Automated Vulnerability Governance: Integrating AI-Driven Risk Mitigation and DevSecOps in High-Scale Cloud Infrastructures
Keywords:
Vulnerability Management, DevSecOps, Cloud Security, Artificial IntelligenceAbstract
As enterprise infrastructures expand into hybrid cloud and IoT environments, traditional vulnerability management (VM) strategies struggle to maintain efficacy. The volume of assets—often exceeding 100,000 endpoints—creates a "noise" of alerts that overwhelms security operations centers.
Objective: This study aims to develop and evaluate an integrated framework, the Intelligent Vulnerability Orchestration Model (IVOM), which leverages Artificial Intelligence (AI) and DevSecOps principles to automate the lifecycle of vulnerability detection, prioritization, and remediation.
Method: We synthesized current regulatory standards, including CISA’s Secure by Design and NIST’s Secure Software Development Framework (SSDF), with advanced machine learning perspectives. The IVOM framework was designed to utilize predictive algorithms for risk scoring and automated pipelines for patch deployment.
Results: The analysis suggests that integrating AI-driven prioritization significantly reduces false positive rates compared to static scanning methods. Furthermore, embedding security testing into the CI/CD pipeline (DevSecOps) demonstrates a theoretical reduction in Mean Time to Remediate (MTTR) by bridging the operational silo between security and engineering teams.
Conclusion: The transition to automated, AI-enhanced vulnerability governance is not merely a technical upgrade but a strategic necessity for maintaining resilience in high-scale environments. Future efforts must focus on the explainability of AI decisions in compliance-heavy sectors.
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Copyright (c) 2025 Kenji T. Morikawa (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
