AI governance becomes practical when decisions, ownership, evidence, and review triggers are explicit. The goal is to make responsible operation repeatable rather than dependent on memory.
Record the decision and owner
Record the business purpose, system owner, data boundaries, human review expectations, testing evidence, known limitations, and conditions that require escalation or re-review.
Set review triggers
Require review when the model, vendor, data source, permissions, autonomy, business consequence, or observed performance changes materially. Time-bounded approvals help prevent outdated assumptions from becoming permanent.
Practical takeaway
AI Governance Incident Records: Preserving Evidence When AI Behavior Goes Wrong should produce a small set of evidence that another person can review: the owner, the rule or decision, the test result, unresolved exceptions, and the next review date. That evidence turns a good intention into an operating control.