Tackling Cyber Espionage in Critical Energy Sectors Using AI-Driven Zero Trust Systems

Cyber espionage has become a key threat to the energy infrastructure, which targets supervisory control and data acquisition (SCADA) systems, smart grids, and distributed energy resources to steal sensitive information, sabotage operations, or undermine operations over the long term. Perimeter-based security architectures are inadequate in the case of sophisticated and persistent adversaries that advance laterally, utilise insider access and exploit the compromised supply chain. In response, this research is proposing an AI-driven Zero Trust Architecture (AI-ZTA), that is unique for critical energy sectors. The proposed framework eliminates implicit trust by implementing continuous authentication, dynamic risk scoring, micro-segmentation, and real time anomaly detection. A policy enforcement point is combined with behavioral analysis engine based on LSTM-RNN in order to calculate each access request against a dynamically updated trust score. In addition, the blockchain technology is added to ensure an unalterable record of access and policy choices. A simulation was performed using a user created virtual SCADA environment of over 1000 devices and based on energy specific cyber attack scenarios were tested. The results show that AI-ZTA cut average time to resolve attacker dwell by 36 hours to under 3 hours, grow the precision of anomaly detections by from 71% to 93% and significantly reduce false positives. These results serve to highlight the framework’s capacity to presciently identify and address threats with a minimum of interference to operations. The combination of AI with Zero Trust technologies improves cyber resilience not only, but also promotes regulatory compliance and operational continuity in regard to the changing cyber espionage strategies. Finally this research presents defense paradigm of AI-ZTA as a robust and scalable for the future security requirements of the energy sector.

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