Security Control Grid for Optimized Cyber Defense Planning

Abstract

Cybersecurity controls are essential for ensuring information confidentiality, integrity, and availability. However, selecting the most effective controls to maximize return on investment (RoI) in cyber defense is a complex task involving numerous factors such as vulnerabilities, threat prioritization, and budget constraints. This paper introduces an innovative model and optimization techniques to select cybersecurity controls (CSC) for optimal risk mitigation, balancing residual risk, budget, and resiliency requirements. Our approach features the Security Control Grid (SCG) model, which automatically determines the necessary controls based on their security functions (Identify, Protect, Detect, Respond, and Recover), strategic placement within the cyber environment, and effectiveness at different stages of the attack kill chain. We formulate cybersecurity control decision-making as a multidimensional optimization problem, solving it using Satisfiability Modulo Theories (SMT). Additionally, we integrate a domain-specific language model that links CSCs with Common Vulnerabilities and Exposures (CVEs). This approach is implemented in the SCG solver tool, which generates scalable and robust CSC deployment plans that optimize cybersecurity RoI and maintain acceptable residual risk for large-scale enterprises.