PROACTIVE VULNERABILITY MANAGEMENT IN CLOUD CLUSTERS THROUGH AI-AUGMENTED THREAT INTELLIGENCE
Abstract
This research outlines a proactive security framework that augments Cloud cluster security using real-time AI-driven threat intelligence. By continuously scanning for CVEs and integrating results into Terraform plans and ArgoCD manifests, the framework reduces exposure windows and automates patch compliance. The proposed system leverages machine learning algorithms to predict vulnerability exploitation likelihood, prioritize remediation efforts, and maintain continuous security posture assessment across distributed cloud environments. Through empirical evaluation across multiple cloud platforms, the framework demonstrates a 73% reduction in mean time to remediation and 89% improvement in vulnerability detection accuracy compared to traditional reactive approaches.