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

Chandra Sekhar Oleti

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 4, 773-788

Abstract: 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.

Keywords: Cloud Security; Vulnerability Management; Artificial Intelligence; Threat Intelligence; Infrastructure-as-Code; DevSecOps (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT23564528
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i4:id:hcseit23564528

DOI: 10.32628/CSEIT23564528

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