Clinical-Protocol-Aware Cyber Resilience for Connected Clinical Technologies Under AI-Enabled Adversarial Conditions

Abstract

Healthcare increasingly relies on connected medical devices, clinical information systems, cloud services, and distributed-care technologies, creating cybersecurity risks across clinical workflows. AI-enabled capabilities can accelerate vulnerability discovery and attack-path analysis, increasing the need to limit compromise propagation. This paper proposes a clinical-protocol-aware cyber-resilience framework based on bounded clinical micro-networks and controlled interconnections. The framework integrates clinical factors into cybersecurity design, management, and evaluation, covering prevention, containment, and incident response. A parameterized Monte Carlo simulation distinguishes technical cyber exposure from modeled clinical consequence. Experiments investigate architectural connectivity, adversarial capability, security controls, protocol dependency, risk alignment, communication directionality, lifecycle integration, and response timing. Under the simulation assumptions, connected architecture increases mean cyber exposure by 56.3% relative to segmented architecture, while combined prevention and containment reduce exposure by 60.1% relative to no controls. Increasing protocol dependency raises the modeled clinical-impact index from 1.021 to 7.567 without changing mean cyber exposure, demonstrating that technical exposure and clinical consequence can diverge. Risk-alignment experiments further indicate higher modeled clinical impact when greater technical risk corresponds to greater clinical significance. The findings support comparative architectural analysis, not calibrated estimates of real-world cyber risk, control effectiveness, or patient harm. The framework provides a clinical-context-aware approach to security-boundary design, control prioritization, and coordinated containment in connected healthcare environments.