Control Physiology: An Agent-Based Model of FAIR-CAM Dynamics

• URL: http://arxiv.org/abs/2605.26597v1
• Date: Tue, 26 May 2026 06:31:20 GMT
• Title: Control Physiology: An Agent-Based Model of FAIR-CAM Dynamics
• Abstract summary: We present the first agent-based model to operationalize the core FAIR-CAM dynamics.<n>In a hospital ransomware scenario, three organizational dynamics emerge that static analysis cannot represent.<n>These dynamics are structural properties of the FAIR-CAM architecture and should generalize beyond the specific scenario studied.
• Score: 0.14323566945483493
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Security risk analysis typically treats control effectiveness as a static input, yet controls degrade through configuration drift, depend on monitoring systems that may themselves be degraded, and compete for finite remediation budgets. The FAIR Controls Analytics Model (FAIR-CAM) provides the theoretical framework for these dynamics but has so far remained theoretical. We present the first agent-based model to operationalize the core FAIR-CAM dynamics, making control physiology computationally observable, and release the implementation as open source. The simulation implements eight agent types, a multiplicative defense-in-depth susceptibility formula, a three-source variance model, budget-constrained remediation, and a narrative causation engine that produces a complete causal trace for every loss event. In a hospital ransomware scenario (N=1,000 iterations), three organizational dynamics emerge that static analysis cannot represent. First, emergent operational efficacy diverges from the analytical FAIR-CAM formula by approximately 17 percent, driven by correlated extrinsic variance; the divergence grows linearly with extrinsic frequency and vanishes under purely intrinsic drift. Second, a sharp queueing regime transition in the remediation pipeline approximately 2.8x expected loss when budget falls below a scenario-specific threshold (5-10 engineer-hours/month). Third, cascading monitoring failures propagate through the VMC topology: a single degraded VMC silently compounds undetected variance across the controls it manages. These dynamics are structural properties of the FAIR-CAM architecture and should generalize beyond the specific scenario studied.

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