The rapid adoption of artificial intelligence (AI) in government creates substantial opportunities for automated decision-making, threat detection, public-service optimization, and strategic security operations, but it simultaneously introduces risks involving privacy, model reliability, explain ability, cybersecurity, and national security. This research develops an integrated policy framework for secure and responsible government AI by synthesizing the methodological and conceptual lessons contained in the provided literature on machine learning, deep learning, risk prediction, intelligent decision support, and healthcare AI. The methodology adopts a policy-oriented synthesis approach and translates principles of predictive modeling, feature selection, hybrid inference, deep learning, and explain ability into a governmental AI governance architecture. Particular attention is given to the relationship between predictive performance and responsible deployment, emphasizing that technically accurate models may remain unsuitable for public-sector use when their decisions cannot be explained, audited, or governed. The proposed framework integrates six dimensions: cybersecurity protection, privacy preservation, explainable AI, risk-based governance, human oversight, and national-security accountability. The findings indicate that responsible government AI requires a layered rather than purely technical approach in which model development, data governance, deployment security, continuous monitoring, and institutional accountability operate as interconnected controls. The framework also identifies a critical research gap: existing AI studies demonstrate the technical value of intelligent prediction but provide limited direct guidance for reconciling AI-enabled governmental security with privacy and democratic accountability. The study concludes that secure government AI should be governed as a socio-technical infrastructure rather than treated merely as a predictive software capability.