Security at Scale: Automating Vulnerability Triage and Risk-Based Patch Management in CI/CD Pipelines

Abstract

The faster and more frequently used CI/CD pipelines to deliver software have led to an increase in vulnerabilities due to a lack of pace related to traditional manual triaging efforts. This report shows us an expandable framework combining both static and dynamic analysis runs, compound risk-scoring, and automated patch orchestration to simplify vulnerability management in CI/CD pipelines. It uses modular microservices that are deployed through Kubernetes and uses pre-merge hooks, post-build pipelines, and a message bus to isolate the scanners, scoring engines, and orchestration elements. Risk scoring complements CVSS values with up-time threat intelligence, asset criticality, and employs natural language inference in a machine-learned layer of prioritization to further improve urgency assessment. Patch implantation strategies integrate the canary and blue-green strategies with rollback to deliver resilience in case of failures. Well, empirical testing on a microservices-based application comprising 50 injected vulnerabilities showed that median triage time was reduced by 50% (120 to 60 minutes), 30% more vulnerabilities were patched (58% to 88%), and 20% fewer were found to be false positives. Rollback events were reduced to less than 3% compared to 5% in the manual workflow, and resource overhead was not within enterprise quotas. Engaging engineering and security teams in qualitative surveys found that the mental burden and satisfaction decreased, and 95% of them wanted to adopt it. The framework facilitates the multi-cloud and hybrid setups, improved integration of incident response, and explainable ML models in compliance audits. The following areas of development involve adaptive feedback-driven risk models, large-scale field tests, and ML explainability as a means of venerating self-healing CI/CD pipelines that can subordinate security automation to the needs of an organization.