Provenance logging is the practice of recording where data, model outputs, or derived artifacts came from, and how they were used. In security terms, it creates an evidence trail: which account requested an output, which system generated it, what prompt or input was involved, and whether the result was later copied, exported, or reused.
This matters because authorized use and abuse can look similar on the wire. In AI systems, repeated queries may be legitimate testing, but they may also be model extraction, distillation, or prompt harvesting. Provenance logs help defenders correlate access patterns, detect suspicious reuse at scale, and prove whether an output came from an approved workflow. They are also useful in incident response, where investigators need to trace leaked data, confirm source systems, and separate normal operations from misuse. Strong provenance logging is most effective when combined with authentication, rate limits, and integrity protection for the logs themselves.