AI usage governance is the set of policies, responsibilities, controls, and evidence used to determine and verify how people, applications, and agents may use AI in an organization.
This operational definition focuses on activity in its working context. The relevant decision covers a purpose, users or acting identities, an application and configuration, information processed, and permitted uses of the result. An application's approval can remain unchanged while a particular activity requires another decision.
What a usage decision covers
Permission to use an application does not specify every permitted input or recipient of its output. A usage decision needs enough scope to explain which activity is allowed and under what conditions. It should identify who can resolve questions or approve changes to those conditions.
The NIST AI RMF Playbook, MAP 1.1 emphasizes intended purpose, users, context, and assumptions. Usage governance applies that attention to the activity being performed, rather than using the application's name as a complete description of the use.
How the conditions take effect
Organizational decisions establish permitted purposes and handling rules. Procedures and technical controls apply the relevant conditions where the workflow can affect data or systems. Some decisions concern submitting information; others concern reviewing or distributing the result. One approval does not silently substitute for the other.
Evidence helps determine whether the activity fits the permission and whether the relevant conditions were applied. Where the available records do not establish a fact, that uncertainty remains part of the review. Verification here does not mean that every condition is automatically evaluated by software.
Changes to the data, audience, account, feature, or task can require reconsideration of the approved scope. Keeping the same model or application does not answer those questions by itself.
What the term does not establish
Usage governance is broader than maintaining an application inventory or publishing an acceptable-use policy. Neither establishes how all actual activity was handled. The label also does not guarantee complete discovery, prevention, compliance, or visibility across every access path.
Related concepts
- AI governance covers the broader organizational decisions about developing, deploying, and using AI.
- Shadow AI concerns use outside organizational approval or management. Incomplete discovery evidence should remain distinct from confirmed unapproved use.
The AI usage governance pillar explains how these decisions apply to people, applications, agents, inputs, and outputs in enterprise workflows.