AI-enabled security systems have transformed the landscape of modern cybersecurity by enabling real-time detection, prediction, and mitigation of cyber threats. Traditional rule-based risk models struggle to keep up with evolving attack vectors such as zero-day exploits, insider threats, cloud misconfigurations, and social engineering attacks. This study develops a comprehensive theoretical framework for Predictive Risk Modeling (PRM) using AI-enabled security systems. It integrates machine learning, behavioral analytics, threat intelligence, anomaly detection, and automated incident response into a cohesive architecture. The proposed model helps organizations transition from reactive security postures to proactive, predictive, and adaptive cyber defense strategies. The framework offers key insights for CISOs, security architects, policymakers, and enterprise risk managers.