09/10/2026
A practical governance framework needs to answer those questions at the workflow level.
Take a simple example: an AI system might be allowed to read a customer record and prepare a recommendation. That does not mean it should be allowed to change the record.
That’s the distinction access control has to make - and it only gets sharper with AI agents.
A deterministic rule validates the recommendation. A permission layer restricts what the system can access. A human approves actions above a defined threshold. Logs record what the AI proposed, what the system executed, and who approved it.
The more authority an AI system has, the more governance needs to move from broad principles to specific controls:
→ data permissions
→ tool access
→ approval thresholds
→ exception handling
→ monitoring
→ rollback
→ audit evidence
The goal isn’t more AI policy. It’s making sure policy actually changes what the system is allowed to do.
How mature is AI governance in your organization - policy on paper, or controls embedded in production workflows?