Physics-informed AI is machine learning that is built to follow known physical laws, engineering rules, or system constraints instead of relying only on patterns in historical data. In power systems, manufacturing, robotics, and other critical infrastructure, this matters because the model’s output must stay plausible in the real world, not just look statistically correct.
From a cyber security perspective, physics-informed AI can reduce bad decisions in cyber-physical environments, but it is not automatically safe. Attackers may try to poison training data, manipulate sensor feeds, or distort model inputs so predictions drift away from physical reality. Defenders use constraint checks, simulation, and engineering reference models to validate results before they influence operations. In practice, the value of physics-informed AI is trust: it helps operators spot outputs that violate measurable limits, making both attacks and model failures easier to detect.