AI agent access control

The control gap

Static scopes bundle many actions together and persist longer than the agent task. That creates standing access with little request-level context.

Map access at three layers: what the provider credential permits, what runtime policy permits, and what the agent actually uses. OAuth scopes or service roles remain the outer boundary, but they frequently bundle several actions and resources. Runtime controls can narrow authority inside that envelope while posture analysis identifies provider scopes and policy rules that should be reduced at their source.

Use negative selectors during testing: a similarly named tenant, a repository outside the delegation, a production environment reached with a staging identity, an amount just above a threshold, and a tool with a missing resource. Default behavior should be explicit when required attributes cannot be resolved. Keep denied calls and expired approvals so tuning and incident response see attempted reach, not only successful access.

What good looks like

Endram narrows platform credentials at runtime and binds exceptional access to one reviewed request.

• Action-level permissions
• Resource selectors
• Time and environment conditions
• Just-in-time approvals

A production workflow

1. Start with existing OAuth scopes
2. Identify sensitive actions inside each scope
3. Add deny or approval rules
4. Reduce platform scopes where practical

Evidence to require

• Scope-to-action gap analysis
• Requests blocked inside broad scopes
• Approval frequency
• Unused capabilities

Buyer checklist

• Can the product enforce a decision before the external tool executes?
• Can policy distinguish the agent, delegated user, tool, resource, and environment?
• Can reviewers see the exact requested action and approve it without broadening future access?
• Does every allow, deny, and approval retain the policy version and reason?