn autonomy over time. Intern to Principal, just like your human employees. It's open source. CC BY 4.0. And ready to implement. The link's in the comments.

4 AI Governance Frameworks To build trust and confidence in AI. In this post, I’m sharing takeaways from leading firms' research on how organisations can unlock value from AI while managing its risks. As leaders, it’s no longer about whether we implement AI, but how we do it responsibly, strategically, and at scale. ➜ Deloitte’s Roadmap for Strategic AI Governance From Harvard Law School’s Forum on Corporate Governance, Deloitte outlines a structured, board-level approach to AI oversight: 🔹 Clarify roles between the board, management, and committees for AI oversight. 🔹 Embed AI into enterprise risk management processes—not just tech governance. 🔹 Balance innovation with accountability by focusing on cross-functional governance. 🔹 Build a dynamic AI policy framework that adapts with evolving risks and regulations. ➜ Gartner’s AI Ethics Priorities Gartner outlines what organisations must do to build trust in AI systems and avoid reputational harm: 🔹 Create an AI-specific ethics policy—don’t rely solely on general codes of conduct. 🔹 Establish internal AI ethics boards to guide development and deployment. 🔹 Measure and monitor AI outcomes to ensure fairness, explainability, and accountability. 🔹 Embed AI ethics into product lifecycle—from design to deployment. ➜ McKinsey’s Safe and Fast GenAI Deployment Model McKinsey emphasises building robust governance structures that enable speed and safety: 🔹 Establish cross-functional steering groups to coordinate AI efforts. 🔹 Implement tiered controls for risk, especially in regulated sectors. 🔹 Develop AI Guidelines and policies to guide enterprise-wide responsible use. 🔹 Train all stakeholders—not just developers—to manage risks. ➜ PwC’s AI Lifecycle Governance Framework PwC highlights how leaders can unlock AI’s potential while minimising risk and ensuring alignment with business goals: 🔹 Define your organisation’s position on the use of AI and establish methods for innovating safely 🔹 Take AI out of the shadows: establish ‘line of sight’ over the AI and advanced analytics solutions  🔹 Embed ‘compliance by design’ across the AI lifecycle. Achieving success with AI goes beyond just adopting it. It requires strong leadership, effective governance, and trust. I hope these insights give you enough starting points to lead meaningful discussions and foster responsible innovation within your organisation. 💬 What are the biggest hurdles you face with AI governance? I’d be interested to hear your thoughts.

🔐 The A.G.E.N.T. Security Framework: A practical model for securing Agentic AI systems at enterprise scale. 🚨 Lots of orgs are flying blind into Agentic AI. Without a maturity model, chaos is inevitable. 👀 Introducing A.G.E.N.T. Security Framework 👇🏾 The A.G.E.N.T. Security Framework is a 5-phase guide I’ve build with other CISOs & Security Leaders which helped them move from AI chaos → clarity. The 5 Phases of A.G.E.N.T. 🕵🏾♀️ Awareness (A) Shadow AI adoption creates blind spots. Guardrails: governance councils, discovery tools, acceptable use. 🛡️ Governance (G) Copilots enter workflows; identity sprawl + data leakage risk explode. Guardrails: structured onboarding, scoped access, policy consistency. 🏗️ Engineering (E) Enterprises build “paved roads” with MCP servers, LLMs, APIs. Guardrails: sandbox testing, lifecycle governance, token management, version control. 🧭 Navigation (N) Semi-agentic AI starts acting in production. Guardrails: runtime policies, rollback paths, anomaly detection. 💪🏾 Trust (T) Even 95% accurate agents can cascade failures. Guardrails: human-in-loop for high-impact moves, escalation workflows, dashboards. 📊 For CISOs & Tech Leaders: ✔️ Map your org against these 5 phases ✔️ Identify missing guardrails ✔️ Decide where to invest 𝘯𝘦𝘹𝘵 𝘲𝘶𝘢𝘳𝘵𝘦𝘳 💡 Lesson: Without guardrails, small cracks become systemic risks. With them, AI can scale securely without killing innovation. 👉🏾  Most orgs stall between Awareness & Governance. 👀 Want the full A.G.E.N.T. maturity playbook with risks + guardrails mapped? Comment “AGENT” and I’ll share it with you. Question for you: Which phase feels most real in your org today? (see infographics below) 👇🏾 ---------------------------------------------------------------------- 🎙️ I’ll unpack this on the 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 & 𝗔𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 next week -  available on Apple, Spotify, YouTube, LinkedIn. You can now Save 🔖 this post to revisit and come back later when you need to revisit in a easy place to find. 😎 If you're looking to keep up on latest AI strategy, security, and scalability: 🔹 Follow Ashish Rajan for insights tailored to CISOs & Security Practitioners ♻️ Repost to help others to cut through the noise around AI Security. #𝗔𝗜𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 #AI #Cybersecurity

Many AI governance frameworks assume you're building the model. But what if you're not? If your organization deploys, integrates, or operates AI systems built by someone else, you've probably noticed the guidance gap. You're on the hook for responsible use but the playbooks are written for teams with access to training data and model weights you'll never see. ISO/IEC 42001 and ETSI EN 304 223 are different. Both explicitly recognize that AI governance isn't just a developer problem. I wrote up a practical breakdown of how these two standards work together: → ISO 42001 gives you the management system: policies, risk assessments, roles, audit cycles → EN 304 223 gives you the security specifics: threat modeling, access controls, supply chain requirements, secure decommissioning Neither is perfect alone. Together, they get you closer to an AI governance program that's defensible, auditable, and actually implementable. The post covers: ✅ What each standard does (and doesn't do) ✅ Where they complement each other ✅ The gaps you'll still need to fill ✅ Practical steps to get started If you're in legal, compliance, privacy, or security and you're trying to figure out how to govern AI systems you didn't build, this is for you. Link in comments 👇 #AIGovernance #ResponsibleAI #ISO42001 #Compliance #Privacy #AIRisk