Abstract

The growing scale, connectivity, and automation of digital infrastructures have significantly increased exposure to sophisticated cyber threats. Conventional cyber risk control mechanisms remain largely reactive, relying on signature-based detection or post-incident analysis, which limits their effectiveness against evolving and stealthy attack strategies. This paper proposes a predictive intelligence framework for proactive cyber risk control that shifts security operations from detection-centric defense toward anticipatory risk reasoning. The framework integrates historical security telemetry, behavioral indicators, and temporal learning models to forecast emerging threats and vulnerability exploitation patterns before operational impact occurs. A layered system architecture is introduced to support continuous data ingestion, feature abstraction, predictive modeling, and decision-oriented risk prioritization. Unlike isolated anomaly detection systems, the proposed approach embeds predictive outputs into a risk control layer that translates forecasts into actionable mitigation guidance for security teams. Experimental evaluation using representative cybersecurity datasets demonstrates improved threat prediction lead time, enhanced risk prioritization accuracy, and a reduction in false positive alerts compared with conventional intrusion detection baselines. Analytical results indicate that incorporating temporal and behavioral intelligence enables earlier intervention and more consistent decision-making under dynamic threat conditions. The study highlights how predictive intelligence can strengthen organizational cyber resilience by enabling proactive defense planning, reducing response latency, and supporting scalable risk governance. The findings suggest that predictive, intelligence-driven cyber risk control represents a practical and necessary evolution of modern cybersecurity strategies. It also provides a foundation for integrating predictive analytics with existing security operations and policy-driven governance frameworks across complex enterprise and critical infrastructure environments.