I have walked into some version of this scene at energy utilities, automotive plants and pharma sites across sectors and borders for a decade. The dashboards change; the “forgotten” laptop stays. This is the massive visibility gap that no Large Language Model can close. According to the 2026 Dragos OT Cybersecurity Year in Review, fewer than 10 percent of OT networks worldwide currently have meaningful network monitoring in place. In 30 percent of last year’s incident response cases, investigations started not with a detection alert, but with someone on the plant floor noticing that “something seemed wrong.”
If you are a C-level leader planning an AI-driven security strategy, you need to realize: your strategy won’t fail because the AI isn’t smart enough. It will fail because your most critical telemetry never reaches it.
The inverted CIA triad: Where AI hallucinates risk
In IT, we prioritize confidentiality, integrity and availability. In OT — operational technology — the triad is flipped: availability is everything.
This inversion is where AI-driven security tools quietly break. A model trained on enterprise telemetry — HTTP, DNS and Windows event logs — will look at a Modbus or PROFINET segment and flag perfectly normal industrial traffic as an anomaly. If that AI is wired into an automated response playbook, you’ve built a system that can shut down a production line faster than any hacker.
