Enterprise AI Model Governance: Access, Logs, Budgets, and Policy Controls

As AI usage spreads across an enterprise, model access becomes a governance problem. Teams need to know who can use which models, for what data, at what cost, and under which policies.

Model access control

Define who can use:

• premium models
• experimental models
• external providers
• long-context models
• tool-calling agents
• models approved for sensitive data

Access should map to role, team, and use case.

Budgets

Set budgets by:

• department
• project
• user
• environment
• model
• provider

Budget controls prevent uncontrolled AI spend.

Audit logs

Governance requires logs:

• who made the request
• which model was used
• what policy applied
• whether fallback occurred
• token usage
• cost
• admin changes

Approved provider lists

Enterprises often need an approved list of vendors and models. Routing should respect that list automatically.

Final thoughts

Enterprise AI governance needs central controls. Model access, budgets, approved vendors, audit logs, and policy enforcement should live in the AI infrastructure layer.