Cognitive Cloud Security : Machine Learning-Driven Vulnerability Management for Containerized Infrastructure

Modern cloud-native environments face unprecedented security challenges due to the dynamic nature of containerized workloads and rapid deployment cycles. This article presents a comprehensive framework that leverages artificial intelligence and real-time threat intelligence to transform vulnerability management from reactive patching to proactive threat mitigation. The proposed system integrates seamlessly with infrastructure-as-code tools like Terraform and ArgoCD, enabling continuous security assessment and automated remediation workflows. Through extensive evaluation across multiple cloud platforms, our framework demonstrates a 73% reduction in mean time to remediation and 89% improvement in vulnerability detection accuracy. The AI-driven approach successfully predicts exploitation likelihood with 84% accuracy for high-risk vulnerabilities, enabling security teams to prioritize remediation efforts effectively. This research establishes a new paradigm for cloud security that maintains development velocity while significantly enhancing security posture.