AI gateway provenance and delegation
AI-gateway provenance is the ability to connect a billed provider request back to an authorized downstream workload. This guide covers the identity, delegation, and reconciliation records that turn ambiguous metadata into evidence suitable for an investigation.
What records establish request provenance?
Join an authenticated application session to a gateway request, provider invocation, billed usage, and an issued credential or signed delegation. The join must be time-bounded and auditable.
What should an operator do next?
Preserve the relevant time window, attach customer and deployment context, and make the smallest reversible response that contains credible risk. Record why a case opened so another investigator can reproduce the decision.
Where does this approach fail?
Behavioral metadata cannot identify intent or permission by itself. Legitimate launches, failover, automation, and registered relays can resemble misuse. Treat the output as a ranked investigation queue, then resolve authorization with stronger identity and reconciliation evidence.
Frequently asked questions
Why is a source IP insufficient?
It identifies a network path, not the downstream user, workload, or authorization decision.
Does InferTrail read prompts or responses?
No. The investigation design uses provider-visible metadata and customer context, with content collection governed separately if a customer requires it.
What is the right first action?
Open a verification case, preserve evidence, and check credential state before making a destructive enforcement decision.
Related guides
- LLM API key abuse detection
- AI gateway credential misuse
- Investigating unexplained AI usage spikes
- LiteLLM credential exposure response
- LLM token resale risk
- AI gateway provenance and delegation
- AI spend anomaly detection
- Authorized versus unauthorized AI usage
- LLM gateway security logging
- Credential sharing detection for AI platforms