Autonomous Devsecops: A Quantitative Maturity Model and ML-Driven Security Orchestration Framework for Continuous Compliance in Regulated Cloud Environments

Abstract

Regulated cloud delivery demands more than the presence of DevSecOps tools; it requires measurable security maturity, continuous control execution, and audit-ready evidence across every release cycle. This article introduces an autonomous DevSecOps framework that combines a quantitative maturity model with ML-driven security orchestration for continuous compliance. The model scores secure pipeline integration, vulnerability governance, policy-ascode enforcement, cloud configuration security, evidence automation, and response orchestration, while the orchestration layer supports risk prioritization, compliance drift prediction, remediation routing, and automated evidence mapping. Results show that higher orchestration maturity improves DevSecOps maturity score, compliance evidence completeness, policy automation coverage, compliance drift reduction, and security orchestration success under increasing regulated workload complexity. The study demonstrates that autonomous DevSecOps can convert compliance from a periodic audit activity into a measurable, continuously governed cloud security process.