LLM-based forensics is the use of a large language model to help analysts review logs, alerts, chat transcripts, endpoint events, and other incident data. The model can summarize noisy timelines, cluster related events, extract probable indicators, and suggest questions for deeper investigation. It does not replace a human analyst, but it can turn large volumes of telemetry into a smaller set of leads.
This matters because modern intrusions generate too much data for manual review alone. During defense, an LLM may help correlate authentication failures with suspicious API calls, identify unusual sequences in pipeline logs, or draft a narrative of what likely happened. In attacks, the same capability can be used to speed up malicious analysis if exposed to sensitive telemetry. For that reason, organizations should keep incident artifacts in a controlled environment, validate model output against raw evidence, and treat LLM-assisted findings as hypotheses, not proof.