Government agencies hold critical information that is sensitive to the personal security of citizens, of critical infrastructure components, and of national security assets, all of which can be the targets of sophisticated cyberattacks. Current traditional cybersecurity solutions are mainly signature-driven, which are often less effective against advanced persistent threats, insider attacks, ransomware, phishing campaigns, and zero-day attacks. This paper introduces an AITIR (Artificial Intelligencebased Threat Intelligence and Incident Response), a new framework for intelligent cybersecurity that improves threat detection, risk assessment, and automated incident response in government environments. The proposed framework brings together machine learning anomaly detection, behavioral analytics, AI-driven threat intelligence, predictive risk assessment, and automated security orchestration, all under a single roof, to continuously monitor security events and automatically respond to new cyber threats in real-time. The system generates a framework that evaluates traffic, user behavior, system logs, and endpoint activities to detect malicious patterns without generating too many false alarms. Experimental results validate the proposed approach and show that the approach is capable of achieving an overall classification accuracy of 97.4% and high precision, recall, and F1-score for a variety of cyber threat categories. The thorough performance assessment, such as feature significance, correlation matrix, learning behavior, and confusion matrix evaluation, further verifies the robustness and reliability of the proposed framework. The features of AITIR's intelligent analytics, automated response mechanisms, and continuous security monitoring create a scalable, adaptive, and future-proof cybersecurity solution that improves cyber resilience for government agencies, assists with regulatory compliance, and protects critical information systems from the ever-evolving cyber threat landscape.
