AI DRIVEN THREAT DETECTION IN PUBLIC SECTOR CYBERSECURITY, INTEGRATING MACHINE LEARNING INTO NATIONAL SECURITY SYSTEMS

Abstract Public sector institutions are increasingly targeted by advanced persistent threats, ransomware campaigns, supply-chain compromises, and coordinated influence operations that exploit the scale and complexity of national digital services. Conventional signature- and rule-based security controls remain essential but often underperform against novel, stealthy, and fast-evolving attack patterns, particularly where cross-agency visibility and timely threat sharing are limited. This study develops a national-security–oriented perspective on AI-driven threat detection for the public sector, focusing on how machine learning can be operationalized to improve real-time identification, triage, and response across heterogeneous government environments. Drawing on recent advances in AI-enabled threat intelligence, adversarial machine learning, and autonomous cyber defense, the article synthesizes an integrated framework that combines multi-source telemetry ingestion, automated feature engineering, hybrid detection models, and decision-support pipelines aligned with governance constraints. The framework emphasizes: (i) near-real-time threat intelligence fusion and secure inter-agency sharing, (ii) risk-scoring and prioritization for critical infrastructure and mission systems, (iii) resilience against adversarial manipulation and model drift, and (iv) Accountable deployment through human-in-the-loop workflows, auditability, and policy compliance. The analysis highlights implementation considerations for national security systems, including data sovereignty, interoperability across legacy platforms, incident command alignment, and safeguards for civil liberties. The article concludes with design recommendations and a research agenda for deploying scalable, trustworthy, and continuously improving AI-enabled cyber defense capabilities in public sector ecosystems.