Sovereign networks, which support national security, critical infrastructure, and government operations, face increasingly sophisticated cyber threats characterized by advanced persistent threats (APTs), zero-day exploits, and large-scale coordinated attacks. Traditional rule-based and signature-driven security mechanisms are insufficient to defend against such dynamic and intelligent adversaries. This paper presents an AI-based cyber defense framework designed specifically for sovereign network environments. The proposed approach integrates machine learning, deep learning, and behavioral analytics to enable real-time threat detection, predictive risk assessment, and autonomous response. By leveraging supervised and unsupervised learning models, the system identifies anomalous patterns across network traffic, user behavior, and system logs, even in the presence of encrypted or stealthy attacks. Reinforcement learning is employed to support adaptive decision-making and automated mitigation strategies while minimizing operational disruption. The framework emphasizes data sovereignty, explainable AI, and resilience, ensuring compliance with national security policies and regulatory requirements. Experimental analysis and case-based evaluations demonstrate improved detection accuracy, reduced response time, and enhanced robustness compared to conventional cybersecurity solutions. The study highlights the potential of AI-driven cyber defense systems to strengthen the security posture of sovereign networks and provides insights into future directions for autonomous, scalable, and trustworthy national cyber defense architectures.