An AI-Enhanced Cybersecurity Model for Insider Threat Detection and Data-Leak Prevention in Government Networks

This research paper examines the creation of AI-enriched cybersecurity framework that is likely to identify insider threats
and avoid data leaks in governmental networks. It incorporates artificial intelligence, behavioral analytics and conventional
cybersecurity designs to detect abnormal access behavior, unauthorized file transfers and policy violations in real-time. The
model uses machine learning to assess user behavioral patterns and risk factors based on audit logs, HR data, and Security
Operations center inputs and combines them with threat scoring. The focus is made on privacy conscious detection, which
complies with provisions of standards of privacy like NIST SP 800-53 and CMMC 2.0. The system will reduce false
positives by utilizing adaptive learning algorithms with contextual risk analysis to improve situational awareness across
agencies. It is anticipated that the net effect would be a proactive cybersecurity framework to enhance the prevention of
insider threats, reinforce the protection of the data, and the security of the classified operations of the key government
agencies in the United States, including the DHS, DoD, and intelligence communities.