Intelligent Risk Governance Framework for Critical Energy Logistics Integrating Cybersecurity, Physical Security, and Operational Decision Intelligence

Abstract

Emmanuel Emenike Ezeoba*

Critical energy logistics in the United States face a converging risk environment in which cyber intrusions, physical attacks, and natural-hazard disruptions propagate across previously siloed operational domains. The Colonial Pipeline ransomware event, the Moore County substation attack, and the cascading effects of Winter Storm Uri each demonstrated the inadequacy of single-domain governance architectures. Each incident exposed a common failure: domain-allocated governance cannot produce the integrated situational awareness that compound risk demands. This paper proposes the Intelligent Risk Governance Framework (IRGF), a synthetic governance architecture integrating cybersecurity, physical security, logistics planning, emergency response, contractor oversight, and operational analytics into a single decision-support model for energy logistics operators. Positioned as a complement to NIST CSF, IEC 62443, NERC CIP, and ISO 22301, the framework serves as the governance layer connecting them. The framework is developed through Design Science Research methodology grounded in historical case analysis of four anchor incidents. It combines Multi- Criteria Decision Analysis for risk prioritization, Bayesian Networks for probabilistic cross-domain dependencies, and Agent-Based Modeling for emergent failure modes. Validation used a modified three-round Delphi process with a twenty-four-member panel of operators, security practitioners, and federal partners. Inter-rater agreement on the validated requirement set was substantial.

The IRGF comprises three coupled layers: a Risk Sensing Layer ingesting cyber, physical, logistics, and contractor telemetry; a Decision Intelligence Engine that fuses these inputs through a Bayesian Network and produces a dynamic Resilience Score on a 0–100 scale; and a Governance and Response Layer activating emergency playbooks, dynamic logistics re-routing, and contractor interventions. Agent-Based Modeling stress-testing against three high-impact scenarios Colonial-class cyber-to-logistics cascade, Ukraine-class combined cyber-physical attack, and Uri-class natural-hazard cascade produced mean time-to-recovery improvements of 40, 44, and 29 percent respectively, with a cross-scenario mean of approximately 38 percent relative to a conventional siloed response architecture. Operator benefits include reduced time from detection to coordinated response, enhanced contractor accountability, and unified compliance evidence against federal frameworks, including alignment with U.S. national preparedness objectives under Presidential Policy Directive 21 and the National Infrastructure Protection Plan. The IRGF offers operators, regulators, and federal partners a unified model that translates convergent threat intelligence into coordinated, auditable action, advancing U.S. national preparedness and the resilience of energy logistics. It also offers a foundation for integrating next-generation capabilities such as digital twin simulation and quantum-accelerated probabilistic inference.

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