AI governance does not need to begin with a large policy program before there is a real use case. Start with use-case ownership, risk, data boundary, approved models, access, evaluation and approval evidence, then mature controls as risk and adoption grow.
1. Define
Identify owner, business purpose, data boundary, risk level and approval authority.
2. Enforce
Connect policy to identity, access, model registry, tool permission and environments.
3. Assure
Use evaluation gates before release and keep request/version trace plus approval evidence.
4. Scale
As the portfolio grows, add catalogue, reporting, portfolio oversight and more formal governance review.
Frequently asked questions
Must enterprise AI policy be complete before a PoC?
Not in every case, but minimum controls should match the PoC risk and the data involved.
What is a model registry for?
It records approved models and versions plus information required for release and audit.
What is an evaluation gate?
A quality, safety or acceptance checkpoint before a model/application version is approved for the next environment.