Cyber Digital Twin-Informed Zero Trust: A Synergistic Framework for Securing Operational Technology in Defense Logistics Infrastructure

Abstract

The convergence of Information Technology (IT) and Operational Technology (OT) has exposed critical infrastructure to cyber-physical threats that perimeter-based security was never designed to handle. The consequences extend beyond data loss or equipment malfunction: compromised OT systems directly degrade military readiness, endanger both warfighter and civilian lives, and create national security vulnerabilities near-peer adversaries are actively probing. Legacy OT environments—the systems governing logistics, utilities, and manufacturing across military supply chains—operate under assumptions about isolation and trust that no longer hold. This research presents a security framework that integrates Cyber Digital Twins (CDT), Artificial Intelligence and Machine Learning (AI/ML), and a Zero Trust Architecture (ZTA) framework to provide an integrated defensive capability for OT cybersecurity. The approach centers on a high-fidelity virtual replica of the OT environment, training AI/ML models to recognize both normal operational signatures and simulated attack signatures within that replica, using the resulting risk intelligence to drive dynamic ZTA framework enforcement. The concept of the operational signature, the distinctive behavioral fingerprint of a device, process, or communication patterns is central to this framework: the CDT establishes baseline signatures, AI/ML models detect deviations from those signatures, and the ZTA framework enforces containment when anomalous signatures are identified.