“If I had a world of my own, everything would be nonsense. Nothing would be what it is because everything would be what it isn't. And contrary wise, what is, it wouldn't be. And what it wouldn't be, it would. You see?” (Lewis Carroll, Alice's Adventures in Wonderland)
Introduction
An Artificial Intelligence is, basically, the association between an algorithm (or algorithms) and a set of data prepared for decision making. The algorithm is trained on this database and learns from that data. There are a variety of AIs applied from pattern recognition to the creation of images from certain guidelines passed to an algorithm by the end user. With some freedom, the user can create any image by inserting some guidelines. For example, with the guidelines "Monet", "impressionism" and "painting", an AI can create an image. In a scarier scenario, a video can be created by making people believe that a famous actor is "really" in that scene when it's all just a fake video: someone created the video, and the AI superimposed the face of the famous actor over the face of the creator of the video.
I. AI: Hazards and Efficiencies
In the United States, concern about the uncontrolled advance of AI gained ground with the testimonies of representatives of Google and IBM that took place a few days ago on the need to establish controls in AI usability, application, and development. On the other hand, controlled AI can serve as a brake....
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Author
- Paulo Pereira is an independent malware analyst, Cyber Security Professional, EXIN Instructor.
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Great article! It’s fascinating to see how AI is becoming a tool, not a replacement, in digital forensics. I really liked the point about balancing automation with human judgment. For me, the key is using AI to handle the heavy data lifting so analysts can focus on the complex thinking, just like the commenters discussed.
Exactly. The real win is AI identifying “negative evidence” — things humans subconsciously filter out. It shifts our role from data processing to rigorous hypothesis testing.
Interesting read — especially the part about balancing AI automation with human judgment in digital forensics. I work on the compliance side of security projects, and lately we’ve been discussing how AI tools might change how analysts are trained and evaluated. I’m genuinely wondering though: have there actually been serious studies on this — who ran them and did they show any measurable improvement in analysts’ accuracy or decision-making over time?
Yeah, NIST and EU labs ran studies – AI boosts analysts’ accuracy 20-30% with training. Stats here: https://aristeksystems.com/blog/ai-powered-learning-key-statistics-on-its-growing-impact/ Game-changer!
That 20–30% accuracy bump you mentioned really grabbed my attention. I’ve seen AI tools help a lot with evidence triage and pattern spotting, but there’s always that balance between efficiency and over-trust. Sometimes people start leaning on the system instead of double-checking. Do you know if those studies were lab simulations or real casework, and whether they tracked long-term effects on analysts’ judgment or training habits?
Mixed, Serge. Lab sims mostly, but UK/US agencies are testing live triage. Main takeaway: accuracy peaks with human oversight, not solo AI. Data shows it sharpens focus but requires constant audit.
Lana, the real shift isn’t just accuracy—it’s the ‘mental load’ reduction. Studies from MIT/Oxford show AI excels at spotting latent patterns, but the human still owns the final ‘intent’ deduction. The hidden risk is automation bias: where analysts stop questioning the output. I’ve seen teams succeed only when AI acts as a ‘second opinion’ rather than a primary filter. It’s less about a 30% bump and more about preventing the fatigue that leads to critical misses in hour ten of a shift.