When a Prompt Becomes Methodology: AI-Assisted Forensics, Expert Discovery, and Operation-Level Provenance

In Villanueva v. Las Vegas Metropolitan Police Department, a federal district court in Nevada required disclosure of AI-generated case summaries considered by a testifying expert as “facts or data” under Federal Rule of Civil Procedure 26(a)(2)(B)(ii), while protecting research prompts, deposition-preparation materials, and AI-assisted draft content under distinct work-product and draft-protection rationales. The ruling raises an important question for digital forensics and incident response (DFIR): when AI-generated materials produced during an investigation are later considered by a testifying expert, how should methodological inputs, research outputs, and protected preparation materials be distinguished? This article proposes AI Operations Classification (AOC), a non-exclusive taxonomy of analytical functions performed by AI systems in forensic workflows. It separates operation type from delegated execution authority and proposes controls for contemporaneous recording, proportionate verification, and counsel-managed review. It also maps those controls to the Substrate-Layer AI Audit Methodology (SLAM) v1.0 without claiming conformance. The objective is auditability: a documented, reviewable path from source artifacts through material analytical operations to verified conclusions.