Review AI Governance

AI governance is the system of authority, policies, roles, controls, evidence, and oversight used to direct and monitor how an organization develops, acquires, deploys, and retires AI.

Governance answers who may decide, what evidence is required, which uses are allowed, how risk is monitored, and who acts when conditions change or something fails.

AI crosses technical, legal, operational, ethical, and business boundaries. Without coordinated governance, responsibility fragments while risks and dependencies remain connected.

An organization requires named owners, use-case review, data checks, testing evidence, monitoring, incident procedures, and retirement criteria before an AI system enters production.

AI governance is not only a committee, policy document, or compliance exercise. It must shape actual decisions and operating behavior.

Governance connects authority, evidence, and action across the AI lifecycle.

* [[human-ownership|Human Ownership]] * [[responsible-ai|Responsible AI]] * [[model-risk-management|Model Risk Management]] * [[algorithmic-accountability|Algorithmic Accountability]] * [[state-transition-protocol|State Transition Protocol]]

* [NIST AI Risk Management Framework 1.0](https://doi.org/10.6028/NIST.AI.100-1) — consensus framework for governing, mapping, measuring, and managing AI risks. * [NIST AI RMF Core: Govern](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/) — official governance outcomes covering policies, accountability, roles, documentation, and lifecycle oversight.

* Compare broad governance with the risk discipline in [[model-risk-management|Model Risk Management]].