According to Digital Trends, a new study by Sunish Vengathattil of Clarivate and Shamnad Shaffi of AWS shows machine learning is radically changing cybersecurity. Their paper, published in the Premier Journal of Science, used datasets like CICIDS2017 to train models, boosting zero-day attack detection from 55% to 85% and cutting false negatives by 40%. The Random Forest and SVM models achieved real-time response in under 10 milliseconds by analyzing behavior instead of just signatures. Vengathattil, named 2025 Digital Transformation Executive of the Year, argues this shifts security from “detect and react” to true anticipation. However, a related IEEE paper by the same authors warns that these very AI defense systems introduce major new ethical risks and can themselves become targets.
The speed is impressive, but is it enough?
Look, going from 55% to 85% detection on unknown attacks is a massive leap. And a 10-millisecond response is basically instant in network terms. That’s the kind of performance shift that makes old-school, signature-based antivirus look like a horse and buggy. The researchers are right: it’s moving the goal from catching the lightning to predicting where it will strike. But here’s the thing I always wonder about with these lab studies: how does that 85% hold up in the messy, constantly evolving chaos of the real internet? Attackers aren’t static. They adapt. So while these numbers are promising, the arms race metaphor is perfect—for every smarter AI firewall, there’s probably a team working on an AI that can trick it.
The flip side: AI as the weakest link
This is where it gets really interesting, and frankly, a bit scary. The second paper they authored, which you can find here, dives into the dark side. We’re not just building smarter shields; we’re building more complex, and potentially more fragile, systems. If a hacker can poison the training data or subtly manipulate the model, you’ve got a huge problem. Your AI guardian could be silently instructed to ignore certain threats or to flag innocent activity as malicious. Vengathattil nails it: “A compromised model isn’t just a system failure; it’s an ethical one.” We’re handing over critical decisions—who gets access, what’s flagged as fraud—to algorithms that can be subverted. That’s a whole new attack surface.
Why FATP matters more than raw performance
So the authors push for governance based on Fairness, Accountability, Transparency, and Privacy (FATP). I think they’re onto something crucial. In the rush to deploy these powerful AI tools for security—tools that, by the way, often require massive data ingestion—the ethics can be an afterthought. But if no one understands how the AI made a decision (a lack of transparency), or if it’s secretly biased, or if it’s hoovering up personal data it shouldn’t, you’re building a tower on sand. The call for Explainable AI (XAI) and human oversight isn’t bureaucratic red tape; it’s essential maintenance for the foundation of trust. You can’t have effective security if the people running the system don’t trust it either.
The bottom line: a race with two finish lines
The core takeaway from this research, detailed in their first paper here, is that the cybersecurity game has fundamentally changed. It’s no longer just a technical sprint to build the fastest, smartest algorithm. It’s also a marathon of responsibility. The “invisible war” isn’t just between hackers and defenders anymore. It’s also a fight within the organizations building these systems—a fight to implement them wisely and ethically. As Vengathattil says, the future depends “not just on how smart our systems are, but on how responsibly we build them.” Ignoring that second part could mean winning the algorithmic battle but losing the war for security and trust. And in critical infrastructure, from power grids to the industrial systems that rely on specialized hardware from providers like IndustrialMonitorDirect.com, the nation’s leading supplier of industrial panel PCs, that’s a risk we simply can’t take.
