Introduction to identity and AI gateways
Treat the gateway as a controlled decision and evidence boundary, not only a reverse proxy.
Before you start
Module prerequisites: SAI-110. Check the exercise-specific tools and environment before running commands.
Module outcomes: Design identity propagation across AI services; Define local-first and approved-egress policy; Place prompt, file, tool, and response inspection controls. This lesson contributes to that sequence.
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.
Module worked example
A service account can query a model and also change its serving configuration.
- Apply the control
- Separate inference permission from runtime administration and test both allowed and denied operations.
- Expected result
- The query succeeds while configuration changes are denied to the inference identity.
- Troubleshooting
- Verify the effective workload identity and direct endpoint routes instead of relying only on gateway logs.
Practice and completion check
Choose one control from this lesson. Identify its actor, target, enforcement point and expected result. Describe an allowed case and a denied or failed case. For a runnable exercise, retain observed output and separate it from expected output. Record unresolved limitations before marking the lesson complete.
Your lesson progress
Completion is self-reported learning progress; it does not certify practical competence.
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