SANS Introduces Frameworks for AI in Digital Forensics and Incident Response

The Challenge of AI Confidence in Forensics

AI is increasingly integrated into digital forensics and incident response, often without clear governing policies. A significant issue arises as AI tools become more confident in their outputs faster than practitioners can develop reliable verification methods. This creates a gap where unverified AI findings can lead to inaccuracies in forensic reports, depositions, and court proceedings.

SANS Introduces New AI Frameworks

To address the verification challenge, SANS developed two new frameworks: the Digital Forensics & AI Investigations Framework and the Incident Response & AI Investigations Framework. The digital forensics framework aligns with the Scientific Working Group on Digital Evidence (SWGDE) standards, while the incident response framework maps to the NIST Cybersecurity Framework 2.0. Both frameworks are built on the principle that AI can speed up investigations, but human examiners must retain final authority.

The Importance of Human Validation

Validating AI findings in digital forensics begins by examining the source file. Tools that do not provide their reasoning for a finding produce unreliable results, regardless of their confidence score. A tool that cites a specific parsed file, such as a browser history from Chrome's database, offers a starting point for an examiner to independently confirm the claim before accepting it as fact.

Avoiding AI as a Single Source of Truth

A common error in the field is treating AI output as definitive truth. For example, a file scanned by a detector might be reported as AI-generated without a named source or reasoning. An examiner lacking foundational skills may not be able to identify or correct such an unverified claim. Documenting the origin of a finding is as crucial as verifying it.