Why AI Confidence Is Not Validation

AI is already being used in digital forensics and incident response, even without policies governing its use. As these tools become more confident in their output, the gap between their confidence and reliable verification grows, and without proper governance, that gap often expands beyond a single workstation. This can lead to unverified findings appearing in forensic reports, depositions, or even court proceedings.

To address this challenge, SANS created two new AI frameworks this year: one for Digital Forensics & AI Investigations, mapped to standards set by the Scientific Working Group on Digital Evidence (SWGDE), and another for Incident Response & AI Investigations, mapped to the NIST Cybersecurity Framework 2.0. Both frameworks rely on the principle that human examiners remain the final authority throughout the investigative process.

When validating an AI finding, the verification process should start with the source file. A tool that does not show its reasoning has already produced an unreliable result, regardless of the confidence score. A tool that cites specific files parsed, such as browser history from Chrome's database, provides a starting point for verification.

However, even a high-confidence AI output should be independently confirmed before being accepted as fact. The mistake often seen in the field is treating AI as a single source of truth. Examining forums frequently discuss scanning files and receiving AI-generated reports, treating the claim as fact without any named source or reasoning, and without a skilled examiner to catch the lack of validation.

It's crucial to document what produced a finding, just as much as checking the fact. While some examiners claim they do not use AI, machine learning models have been integrated into forensic tools for years, generating confidence scores that practitioners rely on. When AI is built into a forensic product, naming the tool is sufficient, as opposing experts can research the integration.

For general-purpose models, the exact version must be recorded, as casework and AI capabilities move at different paces. In Digital Forensics and Incident Response, risk tolerance differs, leading to the need for separate AI policies for each. Incident response has more flexibility, as responders are not scrutinized for every detail, allowing AI to aid in detection and surfacing activity that humans might overlook.

Forensic examiners, however, operate under stricter scrutiny, requiring validation of every source file. AI can enhance incident response by aiding in detection and surfacing activities humans might miss. Forensic examiners, on the other hand, face the opposite pressure, as every source file requires validation. This is why the two frameworks exist as separate documents, rather than one shared policy.

AI capability is advancing faster than human oversight can keep up, both offensively and defensively. During live incidents, closing the gap depends on an organization's trust in its own tooling. If a team built and validated the AI making containment or triage recommendations, a human in the loop, monitoring and intervening rather than approving every step, can be effective.

Requiring sign-off on every individual action during an active incident would overwhelm analysts, defeating the purpose of speed. Every phase of both frameworks emphasizes that AI should never take an irreversible action. Evidence collection serves as an example: investigators have only one chance at a sound acquisition, and a flawed one can prevent further analysis, regardless of the skill level of the analyst who follows.

Hash verification can be safely automated, but deciding how to pull cloud data, approach a scene, or deploy tools to capture lateral movement across systems cannot be, as these decisions cannot be undone. Legal authority also follows this logic; AI flagging a subject's linked cloud account is useful, but AI collecting that data without human approval crosses a boundary that an examiner cannot undo.

The real argument behind both frameworks lies in operational use cases and their mapping by phase. The effectiveness of an investigation hinges on the practitioner's understanding of the underlying discipline and their ability to apply AI effectively. An experienced examiner, who understands the discipline and leverages AI on top of it, becomes more valuable to their organization.

An inexperienced person using AI without the necessary skills is a landmine, and it is leadership's responsibility to assess that risk honestly. These frameworks are dynamic, evolving alongside the technology they address, and they remain effective only if practitioners have access to the most current information and the foundational skills to apply them, understanding where artifacts reside before a tool indicates so, knowing the requirements for a sound acquisition, and recognizing a wrong answer when a model produces one.