AI Security Policy Guidelines

The Cybersecurity and Infrastructure Security Agency, National Security Agency, and other cybersecurity agencies Published “Careful Adoption of Agentic AI Services” providing a detailed framework for securely deploying, operating, and governing agentic AI systems. This joint guidance focuses on the unique risks introduced by AI systems capable of autonomously making decisions, using tools, and taking actions with limited human intervention, and recommends a “secure by default” approach. Some of the recommendations include: • Adopt a phased deployment approach by starting with low-risk use cases, limiting permissions and autonomy initially, and progressively expanding capabilities based on ongoing evaluation and oversight. • Implement strong guardrails and constraints, including explicit “do-not-do” rules, deny lists, safety policies, sandboxing, and layered controls to reduce the risk of harmful or unintended actions. • Maintain meaningful human oversight as a central control mechanism for high-impact or irreversible actions. The document recommends clear human approval checkpoints , defined accountability structures, and escalation procedures for sensitive operations. • Apply strict privilege and authentication controls by limiting agents to the minimum access required, using just-in-time credentials, continuously validating authorization, and preventing agents from modifying their own privileges. • Use continuous monitoring and comprehensive logging to track agent reasoning, tool usage, decisions, identity changes, and anomalous behavior in real time. The guidance stresses that monitoring should extend beyond inputs and outputs to include internal agent processes. • Conduct red teaming and scenario-based testing before and after deployment to identify prompt injection risks, emergent behaviors, attempts to evade safeguards, and other unexpected system interactions. • Strengthen resilience through fail-safe defaults, rollback capabilities, segmentation, and containment mechanisms designed to reduce the operational impact of compromised or malfunctioning agents. • Manage third-party and tool-integration risks by verifying external components, restricting tool usage to approved allow lists, monitoring inter-agent interactions, and applying supply chain risk management practices. • Integrate governance and accountability structures that define risk ownership, establish AI-specific policies, and align agentic AI oversight with existing cybersecurity and risk management frameworks. • Use system-level security analysis rather than evaluating components in isolation. The document highlights that risks in agentic AI environments often emerge from interactions between models, tools, humans, datasets, and infrastructure. The document presents agentic AI security as an ongoing operational discipline focused on resilience, containment, observability, and controlled autonomy across the full lifecycle of deployment and use. 

𝟐𝟎 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐃𝐞𝐩𝐥𝐨𝐲 𝐀𝐈 Most AI Failures in enterprises are not Technical. They are Compliance Failures. Before deploying AI into Production,  Here are the 20 Non-Negotiables: 1. Appoint AI Accountability Leader   Assign a senior executive responsible for AI compliance, oversight, and reporting. 2. Establish Cross-Functional AI Board   Include legal, security, HR, data, and business teams for governance and approvals. 3. Define Legal AI Role   Clarify provider versus deployer obligations and compliance responsibilities. 4. Maintain Technical Documentation   Document architecture, data sources, performance metrics, and intended use limitations. 5. Disclose AI Usage Transparently   Notify users about AI interactions and synthetic content usage. 6. Publish Model Transparency Reports   Document purpose, performance across demographics, limits, and out-of-scope scenarios. 7. Implement Logging and Audits   Track inputs, outputs, versions, and decisions for investigations and traceability. 8. Ensure Decision Explainability   Provide meaningful explanations and enable human review of high-impact decisions. 9. Create Comprehensive AI Inventory   Document all AI systems, APIs, models, and embedded SaaS tools. 10. Develop AI Acceptable Use Policy   Define permitted uses, prohibited activities, and approved data types. 11. Classify AI Risk Levels   Categorize systems into prohibited, high, limited, or minimal risk tiers. 12. Conduct Formal Risk Assessments   Identify harms, discrimination risks, and safety issues before deployment. 13. Test for Bias Regularly   Evaluate outputs across protected groups and document mitigation steps. 14. Review Third-Party AI Risk   Assess vendor compliance, contracts, liabilities, and regulatory responsibilities. 15. Govern Training Data Legality   Track licenses, avoid unauthorized scraping, and respect copyrights. 16. Perform Required DPIAs   Assess high-risk personal data processing under GDPR and similar regulations. 17. Confirm Lawful Data Basis   Verify consent, contractual necessity, or legitimate interest before processing data. 18. Apply Data Minimization Rules   Limit data usage and enforce strict retention schedules. 19. Secure AI Infrastructure Assets   Protect pipelines, weights, APIs, and model endpoints with strong controls. 20. Support Data Subject Rights   Enable access, correction, deletion, restriction, and automated decision opt-outs. The real shift in enterprise AI is this. From model performance to governance readiness. From proof of concept to regulatory durability. If your AI cannot pass audit, it cannot scale. Compliance is not friction. It is infrastructure. PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #EnterpriseAI #AIGovernance #ResponsibleAI

