AI-Driven Threat Intelligence for National Cybersecurity Governance: A Framework for Adaptive Risk Detection and Policy-Aware Response

Abstract

The rapid evolution of cyber threats has exposed critical limitations in traditional, rule-based cybersecurity governance models. Nation-state infrastructures increasingly face adaptive, artificial intelligence (AI)-assisted attacks that outpace static defence mechanisms and policy frameworks. This paper proposes an AI-driven threat intelligence framework designed to enhance national cybersecurity governance through adaptive risk detection, contextual analysis, and policy-aware response orchestration. The framework integrates machine learning (ML)-based anomaly detection, graphbased threat correlation, and governance-aligned decision layers to bridge the gap between technical cybersecurity operations and regulatory oversight. Unlike conventional security information and event management systems, the proposed approach emphasises explainability, institutional accountability, and alignment with national digital governance objectives. The paper presents the conceptual architecture, operational workflow, and governance implications of the framework, demonstrating how AI can support strategic cyber resilience while preserving transparency and policy compliance. The findings contribute to applied cybersecurity research by offering a scalable, governance-centric model suitable for critical infrastructure protection and national cyber defence strategies.