Build an Operational AI Risk Register

Begin with system context

Record purpose, users, affected non-users, deployment environment, data sources, model and provider, tools, integrations, decisions influenced, and prohibited uses. A generic list of “bias, privacy, security” cannot guide a release decision without this context.

Include foreseeable misuse and failures: exclusion, misleading output, privacy loss, manipulation, over-reliance, unsafe action, denial of service, intellectual-property issues, and unequal performance. Invite domain, security, legal, operations, accessibility, and affected-user perspectives.

Turn risks into testable records

Each entry needs scenario, affected people, cause, impact, likelihood rationale, current evidence, preventive control, detective control, owner, deadline, residual risk, and decision. Link controls to executable tests, review artifacts, logs, policies, or contracts.

Do not mark a risk “mitigated” because a policy exists. State how compliance is measured and what happens when the threshold is missed.

Keep it alive

Review when data, model, prompt, retrieval source, tool permission, user group, geography, regulation, or workflow changes. Add production incidents and near misses. Archive decisions with the evidence available at the time.

Practice: five-scenario register

For one AI feature, write five scenarios across quality, fairness, privacy, security, and misuse. Assign one owner and one measurable control to each. Run a cross-functional review and document disagreements instead of averaging them away.

Release gate

A release is defensible when critical risks have evidence, residual risks are explicitly accepted by an authorized owner, monitoring exists, and users have a practical recovery path.