Introduction to identity and AI gateways

A gateway is a control boundary

AI gateways sit between clients and models, knowledge services, or tools. They can authenticate, authorize, inspect, transform, limit, route, approve, record, and block requests and responses. Their value comes from trustworthy policy enforcement and evidence, not from centrality alone.

A gateway must not become a universal bypass account or a hidden place where identity is lost. Downstream services still enforce resource-level authorization.

Policy surfaces

Model prompts, uploaded files, retrieved context, model selection, tool calls, endpoint routes, external egress, responses, streaming, metadata, and administrative actions may each need different controls.

Design principles

• Preserve actor, workload, tenant, purpose, and delegation context.
• Make allow, deny, redact, route, require-approval, and failure behavior explicit.
• Keep policy versions and reason codes in decision evidence.
• Deny unsafe fallback when identity or policy cannot be trusted.
• Test bypass through alternate endpoints, protocols, admin paths, and retries.
• Minimize sensitive content in gateway logs.

Module outcome

You will create an identity flow, policy decision table, inspection test suite, egress and route design, release gate, and evidence model.