AI Security Should Learn From Industrial Functional Safety

Gregory Shahnovsky is CEO of Modcon Systems, specializing in process analytics and AI-driven optimization for complex industrial operations.

Artificial intelligence is moving rapidly from the information domain into the operational world. Until now, enterprise AI systems analyzed data, generated content or supported human decisions. That is changing.

AI agents can now access software systems, execute workflows, interact with connected devices and perform actions with consequences outside the digital environment. It is reasonable to expect that AI will soon participate directly in operational decision-making.

This changes the nature of the risk. Once AI can influence physical equipment, industrial processes or critical infrastructure, conventional IT security methods are no longer sufficient on their own. Protecting identities, networks, software and data remains essential, but what happens if AI makes the wrong decision, is manipulated, exceeds its authority or creates a hazardous physical intervention?

That is fundamentally an operational technology and functional-safety problem. High-consequence AI should therefore draw more heavily from established industrial safety engineering.

Industry Has Already Faced This Problem

Industrial automation has never assumed that instruments, controllers or operators will always behave correctly. Process plants and other production facilities use independent layers of protection because failures are expected to occur.

IEC 61508 formalized this approach through Safety Integrity Levels, from SIL 1 to SIL 4. The higher the required SIL, the greater the required risk reduction and the more demanding the design, verification and independence of the safety function.

The lesson for AI is not to copy SIL terminology directly, but to apply the same principle: The greater the possible consequence of failure, the stronger and more independent the protection must be.