AI governance is the discipline of deciding how an organization will allow AI to be designed, bought, deployed, used, monitored, changed, and stopped—and making those decisions accountable, testable, and reviewable.   It is not simply an AI policy, an ethics statement, a security checklist, or a compliance document. Governance connects decisions to owners, controls, evidence, and consequences. That distinction becomes important as soon as an AI system moves beyond experimentation. Imagine an internal document assistant that answers questions about company procedures. At first, the system may only retrieve documents and generate answers. Later, someone connects it to a business system so it can update a record or send a message. The model may be unchanged, but the governance question has changed dramatically: who authorized the new action, what can the system touch, when must a person approve it, what gets logged, and who can stop it if something goes wrong? That is the practi...