How do AI agents handle permissions and access control?
Well-designed AI agents act with the permissions of the person they are working for, so they only retrieve data and take actions that user is allowed to. Permissions should be enforced at the data layer, the agent should have its own scoped identity for auditing, sensitive actions should need approval, and every access and action should be logged.
Permission models compared
Key controls
- User-scoped access for retrieval and actions.
- Synced permissions from source systems, kept current.
- Agent identity (non-human identity) with least privilege.
- Approval gates for refunds, access grants, external messages.
- Audit trail of queries, data touched and actions.
- Role-based access for who can build and change agents.
Example
A sales rep asks the agent about a customer. The agent can see that customer’s tickets and CRM record but not HR files or board documents, because those permissions carry over from source systems.
