Zero Trust Architecture Leveraging AI-Driven Behavior Analytics for Industrial Control Systems in Energy Distribution Networks

The growing digitization and interconnectivity of energy distribution networks have increased their vulnerability to sophisticated cyber threats, particularly within Industrial Control Systems (ICS). Traditional perimeter-based security approaches are no longer sufficient to address the evolving threat landscape. This review explores the integration of Zero Trust Architecture (ZTA) with AI-driven behavior analytics to enhance cybersecurity in ICS across energy distribution networks. ZTA, built on the principle of "never trust, always verify," requires rigorous identity verification, least privilege access, and continuous monitoring. When paired with artificial intelligence, behavior analytics can autonomously identify deviations from baseline operational behavior, detect anomalies, and preemptively respond to insider threats or advanced persistent threats (APTs) without manual intervention. This paper analyzes the challenges of legacy ICS integration, models for AI-driven behavioral profiling, trust scoring, real-time authentication, and policy enforcement mechanisms. Additionally, it examines use cases in power grids, substations, and SCADA systems, emphasizing regulatory compliance and resilience strategies. By synthesizing current literature, standards, and technological advancements, this review outlines a comprehensive framework for deploying intelligent Zero Trust solutions in the critical infrastructure sector. The study also identifies open challenges and future directions for scalable, AI-enhanced Zero Trust deployments tailored to operational technologies (OT).