AI governance defines not what the model can do but what the organisation permits. It's a management topic, not a technical one.
01. Why It's Needed
As AI use spreads across departments, who sends which data to which tool becomes untrackable. Governance is the framework that brings that sprawl under control.
02. Inventory
The first step is a list of AI applications in use: which process, which model, which data, who's responsible. Without that list no risk assessment is possible.
03. Usage Policy
Which data class may go to which tool, which decisions may be automated, which outputs need human approval. The policy must be short and workable; a thirty-page document nobody reads doesn't help.
04. Roles
There has to be an owner: a person or committee responsible for AI use, with legal, information security and business representation. An ownerless policy soon exists only on paper.
05. Risk Classification
Every use case should be classified by impact. A summarisation tool for internal efficiency cannot sit at the same scrutiny level as a system influencing hiring decisions.
06. Review
Review not annually but whenever a new tool is added and whenever regulation changes. This field moves fast on both the technology and the regulatory side.