Policy-Based AI Control
Define what AI can and can't do across your organization. 24 policy types with inheritance from org to user level.
Read the DocsThe problem with AI governance today
Your security team says no to AI. Your engineers say they need it. The result is shadow AI that nobody controls.
Zentinelle policies let you say yes with guardrails. Define what is allowed, what is limited, and what is blocked. Set org-wide defaults, let teams customize within bounds, and give specific users the access they need.
Built for how real organisations work
How the inheritance model works
Policies cascade through five levels: Organization → Team → Deployment → Endpoint → User
Set org-wide defaults. Let teams tighten or (with permission) loosen. Grant specific users elevated access. The most specific policy wins.
No manual per-user configuration. No policy sprawl. No gaps.
Organization
Global defaults for all agents
Team
Tighten or loosen within org bounds
Deployment
Per-product overrides
Endpoint
Single agent configuration
User
Individual access grants
Real-time evaluation
Agents call the /evaluate endpoint before taking action. Zentinelle resolves the effective policy in milliseconds.
Allow, block, or warn — you choose the enforcement model. Blocked actions are logged. Warnings are surfaced. Everything is auditable.
Policy versioning
Every policy change is versioned. See who changed what, when. Roll back if needed.
Git-like version control for your AI governance. Because "who approved that?" should have an answer.