Investigating unexplained AI usage spikes
An AI-usage spike can be customer growth, a retry loop, an agent failure, a model migration, or credential misuse. This guide provides a practical investigation sequence that starts with the operational question, narrows the time window, and preserves content privacy.
What should I check first when spend jumps?
Establish when the change began, which credential and model contributed, whether errors or retries rose, and whether a deployment or incident explains it. Compare both requests and tokens: they fail in different ways.
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
Is a spend alert enough?
No. Spend is an outcome. The investigation needs the workload, credential, model, and deployment context behind it.
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