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Cybersecurity governance frameworks increasingly require dynamic risk assessment mechanisms that align operational security signals with evolving regulatory obligations. Conventional Governance, Risk, and Compliance (GRC) systems rely on static control scoring and manual cross-framework mapping, limiting responsiveness and audit transparency. This study proposes the Adaptive Cyber Risk Intelligence Fabric (ACRIF). This regulator-aligned architecture integrates dynamic control weighting, graph-based cross-framework synchronisation, and deterministic explainability within a unified governance intelligence model. The framework introduces regulatory-cycle-aware weighting, sector-specific amplification modifiers, and time-bound decay functions to recalibrate control prioritisation. Automated propagation mechanisms synchronise compliance impact across multiple cybersecurity standards, while rule-based reasoning chains generate auditready explanations linked to statutory obligations. Analytical validation demonstrates enhanced governance responsiveness, reduced compliance fragmentation, and improved computational efficiency through selective recalculation logic. The findings suggest that ACRIF advances cybersecurity governance beyond static compliance systems, offering a scalable, regulator-sensitive foundation for dynamic enterprise risk intelligence across multi-framework environments.
Adaptive Cyber Risk; Cybersecurity Governance; Regulatory Compliance; Risk Intelligence; Explainable Security.