AI-Powered Real-Time Risk Scoring in Cloud Security Environments

Real-time risk scoring has become a critical component of modern cloud security frameworks, particularly in multi-cloud infrastructures where data heterogeneity, dynamic configurations, and privacy constraints present significant challenges. Traditional risk scoring models are often static, siloed, and non-transparent, limiting their ability to adapt to evolving threat landscapes or provide interpretable insights for analysts. To address these limitations, this paper proposes CA-FERAF (Context-Aware Federated Risk Scoring with Explainable Adaptive Feedback), an intelligent and adaptive framework that integrates federated learning (FL) and explainable artificial intelligence (XAI) to achieve scalable, privacy-preserving, and transparent real-time risk analysis. In CA-FERAF, federated learning enables collaborative model training across multiple cloud nodes without exposing sensitive data, ensuring regulatory compliance and data confidentiality. The framework incorporates contextual data fusion, combining diverse sources such as asset criticality, user behavior, network topology, and threat intelligence to generate risk scores that accurately reflect both localized and systemic security exposures. An embedded explainability module employs SHAP and LIME techniques to interpret the reasoning behind each risk score, enhancing analyst trust and decision confidence. Additionally, a human-in-the-loop adaptive feedback mechanism allows continuous model refinement through expert interaction, enabling the system to evolve alongside emerging threat patterns. Comprehensive experimental evaluations demonstrate that CA-FERAF significantly improves detection accuracy, precision, and recall compared to existing centralized AI-based scoring systems, while achieving lower latency suitable for real-time cloud operations. The results confirm that integrating federated learning with explainable AI enhances transparency, adaptability, and efficiency in risk management processes. Ultimately, CA-FERAF represents a major advancement toward intelligent, interpretable, and resilient cloud security operations, offering an innovative pathway for organizations seeking dynamic, data-driven protection in complex, distributed environments.

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