AI-Enabled Digital Twins for Predictive Cyber Risk Validation and Operational Resilience in U.S. Critical Infrastructure Systems

As digital transformation accelerates across U.S. critical infrastructure systems, they are not only becoming more efficient but are also becoming larger, more interconnected cyber-physical systems with greater cyberattack surface. Traditional cybersecurity solutions are largely based on reactive threat detection and responses after a breach, which prevents them from detecting cyber threats and maintaining operational resiliency against advanced attacks. The purpose of this study is to introduce the concept of an Artificial Intelligence (AI) powered Digital Twin framework to support the predictive cyber risk validation and operational resilience within U.S. critical infrastructure systems. The proposed framework combines the real-time data of Industrial Internet of Things (IIoT) devices, Supervisory Control and Data Acquisition (SCADA) systems, Industrial Control Systems (ICS) and network monitoring systems to create a virtual model of the physical infrastructure that is continually updated. The Digital Twin includes AI models and algorithms for anomaly detection, cyber risk prediction, attack path analysis and adaptive threat assessment. Moreover, the framework allows for the safe simulation of various cyberattack scenarios such as ransomware, distributed denial-of-service (DDoS), malware propagation, insider threats and advanced persistent threats (APTs) to determine vulnerabilities in the system and test cybersecurity strategies before deploying them to operational systems. The framework uses resilience-based metrics including Cyber Risk Score (CRS), Operational Resilience Index (ORI), Recovery Rate (RR) and Service Availability (SA) to quantify infrastructure preparedness. Additionally, it has a continuous learning mechanism that integrates new operational data and threat intelligence into the AI algorithms, making them more adaptive and resilient to new cyber threats. This indicates that combining AI and Digital Twin technology can greatly improve the predictive cyber risk assessment, decrease attack propagation, boost operational continuity and provide better infrastructure resilience than traditional cybersecurity methods. The proposed framework is scalable and intelligent, serving as a practical basis for future research on AI-driven cyber-resilience in complex cyber-physical environments and helps provide a decision support solution for securing U.S. critical infrastructure.