AI is already part of digital forensics and incident response work, with or without a policy in place to govern it. The issue is that these tools are growing more confident in their own output faster than practitioners are developing reliable ways to verify it. Without governance models and validation standards in place, that gap compounds and rarely stays contained to a workstation: an unverified finding can end up in a forensic report, a deposition, or in court.
At SANS, we created two new AI frameworks this year to help address this challenge: the Digital Forensics & AI Investigations Framework, which we mapped to the trusted standards established by the Scientific Working Group on Digital Evidence (SWGDE), and the Incident Response & AI Investigations Framework, which we mapped to the NIST Cybersecurity Framework 2.0. Both frameworks rest on the same principle: AI can accelerate investigative work, but the human examiner remains the final authority at every phase.
Where Validation Cannot Be Delegated
In digital forensics, validating an AI finding starts at the source file. A tool that doesn’t show its reasoning has already produced an unreliable result, regardless of the confidence score attached to it. A tool that cites the specific file it parsed — for example, a claim that browser history came from Chrome’s own database — gives an examiner somewhere to start. The claim is plausible at face value, but it still has to be confirmed independently before anyone treats it as fact.
I think of a high-confidence AI output the way I’d think of a third grader insisting the homework is finished. Saying it’s done isn’t the same as it being done correctly, and no amount of confidence changes that. The only way to know is to verify it, the way a parent checks the homework instead of taking their child’s word for it alone.
The mistake I see often in the field is treating AI as a single source of truth. Practitioner forums are full of the same exchange: Someone scans a file, a detector reports it as AI-generated, and that becomes the finding. The claim has no named source and no reasoning behind it, and an examiner without foundational skills has no way to catch that.
What the Record Has to Show
Documenting what produced a finding matters as much as checking it. Plenty of examiners say they don’t use AI, while machine learning has been running skin detection, facial similarity, and image categorization inside their forensic tools for years, generating the confidence scores they rely on. Naming the tool is sufficient when AI is built into a forensic product, since opposing experts can research the integration themselves. For a general-purpose model, the exact version needs to be on record.
Version history matters more than practitioners expect because casework and AI capability move on different timelines. I worked a case where the tool that would have helped with the log files didn’t exist at the time of collection. By the time I testified two years later, the technology had advanced well beyond what was available during the investigation, which is exactly the kind of gap opposing counsel can use against you without a clear record of what the technology could do at the time.
One Policy Cannot Govern Both Disciplines
Digital Forensics and Incident Response carry different risk tolerances, and a single AI policy written for one will fail the other. Incident response has more latitude by design, because responders aren’t scrutinized on every single bit and byte, and the organization needs to know what happened quickly. AI genuinely helps there, in detection and in surfacing activity a human might not readily see. Forensic examiners operate under the opposite pressure, since every source file requires validation, which is why many expect AI to add work rather than remove it. Both instincts are correct for the job in front of them, which is why the two frameworks exist as separate documents instead of one shared policy.
AI capability is moving faster than human oversight can keep up with. Offensively, AI is excelling; defensively, security teams remain more cautious and slow. Closing that gap during a live incident depends on how much an organization trusts its own tooling. If a team built and validated the AI making containment or triage recommendations, then a human in the loop, monitoring and intervening rather than approving every step, is workable. Requiring sign-off on every individual action during an active incident overwhelms an analyst quickly, exactly when speed matters most.
The Real Variable Is the Practitioner
Every phase of both frameworks returns to one constraint. AI should never take an action that can’t be undone. Evidence collection illustrates why: An investigator gets one opportunity at a sound acquisition, and a flawed one forecloses analysis entirely, no matter how skilled the analyst who comes after. Hash verification can be automated safely. Deciding how to pull cloud data, approach a scene, or deploy tools to capture lateral movement across other systems cannot be, because none of those decisions can be walked back. Legal authority follows the same logic. AI flagging that a subject has a linked cloud account is useful; AI collecting that data on its own crosses a boundary an examiner cannot undo, and some forensic tools can cross it on their own if configured to.
That’s the real argument behind both frameworks’ operational use cases and their course mappings by phase. Whether an investigation holds up depends on the practitioner running the AI. An examiner who understands the underlying discipline and uses AI on top of it becomes more valuable to their organization. An inexperienced person using AI is the landmine, and it’s leadership’s job to assess that risk honestly.
Building a Defensible AI Practice in DFIR
Frameworks like these are as dynamic as the technology they address, and they are only effective if practitioners have the most current information and the underlying skills to apply it, knowing where artifacts live before a tool tells you, understanding what a sound acquisition actually requires, and recognizing a wrong answer when a model produces one. SANS courses including FOR498: Digital Acquisition and Rapid Triage, FOR508: Advanced Incident Response, Threat Hunting, and Digital Forensics, and FOR608: Enterprise-Class Incident Response & Threat Hunting build that foundation across the phases both frameworks cover.
Join us at SANS Cyber Defense Initiative 2026 in December to attend FOR508: Advanced Incident Response, Threat Hunting, and Digital Forensics in person, taught by Carlos Cajigas.
Learn more about SANS’s DFIR training offerings here.
Note: This article has been expertly written and contributed by Heather Barnhart, SANS VP of Curriculum.
