AI and machine learning are transforming predictive maintenance into a critical infrastructure defense capability.
Power plants, substations, transformers, transmission networks, and renewable-energy systems are increasingly interconnected, making equipment failure, cyberattacks, sensor manipulation, and operational disruption potentially far-reaching.
AI-driven predictive maintenance continuously analyzes equipment behaviour to identify anomalies, deterioration, abnormal loads, and emerging failures before they trigger outages.
Combined with digital twins, IIoT sensors, edge computing, and real-time monitoring, utilities can move from reactive maintenance to predictive resilience.
However, greater digitalization also expands the cyberattack surface.
Compromised sensors, manipulated AI models, or unauthorized access to operational systems could influence maintenance decisions and threaten grid stability.
Therefore, predictive maintenance must combine AI, cybersecurity, Zero Trust, continuous authentication, anomaly detection, and human oversight.
For critical power infrastructure, the objective is no longer simply preventing equipment failure—it is ensuring continuous, secure, and resilient energy delivery against both physical and AI-driven cyber threats.
