RDFR-CI: Risk-Driven Deployment and Forensic Readiness for Governing AI Authority in Critical Infrastructure Protection

Critical-infrastructure protection must distinguish the detection capability of artificial intelligence (AI) from its permission to act on physical processes. This article proposes Risk-Driven Deployment and Forensic Readiness for Critical Infrastructure (RDFR-CI), an action-specific framework that adds the consequence of an incorrect defensive action and execution constraints to a published AI security operations center model. A weighted score of threat exposure, detection gap, AI-specific risk, forensic-readiness risk, and consequence assigns a provisional authority level; safety, availability, forensic, AI-assurance, and human-authorization gates can only restrict it, and demonstrably prohibited actions are stopped first. A consequence-driven score floor imposes an authority ceiling, and delegation is scoped. Under joint perturbation of inputs, weights, and band boundaries, 25 of 30 synthetic scenarios retain their final authority level with probability ≥0.90. On three releases of the hardware-in-the-loop (HIL)-based Augmented Industrial Control System (ICS) Security (HAI) dataset (128 held-out attack episodes) and, with pre-specified predictions, the BATADAL water-network benchmark, the detection gap or the assurance gate limited autonomous execution to the Shadow/restricted level in 13 of 15 settings. Simulated defensive actions showed that an incorrect action’s consequence depends on the action and the asset. Exposure, AI-specific risk, forensic readiness, policy parameters, and field outcomes remain unvalidated.