AI success isn’t just about innovation - it’s about governance, trust, and accountability. I've seen too many promising AI projects stall because these foundational policies were an afterthought, not a priority. Learn from those mistakes. Here are the 16 foundational AI policies that every enterprise should implement: ➞ 1. Data Privacy: Prevent sensitive data from leaking into prompts or models. Classify data (Public, Internal, Confidential) before AI usage. ➞ 2. Access Control: Stop unauthorized access to AI systems. Use role-based access and least-privilege principles for all AI tools. ➞ 3. Model Usage: Ensure teams use only approved AI models. Maintain an internal “model catalog” with ownership and review logs. ➞ 4. Prompt Handling: Block confidential information from leaking through prompts. Use redaction and filters to sanitize inputs automatically. ➞ 5. Data Retention: Keep your AI logs compliant and secure. Define deletion timelines for logs, outputs, and prompts. ➞ 6. AI Security: Prevent prompt injection and jailbreaks. Run adversarial testing before deploying AI systems. ➞ 7. Human-in-the-Loop: Add human oversight to avoid irreversible AI errors. Set approval steps for critical or sensitive AI actions. ➞ 8. Explainability: Justify AI-driven decisions transparently. Require “why this output” traceability for regulated workflows. ➞ 9. Audit Logging: Without logs, you can’t debug or prove compliance. Log every prompt, model, output, and decision event. ➞ 10. Bias & Fairness: Avoid biased AI outputs that harm users or breach laws. Run fairness testing across diverse user groups and use cases. ➞ 11. Model Evaluation: Don’t let “good-looking” models fail in production. Use pre-defined benchmarks before deployment. ➞ 12. Monitoring & Drift: Models degrade silently over time. Track performance drift metrics weekly to maintain reliability. ➞ 13. Vendor Governance: External AI providers can introduce hidden risks. Perform security and privacy reviews before onboarding vendors. ➞ 14. IP Protection: Protect internal IP from external model exposure. Define what data cannot be shared with third-party AI tools. ➞ 15. Incident Response: Every AI failure needs a containment plan. Create a “kill switch” and escalation playbook for quick action. ➞ 16. Responsible AI: Ensure AI is built and used ethically. Publish internal AI principles and enforce them in reviews. AI without policy is chaos. Strong governance isn’t bureaucracy - it’s your competitive edge in the AI era. 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow Nick Tudor for more insights on AI + IoT that actually ship.

✴ 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.

