Creating an AI Risk Register: A Practical Template

Why This Matters

An AI risk register tracks risks for each AI tool you use. For each risk: tool, description, likelihood, impact, current controls, and planned improvements. Review quarterly.

Under the EU AI Act, having documented AI governance demonstrates that your business takes AI compliance seriously. If regulators or clients ask how you manage AI use, pointing to established practices is far better than starting from scratch.

What to Include for Each Risk

Document: the AI tool and use case, a clear risk description, likelihood (low/medium/high), potential impact (minor/moderate/severe), current controls, residual risk level, planned additional actions, responsible person, and last review date.

Be specific. 'AI might be wrong' is too vague. 'AI chatbot might provide incorrect product safety information to customers' is manageable.

Common AI Risks

Consider these categories: accuracy risks (wrong answers), data privacy risks (sensitive data exposure), bias risks (unfair treatment of groups), dependency risks (can't function without AI), compliance risks (regulatory violations), reputational risks (customer trust loss), security risks (hacking or manipulation), and over-reliance risks (staff stop thinking critically).

Managing and Updating

Review quarterly and after any incident. When you add new tools, add risk entries. When you change how you use a tool, reassess risks. Track whether controls are working — if incidents keep happening despite controls, those controls aren't effective enough.

This article is for informational purposes only and does not constitute legal advice. Regulatory requirements change frequently — verify current rules with official sources. Built by Sawai Gyoseishoshi Office, Hiroshima, Japan.