AI-Based Security Posture Management as an Enabling Layer for Real-Time NIST Cybersecurity Framework Adherence: A Theory- Grounded Conceptual Framework

Abstract

Critical infrastructure (CI) operators face a widening gap between the point-in-time, auditoriented mechanisms through which they demonstrate compliance with the NIST Cybersecurity Framework (CSF) and the continuous, machine-speed dynamics of the environments those mechanisms are meant to protect. This article develops a theory-grounded conceptual framework that positions AI-Based Security Posture Management (ASPM) as the enabling layer through which CSF functions are transformed from periodically evidenced artifacts into continuously operating control mechanisms. The paper first distills five continuous control principles latent in NIST guidance: real-time assessment, automated detection and analytics, responsive and adaptive control, policy-based enforcement, and cross-environment visibility. It then defines five corresponding ASPM capabilities grounded in the artificial intelligence and machine learning literature: automated configuration monitoring, real-time control mapping, behavior and pattern analysis, automated remediation, and unified cross-environment visibility. The framework is anchored in three complementary theoretical traditions: Risk Management Theory, which justifies continuous risk sensing under Knightian uncertainty; Control Theory and cybernetics, which supply the feedback-loop logic of measurement, comparison, and correction; and Sociotechnical Systems Theory, which insists that automated control be embedded in a balanced human and technological ecosystem. A capability-to-function mapping across the six CSF 2.0 functions (Govern, Identify, Protect, Detect, Respond, Recover) is presented, together with seven testable propositions. The article closes with implications for CI operators and regulators, boundary conditions of the framework, and a research agenda for empirical validation.