AI Incident Review

AI incidents are rarely only model incidents. They often combine data changes, unclear ownership, missing tests, and weak rollout controls.

Review structure

1. Timeline of detection, mitigation, and recovery.
2. User or business impact.
3. Data, model, serving, and governance contributors.
4. Detection gaps.
5. Prevention actions with owners.

Useful outcome

The review is successful when it changes a monitor, test, contract, or rollout rule. A meeting with no operational change is just documentation.

Follow-up artifact

Keep a short launch-readiness diff: what would have caught this before release?