ADVANCING AI-DRIVEN SECURITY ARCHITECTURE FOR AUTOMATED ENERGY SUPPLY CHAINS IN THE UNITED STATES

Abstract

The modernization of United States energy infrastructure through artificial intelligence (AI), Industrial Internet of Things (IIoT), smart grids, cloud-integrated energy management systems, autonomous monitoring platforms, and digitally interconnected supply chain networks has significantly improved operational efficiency, predictive maintenance, and real-time decision-making across power generation, transmission, and distribution environments. However, the rapid digitalization of automated energy supply chains has simultaneously expanded the cyberattack surface, exposing critical infrastructure to increasingly sophisticated threats including ransomware, adversarial AI attacks, supply chain compromise, SCADA manipulation, insider threats, and large-scale data exfiltration. Energy systems now process enormous volumes of operational technology (OT), information technology (IT), and consumer energy usage data across interconnected cyber-physical ecosystems, making security resilience a national priority for the United States. This article presents a comprehensive analysis of AI-driven security architectures for protecting automated energy supply chains in the United States. The study evaluates machine learning-based intrusion detection systems, federated learning security frameworks, blockchain-enabled energy data governance, deep learning anomaly detection, and adversarial defense mechanisms for critical energy infrastructure. Threats are analyzed across five interconnected system layers including physical infrastructure, industrial control systems, communication networks, cloud analytics, and AI decision-making platforms. The article further examines alignment with U.S. regulatory frameworks including NIST Cybersecurity Framework 2.0, NERC CIP standards, Executive Order 14028, DOE cybersecurity guidelines, and CISA critical infrastructure directives. A multi-phase implementation roadmap is proposed to guide U.S. energy operators toward resilient, privacy-preserving, and AI-enhanced cybersecurity ecosystems. The analysis demonstrates that layered AI-driven architectures integrating federated learning, blockchain provenance, zero-trust networking, and adversarial robust deep learning models provide the most effective defense strategy for securing next-generation automated energy supply chains in the United States