Natural hazards can degrade or corrupt sensor data, and distinguishing between hardware damage caused by Natech events and deliberately spoofed sensor signals presents challenges. Physics-based detection methods can provide valuable additional layers of protection.
A malicious actor can easily weaponize every vulnerability that a hurricane exposes. And the tools built to stop them (firewalls, intrusion detection systems, access controls) are structurally incapable of seeing it happen.
When a natural disaster strikes an industrial facility, the immediate threat isn't always the one that matters most. While the floods, earthquakes, or ice storms cause visible damage, the real danger often begins quietly, in the seconds that follow, when sensors start lying.
As we face the rise of Agent-centric AI—capable of spoofing data protocols, mimicking operator behaviors, and generating "statistically plausible" logs—we need a defense mechanism that lies beyond the reach of statistical inference.