AI Risk Management Policy Template (2026 Guide)

The AI Risk Gap Most Organizations Have

AI introduces risks that traditional risk management frameworks weren't designed to address — model drift, algorithmic bias, data poisoning, unexplainable outputs, and third-party AI failures. Without a dedicated AI risk management policy, organizations apply general IT risk controls to AI-specific problems and create compliance findings in the process.

The EU AI Act requires organizations to conduct conformity assessments for high-risk AI systems. SR 11-7 requires documented risk management across the full model lifecycle. Both require the same foundation: a structured, repeatable risk management policy.

What an AI Risk Management Policy Must Cover

• Risk identification process — how AI use cases are surfaced for assessment
• Risk assessment methodology — criteria for evaluating likelihood and impact
• Risk tiering framework — low, medium, and high classifications with required controls
• EU AI Act risk classification — prohibited, high-risk, limited-risk, and minimal-risk categories
• Model validation requirements for high-risk AI use cases
• Ongoing monitoring obligations and review frequency
• Escalation paths and risk acceptance procedures
• Third-party AI risk requirements and vendor accountability

Critical Mistakes to Avoid

• Applying a single risk tier to all AI tools regardless of their function or data access
• Completing risk assessments once at deployment and never revisiting them
• No documentation — risk conversations without written records have no regulatory value
• Omitting third-party AI tools (ChatGPT, Copilot, Gemini) from the risk scope
• Conflating AI risk management with cybersecurity risk — they overlap but are not the same