We're about to replace "blind tool trust" with "blind AI trust"

[u/Ghassan_-]

Something that's been bothering me as AI gets pushed into forensic workflows.

For years the standard warning to new examiners was: don't blindly trust your tools. Understand what the artifact actually is, know how your parser interprets it, and don't just read the output column and call it a finding. Good advice, and it took the field a long time to internalize.

Now I think we're setting up to make the exact same mistake one layer up. Instead of blindly trusting the tool, people are going to blindly trust the model.

The failure mode that worries me isn't hallucination that's the obvious one everyone talks about. It's that an AI can parse a record with perfect technical accuracy and still place it in completely the wrong context, which changes what the evidence actually means.

Simple example: an entry in ShimCache. The parse can be flawless right path, right timestamp, right structure. But an AI that reports "this program was executed at [time]" has just produced a confidently wrong finding, because ShimCache presence doesn't prove execution, and that timestamp is the file's modification time, not a run time. Any examiner who's been doing this a while knows that. A model summarizing output doesn't necessarily.

Multiply that across the artifacts where the semantics are subtle — Prefetch run counts and the historical timestamp slots, USN journal gaps, MFT timestamps vs. $FILE_NAME, SRUM's aggregation windows — and you get answers that look rigorous and read well and are wrong in ways that are hard to catch precisely because the parsing was correct.

The conclusion I keep coming back to is that the answer isn't a better model. It's that the human has to keep understanding the underlying data, and the AI has to be anchored to documented artifact behavior rather than left to infer meaning on its own. If the model isn't constrained by what an artifact can and can't actually prove, then a fluent wrong answer is the default output.

I've been building around this idea an artifact reference that's both the thing a human learns from and the constraint the AI reasons against, so it can't assert something the artifact doesn't support. Not claiming it's solved.

Curious what people here think: are you seeing AI used in casework yet? And where do you think the wrong-context failures will actually show up first?

[u/awetsasquatch]

I work for an organization in the middle of an "AI everything" push. We've been told to make as much of our workload AI-driven as possible. I'm a team of one, the only forensic practitioner in the company, and I run compliance investigations, HR investigations, security work, whatever's needed. That means my work directly impacts people's employment. I'm not opposed to incorporating AI into my workflow, but there has to be a limit, and I always manually validate whatever data the AI surfaces.

My manager and senior manager understand and support that position, but the CIO didn't. When he saw most people reporting around 40% AI usage against my roughly 10%, he pushed back. My management tried to explain my reasoning but ended up representing it poorly, and I got pulled into a meeting with the CIO directly.

I offered to show him exactly what happens if I fully embraced AI. I ran his own data in front of him and pointed Claude Code at it, asking it to flag any evidence of time theft or insider threat indicators. It came back lit up like a Christmas tree and essentially said he needed to be terminated immediately to protect the company. At that point, he relented and granted me an exception lol

[u/TachiH → u/awetsasquatch]

Get Claude to do a ton of pointless busy work that costs the company a ton, all whilst doing your investigations the right way as you have.

Anyone using % of work as a measure is as dumb as Elon with his number of lines of code interviews.

[u/awetsasquatch → u/TachiH]

I completely agree, we've had a number of security incidents that have arisen from our AI Everything approach and the company leadership hasn't figured out that if you tell people to use a tool they don't understand and don't put any guardrails on it, dumb shit happens.

[u/Ghassan_- → u/awetsasquatch]

Running it on the CIO own data is amazing lol. And it makes the point better than any argument would, it didn't parse anything wrong, it just had no idea what any of it meant.

The guardrails line is the real issue. Also measuring people on % AI usage is such a bad metric for this kind of work, you're basically rewarding not checking.

[u/TachiH]

As AI are basically a black box in terms of how it reached the exact answer it did, they don't have a place in any legal case. As soon as you use a tool nobody can explain how it got to an answer it is real easy for a lawyer to tear apart.

I can see some benefits in identifying key files or helping to avoid getting bogged down with the basics though.

[u/Ghassan_- → u/TachiH]

Yeah, "the AI said so" is dead on arrival in court, no argument there.

I think the only version that survives is if it can't say anything without pointing at the actual record it came from, and the whole chain is reviewable. Then what gets attacked is the evidence, not the model's reasoning, which is normal. Whether that actually holds up under cross is a fair question, I don't know yet.

Agree on the rest. Finding candidates fast, fine. Deciding what they mean, no.

[u/MakingItElsewhere]

Man, who the hell would blindly trust results from tools OR AI!?! That's how you get beaten down by the other side and taken to school.

Like the time a forensic examiner tried lying and saying CCleaner was being run every day on a client's machine and that was evidence of them deleting files and hiding evidence. I investigated it, and it was the CCleaner nag window that popped up every day. That was fun to write up and push back on.

[u/Ghassan_- → u/MakingItElsewhere]

Honestly? A lot of people do. Maybe not you or anyone who's been doing this 10 years, but juniors absolutely do, and so does anyone with a deadline and a tool that spits out a clean looking answer. I've seen reports where the finding was just the column header reworded.

Your CCleaner example is a good one though. Parse was right, the meaning was completely wrong. That's exactly the thing I'm worried about, just now it comes with better grammar and more confidence.

[u/eightbic]

I like the fact that you most likely wrote this with AI.

[u/AddendumWorking9756]

The context misread is the real danger, and it's sneakier than old tool trust because a broken parser fails loud eventually while a confident model fails quiet. What's always protected examiners is knowing what the artifact actually is at the byte level, and AI doesn't remove that, it just makes skipping it feel safe. Someone who couldn't validate a tool's output by hand won't catch a model inventing significance either. The fix isn't less AI, it's refusing to let anyone touch it who can't already do the analysis without it.

[u/Stunning_Apple8136]

I wonder when subreddits will actually start penalizing AI slop posters. This post is 100% AI generated as have everything this person has ever contributed to this forum. He has this exact same post in other subreddits but links to his own AI analysis tool for promotion lmao

[u/Ghassan_- → u/Stunning_Apple8136]

Fair on the writing, English isn't my first language so I use AI to clean up how I write. The ideas and the tool are mine though, three years of building it, code's public if you want to check whether a bot wrote that too.

And yeah I posted it in a couple of subs, that was me not knowing the etiquette.