AI-Driven Threat Detection for Public Sector Systems: Balancing Innovation and Privacy
Abstract
The integration of artificial intelligence (AI) into cybersecurity systems has revolutionized threat detection capabilities within public sector organizations. As government agencies increasingly rely on digital infrastructure to deliver essential services, they face sophisticated cyber threats that traditional security measures struggle to address. This study examines the implementation of AI-driven threat detection systems in public sector environments, with particular emphasis on the critical balance between technological innovation and privacy preservation. Through a comprehensive review of current literature and analysis of existing frameworks, this research explores how machine learning and deep learning approaches enhance intrusion detection while maintaining compliance with data protection regulations. The findings reveal that federated learning, differential privacy mechanisms, and explainable AI techniques offer promising pathways for deploying robust threat detection systems that respect citizen privacy. However, challenges persist in addressing adversarial attacks, ensuring algorithmic fairness, and maintaining transparency in automated decision-making processes. This study contributes to the growing body of knowledge on responsible AI deployment in security-critical environments and provides practical recommendations for public sector organizations seeking to modernize their cybersecurity infrastructure without compromising fundamental privacy rights.
Keywords
Artificial IntelligenceThreat DetectionPublic Sector CybersecurityPrivacy-Preserving Machine LearningFederated LearningDifferential PrivacyExplainable AIIntrusion Detection Systems
References
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