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.

Most AI governance programs are built backwards 🔁 They start with policy. They end with a risk register. And somewhere in the middle, no one owns anything, and nothing is actually governed. The framework that changed how I think about this is the AI Governance Stack! It's the best mental model I've encountered for making AI governance executable rather than aspirational. Here's what each layer actually requires: 1️⃣ Data Governance: This is the foundation! Training data quality thresholds, bias assessment before the first model weight is set, provenance tracking from source through transformation, consent documentation for personal data, and version control on every dataset used in training. The core principle: model quality cannot exceed data quality. A fairness problem that originates here cannot be fixed at any layer above. 2️⃣ Model Governance: Architecture review, fairness testing across demographic subgroups, robustness evaluation against adversarial inputs, interpretability requirements appropriate to the deployment context, and model documentation (model cards) created during development. This is where most teams underinvest. The model is the governance artifact everyone focuses on, and it's often the layer with the least systematic coverage. 3️⃣ System Integration Governance: How the AI connects to everything else. Cascading failure analysis across dependent systems, human-AI interaction design that supports genuine oversight rather than rubber-stamping, boundary condition testing for inputs outside the training distribution. A model that works in isolation can fail catastrophically in production when the surrounding system doesn't account for how it actually behaves. 4️⃣ Control & Monitoring Governance: Real-time performance monitoring, drift detection, anomaly detection, access controls, incident response procedures, and deployment gates that prevent promotion without sign-off. This is the operational layer most organizations may not build fully. Monitoring requirements should shape deployment architecture from the start. 5️⃣ Audit & Evidence Governance: Documentation standards, immutable audit trails, regulatory reporting capabilities, and stakeholder communication protocols. The EU AI Act's technical documentation requirements alone are extensive enough to require dedicated infrastructure. The critical insight that makes the Stack more than a checklist: failures cascade upward, not downward. A Layer 1 data problem corrupts Layer 2 model outputs. This is why bolt-on governance fails. You can't audit your way out of a training data problem. Bookmark this 🔖 every post in this series maps back to one or more of these five layers. Drop a comment: which layer does your organization have the least mature coverage on right now? #AIGovernance #GRC #RiskManagement #AI #Compliance

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.

✴ 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.

Everyone’s racing to deploy AI. Very few have defined who’s accountable when it fails. AI governance isn’t a compliance checkbox. It’s the operating system that determines whether your AI can be trusted — at scale, in production, under scrutiny. Here’s what a real AI Governance Framework looks like: ⸻ 👥 People — Ownership & Accountability → Chief AI / Data leadership setting direction → AI Governance Council & Ethics Board driving oversight → Model owners, product owners, Responsible AI champions → Cross-functional alignment: Legal, Risk, Security, Compliance, Data, Engineering → Organization-wide training and awareness ⸻ ⚙️ Process — Policy, Risk & Control → Clear decision rights and governance policies upfront → Responsible AI principles: fairness, explainability, transparency → Use-case risk assessment before deployment → Alignment with EU AI Act, NIST AI RMF, ISO/IEC 42001 → End-to-end lifecycle governance: design → test → monitor → audit → Incident response for hallucinations, bias, and misuse ⸻ 🧠 Technology — Enforcement by Design → AI catalog & model registry for visibility → Centralized AI gateway for policy enforcement → Observability, monitoring, and traceability across the stack → Data governance + lineage as the foundation → Access control, security, and human-in-the-loop → Explainability built into the architecture — not bolted on ⸻ The winners in AI won’t be the fastest movers. They’ll be the ones who built trust into the system from day one.

🔐 AI Governance Is No Longer Optional — It Must Be Integrated Into Cybersecurity Training & GRC Now As AI systems become embedded across enterprise security, threat detection, identity workflows, and automation pipelines, the risk surface is expanding faster than traditional controls can keep up. Effective AI governance must now be treated as a first-class component of cybersecurity programs—embedded directly into training, operational security, and GRC frameworks. Here’s how forward-leaning security teams are doing it: 🔎 1. Establish an AI Governance Framework Use structured governance models that mirror established security frameworks: AI risk classification: Identify AI systems, data flows, decision impact, and safety-critical components. Model lifecycle controls: Apply versioning, approval gates, drift monitoring, and performance validation. Security & privacy baselines: Enforce threat modeling, data minimization, PII controls, and red-team evaluations against prompt injection and model exploitation. 🛡 2. Integrate AI Threat Modeling Into Training Extend existing secure engineering and AppSec training to include: AI/ML-specific threat scenarios: Model poisoning, adversarial inputs, jailbreaks, training-data leakage. Secure prompt engineering: Guardrails, context restriction, least-privilege prompts, and API-level access management. Model behavior validation: Teach staff how to evaluate hallucination risk, output integrity, and system response boundaries. Supply chain considerations: Validate datasets, model sources, vendor controls, and licensing compliance. 📘 3. Embed AI Governance Into GRC Processes Treat AI systems like any other technology subject to governance, but with enhanced oversight: Policy Mapping: Align AI use with ISO 42001, NIST AI RMF, and existing enterprise security policies. AI Risk Register Entries: Document model usage, data categories, risk ratings, and compensating controls. Continuous Monitoring: Measure model drift, decision error rates, anomalous outputs, and access patterns. Control Families: Integrate AI-specific controls into your existing GRC stack—access control, data classification, audit logging, third-party risk, and model deployment workflows. 🧩 4. Build AI Governance Into Incident Response AI incidents require new playbooks: Model-driven incident categories: Output manipulation, model degradation, training data exposure, unauthorized fine-tuning. Forensic Support: Log prompts, context injection attempts, and model inference metadata. Rollback Mechanisms: Maintain approved model versions, data lineage tracking, and automated reversion paths. #Cybersecurity #AIGovernance #GRC #CyberRiskManagement #AIsecurity #InformationSecurity #SecurityEngineering #NISTAI #ISO42001 #ThreatModeling #CyberTraining #CISO #RiskAndCompliance #AIMaturity

