AI in Cybersecurity: When It Works & When It Fails

Many cybersecurity vendors claim their products are "AI-powered," but this term has become overused and often lacks real meaning. An expert working in cybersecurity across regulated industries notes that AI is useful when the problem involves scale, but dangerous when it requires judgment. For instance, AI excels at dark web content classification. The dark web generates a huge amount of content across many platforms. Natural Language Processing, or NLP, can effectively classify posts by threat type and identify targeted sectors, because threat actors often use recurring language patterns. However, NLP cannot understand context like a human analyst. A human is still needed to assess the true meaning of a flagged post. Similarly, machine learning is valuable for detecting patterns in massive credential exposure datasets, such as the expert's own dataset of over 8.2 billion leaked credentials. It can identify password reuse and cluster stealer log entries. The bottom line: understanding where AI genuinely works, and where it falls short, is crucial for effective cybersecurity.