AI-Augmented Threat Intelligence for Autonomous Vulnerability Management in Cloud-Native Clusters
DOI:
https://doi.org/10.32628/CSEIT251117240Keywords:
Vulnerability Management, AI-Augmented Threat Intelligence, Cloud Security, Terraform, ArgoCD, DevSecOps, CVE PrioritizationAbstract
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 (<4%) and seamless integration with GitOps workflows establish its practicality for large-scale, multi-cloud environments. The proposed approach advances the state of cloud security toward self-healing, AI-driven resilience and continuous compliance within distributed containerized ecosystems.
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