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AI-Augmented Threat Intelligence for Autonomous Vulnerability Management in Cloud-Native Clusters

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  • Sushil Prabhu Prabhakaran

Abstract

This research introduces an AI-augmented framework for proactive vulnerability management in cloud-native clusters, integrating real-time threat intelligence with automated DevSecOps workflows. The system continuously aggregates CVE data, analyzes exploit likelihood using machine learning models, and dynamically injects remediation recommendations into Terraform and ArgoCD pipelines. By coupling predictive analytics with infrastructure-as-code automation, it enables continuous risk scoring, autonomous patch deployment, and contextual prioritization of vulnerabilities. Empirical evaluations across AWS, Azure, and GCP clusters demonstrate a 73% reduction in mean time to remediation (MTTR) and an 89% increase in vulnerability detection accuracy, outperforming conventional reactive methods. The framework’s low operational overhead (

Suggested Citation

  • Sushil Prabhu Prabhakaran, 2025. "AI-Augmented Threat Intelligence for Autonomous Vulnerability Management in Cloud-Native Clusters," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(5), pages 418-429, October.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i5:id:1785
    DOI: 10.32628/CSEIT251117240
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251117240
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