LLM-Driven DevSecOps for Secure Software Engineering: Integrating Static Analysis, Retrieval-Augmented Generation, and Automated Vulnerability Remediation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P127Keywords:
Devsecops, Large Language Models, Static Analysis, Retrieval-Augmented Generation, Vulnerability Remediation, Secure Software Engineering, Software Supply Chain, AI Governance, Automated Program Repair, Secure CodingAbstract
The rapid adoption of large language models in software engineering has transformed how developers design, generate, review, test, and maintain code. However, the same automation that accelerates development also introduces security risks when generated patches, dependency upgrades, or code explanations are accepted without systematic verification. This paper proposes an LLM-driven DevSecOps framework that integrates static application security testing, retrieval-augmented generation, secure knowledge grounding, automated vulnerability remediation, human approval workflows, and continuous post-remediation validation. The central thesis is that LLMs should not be treated as autonomous security authorities, but as governed remediation agents operating within evidence-based, policy-constrained, and auditable software delivery pipelines. The framework aligns secure software development with NIST SSDF practices by embedding security activities across planning, implementation, verification, release, and monitoring stages [1]. It also extends prior work on AI-driven software lifecycle governance by positioning defect prediction, automated testing, vulnerability detection, and remediation as mutually reinforcing components rather than isolated quality engineering tasks [2]. The proposed architecture uses static analysis findings as structured triggers, retrieval-augmented generation as an evidence-grounded reasoning layer, and controlled patch generation as a remediation mechanism. The paper further presents a methodological design for evaluating the framework using vulnerability detection precision, remediation correctness, regression safety, developer review burden, mean time to remediate, and auditability. The contribution is a reference architecture for secure AI-assisted engineering that reduces manual triage effort while preserving accountability, traceability, and security assurance.
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