Dynamic API Risk Scoring Architecture for Cyber Threat Intelligence Systems

The rapid expansion of interconnected systems and cloud-based infrastructures has increased dependency on Application Programming Interfaces (APIs), making them a high-value target for cyber adversaries. Traditional static risk models often fail to capture evolving attack vectors, leading to significant gaps in threat detection and mitigation. This study introduces a Dynamic API Risk Scoring Architecture that integrates real-time telemetry, threat intelligence feeds, behavioral analytics, and contextual security metrics to assess and prioritize API vulnerabilities. The proposed architecture employs adaptive scoring mechanisms driven by anomaly detection, endpoint profiling, and threat propagation modeling. It enables continuous risk evaluation and predictive alerting, enhancing the responsiveness of cyber threat intelligence systems. The framework is designed to be interoperable with existing SOC and SIEM platforms, offering scalable deployment across enterprise and cloud-native environments. Experimental evaluation demonstrates improved detection accuracy, timely risk prioritization, and a reduction in false positives compared to static risk assessment approaches. This architecture contributes to more proactive cyber defense mechanisms by enabling dynamic decision support for API security management.