AI governance sounds boring until your model halts production. Or leaks customer data. Or makes a biased hiring decision. We built AI governance from scratch last year. Here's the framework that keeps us compliant, ethical, and fast. The AI Governance Pyramid. Five layers. Most teams skip straight to the top. That's why their AI implementations fail audits, break trust, or get shut down. Layer 1 (Foundation): Ethics & Principles. This is your "why we use AI" layer. Define your red lines before you build anything. What won't you automate? What decisions require humans? What bias are you willing to tolerate (spoiler: none)? We documented ours in a 2-page ethics charter. Every AI project gets measured against it. If it violates the charter, we don't build it. No exceptions. Layer 2: Data Governance. AI is only as good as your data. And your data is probably a mess. Where does it come from? Who owns it? How long do you keep it? What can't you use? We created a data classification system. Public. Internal. Confidential. Restricted. Each AI model gets assigned a data tier. If you need restricted data, you need executive approval. Layer 3: Risk & Compliance. This is where legal and security teams get involved. What regulations apply? GDPR? CCPA? Industry-specific rules? What happens if the AI makes a wrong decision? We run a risk assessment on every AI project. Low risk = fast approval. High risk = board review. Most teams skip this layer. Then spend months fixing compliance issues after launch. Layer 4: Operational Standards. How do you actually build and deploy AI safely? Model testing protocols. Version control. Access permissions. Monitoring and alerts. We created AI deployment checklists. No model goes live without passing every checkpoint. This layer is boring. It's also what prevents disasters. Layer 5 (Peak): Execution & Innovation. This is where most teams start. "Let's build a chatbot." "Let's automate this workflow." But without the four layers underneath, you're building on sand. When you have the foundation, execution is fast. You know what's allowed. You know how to build safely. You know how to scale without breaking things. Here's what we learned. Most AI failures aren't technical failures. They're governance failures. Someone skipped a layer. Someone didn't document data sources. Someone didn't assess risk. The pyramid looks slow. It's actually what lets you move fast without breaking everything. Which layer does your org skip? Found this helpful? Follow Arturo Ferreira and repost ♻️

Deploying AI? Google SAIF vs. Cisco Integrated AI Security and Safety Framework You need both. 🛡️ Google SAIF is your Governance and Implementation Guide on how to build a security program, architect secure infrastructure, and apply specific controls, like identity and input filtering. 🕸️ @Cisco AI Security Framework is your Threat Taxonomy and Risk Atlas, detailing exactly which security and safety threats to defend against. Here’s an oversimplified 4-step playbook to use them together (Friday edition): 1️⃣ Build the Foundation (Google SAIF). Establish AI Governance Controls and an Acceptable Use Policy, enforced by an AI platform across the entire lifecycle from training to deployment. Now you have a model and agent inventory, a secure vault for artifacts, and enforcement rails. 2️⃣ Prioritize Protecting From The Top 3 Techniques (Cisco): 🔹 Goal Hijacking, specifically Indirect Prompt Injections (AITech-1.2). Attackers hide instructions in trusted sources like emails or documents to manipulate AI into abandoning its primary directive. 🔹 Data Exfiltration / Exposure (AITech-8.2). Prioritize exfiltration via tool misuse and exploitation. Attackers coerce AI into using connected tools like Slack or Gmail to send internal data externally. Pay attention to MCP gateways. 🔹 Dependency / Plugin Compromise (AITech-9.3). Third-party libraries play a critical role in AI systems, making them an important attack vector. Attackers publish poisoned packages (e.g., on npm) that coding agents auto-install, creating hidden backdoors to steal SSH keys and API tokens. The OWASP® Foundation folks will reasonably ask "what about identity?" So, add Unauthorized Access (AITech-14.1) to your list. 3️⃣ Deploy Technical Controls (Google SAIF). Map defenses directly to the prioritized vectors. ⚒️ Deploy an “LLM Firewall” to sanitize model inputs and outputs for malicious payloads. There are plenty of options on the market to choose from. 🔧 Enforce "Human-in-the-Loop" approval for sensitive actions and look for a contextual policy solution. 🪛 Basic dependency hygiene by checking for typosquats and provenance, and keeping prompts under version control are a good start. A dependency scanner can level up your protections. 4️⃣ Red Team & Validate (Cisco). Controls are theoretical until tested. Get a third party’s help from an AI-native player to stress-test your AI system. The findings will help prioritize next steps. Your cyber insurance provider will also ask you questions soon regarding how AI is governed and how AI decisions are made. See a great post from Judy Selby in the comments below. 👇 Great news: there’s a growing ecosystem of AI-native cybersecurity companies that aim to address emerging risks. See my earlier post on AI for application security. 👇 #CISO #AISecurity #GoogleCloud #Cisco #Cybersecurity #AIGovernance #RedTeaming #GenAI #LLMSecurity