In July 2025, a tactical team of United States Marshals descended on the Tennessee home of Angela Lipps, arresting the fifty-year-old grandmother at gunpoint while she watched her young grandchildren. Her apprehension was not the culmination of traditional detective work, but the result of authorities placing undue confidence in an AI-based facial recognition system. An algorithm had linked a photograph of her face to a counterfeit military identification card used in a sophisticated bank fraud operation over 1,200 miles away in Fargo, North Dakota.

Spoiler: you are probably already using AI agents, even if marketing hasn’t yelled at you about it yet. Forget the dark ages of 2023 when large language models (LLMs) just confidently hallucinated fake server logs and nonexistent IP addresses. Today’s AI can spin up a virtual environment, navigate web pages, scrape data, and logically process what it finds. Let’s cut through the noise and talk about what “agents” actually are, how “Deep Research” operates, and how to spin up your own pocket investigator that doesn’t come with corporate safety bumpers.

“A core selling point of machine learning is discovery without understanding, which is why errors are particularly common in machine-learning-based science.” I could not resist the temptation to start this article with a quote by AI as Normal Technology – it captures the current state of AI-everything perfectly. Should investigators really trust black boxes running a set of non-deterministic algorithms and providing different results on every reroll? And can we still use such black boxes to automate routine operations? Let’s try to find out.

Artificial intelligence is everywhere – from phones that guess your next move to fridges that shop for you. It’s only natural to ask whether AI can help in a more serious domain: digital forensics, specifically password cracking. The idea sounds promising: use large language models (LLMs) to produce rules and templates for guessing highly probable password variants, prioritizing the most likely ones first. But in practice, things aren’t so straightforward.