Model governance turns organizational responsibility into documented decisions and controls across a model's lifecycle. It can cover evaluation requirements, access rules, release approval, incident reporting, monitoring, version records, and the people accountable for each decision.
Governance becomes harder when a model is distributed beyond one provider's systems. Policies that depend on centralized access can lose force, so release terms, technical safeguards, external oversight, and clear downstream responsibilities may all be needed.
ELI5
Model governance is the set of rules and responsibilities for deciding how an AI model is built, tested, released, and controlled. It makes clear who can approve important actions and who must respond when something goes wrong.
For example, an organization might require an independent safety review before a powerful model can be released. The governance process names the reviewer, records the evidence, and identifies who can stop the release if the requirements are not met.