ISO/IEC 27090 is soon to be published. After reviewing the final draft, one thing stands out: AI is not just introducing new risks. It is forcing organisations to define entirely new policy domains. Here are the key high-level AI security policies emerging from the standard: 🔹 AI Governance Establish ownership, maintain an inventory of AI systems (AIBOM), and manage risk across the lifecycle. 🔹 Data Usage & Minimisation Define what data can be used in AI, minimise data exposure, control retention, and apply privacy-preserving techniques. 🔹 Zero Trust for AI Adopt “never trust, always verify” for both users and AI systems, with strict identity and least privilege controls. 🔹 AI Lifecycle Security Apply secure engineering practices from development to deployment, including model continuous input/output validation and testing. 🔹 Model Behaviour & Safety Controls Set guardrails to manage unwanted behaviour, prevent overreliance, and limit excessive autonomy. 🔹 Human Oversight Define when human review is required to maintain accountability and avoid “out-of-the-loop” risk. 🔹 Supply Chain & Model Provenance Track where models and data come from, and manage risks across increasingly complex AI supply chains. 🔹 Monitoring & Validation Log, monitor, and continuously validate AI behaviour to detect drift, anomalies, and attacks. 🔹 Threat Modelling & Red Teaming Actively test AI systems against adversarial scenarios such as prompt injection and data poisoning. 🔹 AI-Specific Threat Protection Recognise that AI introduces new attack surfaces and requires controls beyond traditional cybersecurity. The shift is clear: 👉 We are no longer just securing systems 👉 We are securing data flows, model behaviour, and decision-making itself Organisations must translate this into clear, enforceable policies aligned to their AI architecture, to scale safely. Curious how others are aligning to emerging standards like ISO 27090.

𝐀𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐈𝐬 𝐧𝐨𝐭 𝐎𝐧𝐞 𝐓𝐨𝐨𝐥, 𝐈𝐭 𝐢𝐬 𝐚 𝐒𝐭𝐚𝐜𝐤 Buying one security product and calling your AI "secure" is like locking the front door while leaving every window open. Real AI security is six layers deep: 𝐋𝐀𝐘𝐄𝐑 𝟏: 𝐈𝐃𝐄𝐍𝐓𝐈𝐓𝐘 𝐀𝐍𝐃 𝐀𝐂𝐂𝐄𝐒𝐒 Purpose: Control who can access AI systems, models, and data. What it includes: Model APIs, internal AI tools, agent-level permissions. Key controls: - Role-based and attribute-based access - Zero-trust architecture - API authentication No identity layer means anyone or any agent can reach your models. 𝐋𝐀𝐘𝐄𝐑 𝟐: 𝐃𝐀𝐓𝐀 𝐏𝐑𝐎𝐓𝐄𝐂𝐓𝐈𝐎𝐍 Purpose: Safeguard sensitive organizational data before it is used by AI models. What it protects: Personally identifiable information, financial records, internal business data. Key controls: - Data masking - Tokenization - Encryption (in transit and at rest) 𝐋𝐀𝐘𝐄𝐑 𝟑: 𝐏𝐑𝐎𝐌𝐏𝐓 𝐀𝐍𝐃 𝐈𝐍𝐏𝐔𝐓 𝐒𝐄𝐂𝐔𝐑𝐈𝐓𝐘 Purpose: Defend AI models against malicious or manipulated inputs. Risks handled: Prompt injection attacks, data leakage through prompts, jailbreak attempts. Key controls: - Input validation - Prompt filtering - Policy enforcement - Rate limiting This is the layer most teams skip and where most AI-specific attacks happen. 𝐋𝐀𝐘𝐄𝐑 𝟒: 𝐆𝐎𝐕𝐄𝐑𝐍𝐀𝐍𝐂𝐄 𝐀𝐍𝐃 𝐂𝐎𝐌𝐏𝐋𝐈𝐀𝐍𝐂𝐄 Purpose: Ensure AI systems comply with regulations and internal policies. Framework coverage: GDPR, EU AI Act, ISO 42001. Key controls: - Audit logging - Risk classification - Decision traceability - Policy enforcement 𝐋𝐀𝐘𝐄𝐑 𝟓: 𝐎𝐔𝐓𝐏𝐔𝐓 𝐕𝐀𝐋𝐈𝐃𝐀𝐓𝐈𝐎𝐍 Purpose: Verify AI-generated responses before they are used or acted upon. Risks addressed: Hallucinated outputs, compliance violations, unsafe or harmful responses. Key controls: - Fact-checking mechanisms - Policy validation - Output moderation 𝐋𝐀𝐘𝐄𝐑 𝟔: 𝐌𝐎𝐍𝐈𝐓𝐎𝐑𝐈𝐍𝐆 𝐀𝐍𝐃 𝐎𝐁𝐒𝐄𝐑𝐕𝐀𝐁𝐈𝐋𝐈𝐓𝐘 Purpose: Continuously track AI system behavior in production environments. What it monitors: Usage patterns, response accuracy, model drift, latency. Key controls: - Behavior tracking - Audit logs - Performance monitoring 𝐖𝐇𝐄𝐑𝐄 𝐓𝐄𝐀𝐌𝐒 𝐆𝐎 𝐖𝐑𝐎𝐍𝐆 They invest heavily in Layer 1 (identity and access) and ignore Layers 3 and 5 (prompt security and output validation).  The result is a system that authenticates users perfectly but lets prompt injections and hallucinated outputs through unchecked. 𝐓𝐇𝐄 𝐏𝐑𝐈𝐍𝐂𝐈𝐏𝐋𝐄 AI security is a stack, not a tool.  Six layers, each protecting a different attack surface.  Miss one and the others can not compensate. 𝐇𝐨𝐰 𝐦𝐚𝐧𝐲 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐬𝐢𝐱 𝐥𝐚𝐲𝐞𝐫𝐬 𝐝𝐨𝐞𝐬 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐜𝐨𝐯𝐞𝐫? ♻️ Repost this to help your network get started ➕ Follow Sivasankar Natarajan for more #EnterpriseAI #AgenticAI #AIAgents

