Why AI Will Replace Every DFIR “Tool Operator” by 2027.

Experience is a brutal and effective teacher.

Early in my forensic career, I made a dumb assumption, that if I meticulously researched every artifact and followed the “best” procedures, my report would be bulletproof. I believed every sentence I wrote was true because I had earned that belief one artifact, one validation, one step at a time.

Then I spent hours in rooms where people weren’t trying to understand me; they were trying to break my work professionally and me personally. That’s the adversarial system.

If you do this work long enough, you will be humbled by the errors your report undoubtedly contains despite your best efforts. Not because you’re careless, but because reality is chaotic, data is incomplete, and there are more ways to be wrong than right.

Philosophers call this the Preface Paradox. You can be rationally justified in believing (1) each claim you wrote is supported by evidence, and (2) given the complexity and volume of the work, the report probably contains at least one error. Investigators should embrace it as a warning label.

AI* is about to make that calibration the defining skill of your career because AI doesn’t just accelerate output. AI accelerates confident mistakes.

AI can summarize mountains of text, cluster events, surface patterns, and draft timelines fast. Useful. But it can’t be accountable. It can generate a thousand claims with perfect grammar and absolute confidence, then integrates them into a story that reads like it was written by an expert analyst. And that’s exactly the trap. The better that the narrative is written, the easier it is to stop thinking critically while reading it.

This is artifact worship with better report formatting.

If you let AI do your thinking, you’re not an investigator. You’re a clerk copying and pasting text from one place to another. And when you’re sitting on the stand or in a hostile review, your AI model won’t be there to defend your wording being ripped apart. You will.

Toolwork is getting commoditized. Casework isn’t. It can’t.

A lot of DFIR careers have been built on being the person who knows the tool, runs the workflow, exports the report, and keeps the machine moving. Entire job listings consist of bullet-point lists of required or desired “tool” skills using specific tools.

AI compresses that work. Not all of it, but enough that the market will ask “Why do we need you…specifically?”

If your answer is “because I can operate the tools” or “I’m certified in the tools,” that’s weak sauce. AI is the ultimate tool operator.

You.will.never.beat.AI.in.tool.operation. 

“We are all, by any practical definition of the word, foolproof and incapable of error. Not in the slightest bit.” – HAL 9000 (2001: A Space Odyssey)

If your answer is “because I can investigate,” you’re on more solid ground, because the job is not producing output. The job is defending conclusions. That means bridging the gap between “the tool says X” and “the human did Y.” That bridge is judgment, sequencing, alternative hypotheses, and defensibility. AI can help you gather building materials. It cannot build the bridge for you.

Here’s the discipline I suggest adopting in every AI-assisted case: AI will tempt you to sign your name under its output. It will promise you that it is correct, that every word is spelled correctly, the math checks out, and your conclusions are solid.

Before you sign your name to what AI outputs, answer these questions.

First: What else could explain this? If you can’t name a plausible alternative hypothesis, you’re not investigating, you’re confirming.

Second: What would I expect to see if my conclusion is true, and do I actually see it? Predictions beat stories.

Third: Where am I most likely wrong? Identify the single most fragile assumption. If it breaks, what collapses with it? What if I am wrong?

Fourth: Am I incentivized to believe this? Client pressure, organizational pressure, ego pressure, time pressure? AI doesn’t fix those. It can amplify them. I’ll certainly be paying the price later for it.

Fifth: Could I explain this to a jury (or executive) without jargon? If you can’t explain it clearly, you don’t own it. If you don’t own it, don’t sign it, and don’t even print it.

These questions are survival.

Use AI for what it’s good at: summarizing, clustering, speeding up review, and drafting. But never let “AI said so” become your rationale. Treat AI output like a tip from an informant, that might be potentially useful, sometimes wrong, and never the final word.

My opinion on the near future of DFIR and AI

Tool operators will not be needed. AI replaces all of them.

Those who can prove cases will be in demand. Practitioners will not only continue tracing artifacts to persons, but will have to go a step further to glean the intention of the people who let AI loose (what did they tell AI to do and why?).

The key to survival is being able to do casework. Not artifact worship. Not masters of every known file system. But able to work cases that use artifacts and file systems as supporting evidence of an investigation. Tool operators will become a few who basically make sure AI is plugged in.

This is why I’m focused on pushing CASEWORK in DFIR. Most people don’t get real practice making investigative decisions under uncertainty until they’re forced to do it in a high-stakes setting, court, breach counsel, HR, audit, regulatory response. CASEWORK trains the part most people don’t train: how to move from technical traces to defensible conclusions without lying to yourself.

AI will help you find data faster and produce cleaner drafts faster. It will not give you judgment. It will not give you accountability. It will not defend your conclusions.

For the naysayers (not that they are wrong!), if an organization can solve 95% of its risks with automated AI instead of human tool operators, they will. And they will. Until AI because self-aware…DFIR Investigators will be in demand.

Casework is AI-proof. Casework is the mission. Toolwork is just the input.

• When I say “AI” here, I mean the whole umbrella of automation marketed as AI, classic machine learning, deep learning, LLMs and other “generative AI” models, copilots/assistants, retrieval-augmented systems, and so-called autonomous agents.

Spoiler alert!

It’s not much of a spoiler for a film released in 1968, but in 2001: A Space Odyssey, a human beats HAL 9000 the old-fashioned way: judgment, adaptability, and refusing to outsource the hard decisions.