Definition
The canonical answer.
AI governance is the documented control system around AI in production: written scopes per automation, hard bounds on what runs alone, logging, output verification against baselines, escalation paths, and named human accountability. Frameworks like the NIST AI RMF structure it, and concrete practice beats aspirational policy.
Worked example
In practice.
Each agent in a fleet carries a written scope stating what it may touch and what needs a person. Runs are logged, outputs verified monthly against measured baselines, and any failure is fixed as a class with a guard, not patched as an instance. That paper trail is what an enterprise buyer’s review actually asks for.
Where this work happens: Trust & Security
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