One of the hardest problems in securing AI agents isn't the AI itself. It's permissions. Traditional applications follow predictable code paths, so you can review the source, identify every API call, and grant exactly what's needed. AI agents don't work that way. They reason dynamically, choose tools at runtime, and operate at machine speed. If you give an agent a permission, you have to assume it will use it, whether you intended it to or not. That's a fundamentally different threat model than most teams are used to designing for.                     I wrote a new post on the Amazon Web Services (AWS) Security Blog that lays out three principles for building deterministic IAM controls around these non-deterministic systems. The first principle is to assume all granted permissions could be used. Design based on acceptable scope of impact, not just intended functionality. The second is to provide organizational guidance on role usage through session policies, permission boundaries, and SCPs so that security doesn't depend on individual developers making the right credential choice. The third is to differentiate AI-driven actions from human-initiated ones using IAM condition keys or session tags, so you can apply different rules depending on whether a human or an agent is behind the request.                                              These patterns apply whether you're running an AI coding assistant on your laptop or deploying agents on Amazon Bedrock AgentCore. The post covers deployment patterns, concrete IAM policy examples, and implementation guidance for both AWS-managed and self-managed MCP servers. If your team is adopting AI agents and you haven't rethought your IAM strategy yet, this is a good place to start. Blog: https://lnkd.in/eZZfTVCe Christopher Rae, Justin Criswell, CISSP, Himanshu Verma, Ryan Orsi, Brian Mendenhall, Jean-François LOMBARDO, Matt Saner #aws #aisecurity

AI security is quickly becoming a real architecture problem, not just a model problem. As more companies deploy copilots, agents, and AI-driven automation, the security stack needs to evolve around how these systems actually operate. Prompts, models, APIs, agents, and automated actions introduce entirely new control points. A practical way to think about the emerging Enterprise AI Security Stack is in four layers. 1. Foundations Identity and Access Data Protection Infrastructure Integrity Start by extending Zero Trust to AI workloads. Every model interaction, API call, and agent action should be tied to a verified identity with clear authorization. 2. Input and Processing Prompt Injection Defense API Security Agent Permissioning Treat prompts as an attack surface. Implement input filtering, strong API authentication, and strict permissioning for agents that can call tools or systems. 3. Output and Actions Output Filtering Monitoring and Anomaly Detection Incident Response Do not just trust model outputs. Monitor behavior for anomalies, filter unsafe responses, and build playbooks for AI-related incidents. 4. Governance and Intelligence Compliance Mapping Encryption and Key Management Risk Intelligence Track where models are used, what data they access, and how they are governed. Encryption, key management, and audit trails become essential. A few practical steps organizations can start with now: 1. Inventory where AI models and agents are already running. 2. Require identity-based access for all model APIs. 3. Implement guardrails for prompts and outputs. 4. Monitor AI systems the same way you monitor production infrastructure. 5. Define incident response procedures for AI failures or misuse. AI security will increasingly look like identity architecture plus runtime monitoring. The organizations that get ahead are the ones designing this intentionally instead of reacting after deployment. How are teams structuring AI security right now?

