Intelligent Cloud Native Security Posture Management Using Explainable AI and Continuous Risk Analytics

Abstract

Cloud-native enterprise environments increasingly depend on containers, Kubernetes, microservices, serverless workloads, application programming interfaces, and multi-cloud infrastructure, creating complex and continuously changing security conditions. Traditional Security Posture Management approaches often rely on static rules, periodic assessments, and large volumes of security alerts, limiting their ability to explain risks and prioritize dynamically evolving threats. This research proposes an Intelligent Cloud Native Security Posture Management framework that integrates Explainable Artificial Intelligence (XAI) with continuous risk analytics to provide adaptive, transparent, and context-aware security assessment. The proposed framework collects security telemetry from cloud infrastructure, workloads, identities, configurations, vulnerabilities, network activities, and compliance controls. Machine learning models analyze these heterogeneous signals to identify anomalous behavior, estimate risk levels, and prioritize security weaknesses according to contextual impact. Explainable AI techniques provide interpretable explanations for risk predictions, enabling security teams to understand the principal factors contributing to each assessment. Continuous risk analytics further updates security posture as infrastructure and threat conditions change. The methodology combines data preprocessing, feature engineering, risk modeling, explainability, continuous monitoring, and experimental validation. The proposed approach aims to improve risk prioritization, reduce alert overload, strengthen compliance visibility, and support proactive security operations across dynamic cloud-native environments.