Series context. Part 3 of the Forensics and Futures series examines how large language models (LLMs) are being integrated into forensic and investigative workflows. Following Part 2’s focus on forensic readiness and defensibility, this installment addresses where LLMs add value, where they introduce new risk, and how organizations should govern their use in 2026. [1]
Why LLMs Are Fundamentally Different From Traditional Forensic Tools
Large language models are being adopted in investigative environments for one primary reason: scale. Modern investigations routinely involve millions of messages, emails, documents, and transcripts. LLMs enable navigation of that volume within timeframes that human-only review cannot sustain.
Common capabilities include summarizing large text collections, clustering conversations, identifying recurring themes, and surfacing entities across datasets. These functions make LLMs attractive for reviewing chat logs, collaboration platforms, email corpora, document repositories, and transcription outputs.
The distinction that matters is this: LLMs do not extract evidence. They interpret content. Traditional forensic tools acquire artifacts and preserve them in a verifiable state. LLMs operate one layer above that process, generating linguistic interpretations rather than discovering new data.
