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DevSecOps Pipeline for Middleware: Automated Deployment, Vulnerability Remediation, and Rollback Strategies

Year : 2025

Publisher : Institute of Electrical and Electronics Engineers Inc.

Source Title : 4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025

Document Type :

Abstract

In today’s fast-paced business environment, middleware is essential for connecting applications, services, and databases, ensuring both security and reliability. This paper introduces DevSecOps, a pipeline framework designed to improve the security, automation, and rollback processes for managing cloud data. The primary goal of DevSecOps is to safeguard cloud data, maintain a seamless workflow, and ensure stable operations. At its core, DevSecOps combines continuous deployment, security, and system integration into a single, unified middleware pipeline. This creates a smoother, more efficient cloud data workflow. One of the standout features is the security verification module, which is built into every stage of the pipeline. It uses tools like OWASP, Sync, and SonarQube to detect vulnerabilities early, making security a priority from the start. The framework also automates key tasks like middleware configuration, orchestration, and containerization using technologies such as Kubernetes, Docker, and Ansible. This reduces the risk of human error, ensuring a more consistent and reliable environment. With DevSecOps, continuous monitoring and analysis are made possible through tools like Prometheus and Grafana. This enables real-time tracking of performance and early detection of any issues, helping to improve both security and overall efficiency. A key feature of DevSecOps is its rollback mechanism, which ensures stability during security processes by quickly recovering from failures. This minimizes downtime and prevents data loss, making it crucial for maintaining system reliability. Overall, DevSecOps fills the gaps left by traditional middleware systems by automating security, deployment, and workflow management. Experimental results show it reduces time complexity by 40% compared to existing models, with enhanced vulnerability detection.