AI Access Control & Identity
AI services need their own identity and access controls — which service can call which model, and with which permissions.
Overview
Beyond end-user permissions, an AI system has its own internal identity questions: which internal service is allowed to call which model, through which gateway, and with which tools available to it.
Where It Fits
Internal Service
Service Identity
AI Gateway (Policy Check)
Model / Tools
Key Points
- Service-to-service identity
- An internal service calling an AI gateway needs its own credential or role, distinct from the end user’s own authentication.
- Scoped model/tool access
- Not every internal service should have access to every model or every tool — access should be scoped per service, per least privilege.
- Centralized policy enforcement
- An AI gateway is a natural place to enforce these access policies consistently, rather than duplicating checks in every service.
Interview Question
Why does an internal service calling your AI gateway need its own identity, separate from the end user’s?
End-user authentication controls what a user can request; service identity controls what that internal service is itself allowed to do against the AI gateway — which models, which tools, at what rate. Without separate service identity, a compromised or buggy service has the same broad access as any other caller, which violates least privilege at the infrastructure level.
Explain It in 30 Seconds
AI access control extends identity and least-privilege thinking to the services calling an AI gateway, not just end users — each internal service should have its own scoped identity determining which models and tools it can access.
Real-World Stack
Technologies commonly used to implement this in production.