In the past few months, we've worked with partners who've run into the same challenge with AI adoption. They rolled out policies or guidelines without bringing people into the conversation first—no workshop, no consensus building, just documents that needed signatures or implementation. Unsurprisingly, the result was frustrated staff expected to enforce or follow rules they had no part in creating, and leaders facing resistance instead of adoption. Both AI policies and guidelines are critical for responsible AI adoption, but they have to be built intentionally, with stakeholders driving consensus, or they most likely won't work. After working with hundreds of districts, we've created the resource below. Here are the best practices we recommend. Policies are your compliance layer and are designed to protect your district. We suggest adaptations to existing: ✔️ Acceptable use policies ✔️ Data privacy/FERPA protections ✔️ Academic integrity standards ✔️ Cyberbullying policies (to add deepfakes) Guidelines are your change management layer. They are the "why" that brings people along. We recommend including the following in your AI guidelines: 💡 Vision for GenAI adoption across your district 💡 GenAI misuse/academic integrity response protocols 💡 GenAI chatbot and EdTech tool vetting processes 💡 Digital wellbeing, data privacy, and student safety practices 💡 Implementation tips and instructional supports 💡 AI Literacy training opportunities and expectations What matters most is that both policies and guidelines should be built with stakeholders, not handed down to them. They should evolve with feedback, evidence of impact, and technical advancements. In all of our guideline and policy development work, we always start with AI literacy. It's important to build foundational understanding across stakeholders so that when policies and guidelines are developed, people can contribute meaningfully to the process and understand the "why" behind what they're being asked to implement. Intentional stakeholder engagement isn't a nice-to-have. It's what we've seen drive adoption. #AIforEducation #GenAI #ChangeManagement #AI

Today, NIST released the initial preliminary draft of the Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile), a community profile built on NIST CSF 2.0 to help organizations manage cybersecurity risk in an AI-driven world. A key section of this draft is Section 2.1, which introduces three Focus Areas that explain how AI and cybersecurity intersect in practice: 1. Securing AI System Components (Secure) AI systems introduce new assets that must be secured; models, training data, prompts, agents, pipelines, and deployment environments. This focus area emphasizes treating AI components as first-class cybersecurity assets, integrating them into governance, risk assessments, protection controls, and monitoring processes. It reinforces that AI risk should not be siloed from enterprise cybersecurity risk management. 2. Conducting AI-Enabled Cyber Defense (Defend) AI is not just something to protect, it is also a powerful defensive capability. This area focuses on using AI to enhance detection, analytics, automation, and response across security operations. At the same time, it recognizes the risks of over-reliance on automation, model integrity concerns, and the need for human oversight when AI supports security decision-making. 3. Thwarting AI-Enabled Cyber Attacks (Thwart) Adversaries are increasingly using AI to scale phishing, evade detection, and automate attacks. This focus area addresses how organizations must anticipate and counter AI-enabled threats by building resilience, improving detection of AI-driven attack patterns, and preparing for a rapidly evolving threat landscape where AI is weaponized. Why This Matters Together, Secure, Defend, and Thwart provide a practical structure for aligning AI initiatives with existing cybersecurity programs. By mapping AI-specific considerations to CSF 2.0 outcomes (Govern, Identify, Protect, Detect, Respond, Recover), the Cyber AI Profile helps organizations integrate AI security into familiar risk management practices. This is a preliminary draft, and NIST is seeking public feedback through January 30, 2026. If your organization is building, deploying, or defending with AI, now is the time to review and contribute. 🔗 https://lnkd.in/e-ETZXH8