LLM gateway security logging
Useful LLM gateway logs capture enough metadata to reconstruct an investigation without retaining prompts or responses. This guide defines the minimum event fields, correlation records, retention priorities, and privacy boundaries for a security-ready gateway.
What should every gateway event contain?
Timestamp, credential or pseudonymous principal, model, token counts, cost basis, status, latency, source relationship, request correlation ID, and deployment or session linkage when available.
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
Should prompts be logged for abuse detection?
Usually no. Start with metadata and collect content only under a separate, justified policy.
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