A Framework for Real-Time AI-Driven Secure Code Analysis Integrated with DevSecOps in Cloud-Native CI/CD Pipelines

A Framework for Real-Time AI-Driven Secure Code Analysis Integrated with DevSecOps in Cloud-Native CI/CD Pipelines

Charan Shankar Kummarapurugu

Senior DevOps Engineer — Cloud, DevSecOps and AI/ML Brambleton, VA, USA

Email: charanshankar@outlook.com

Abstract—This paper presents a novel framework for inte- grating real-time AI-driven secure code analysis into DevSecOps practices within cloud-native CI/CD pipelines. As organizations increasingly adopt cloud-native architectures and agile develop- ment methodologies, the need for robust, automated security mea- sures becomes paramount. Our proposed framework leverages advanced machine learning algorithms to perform continuous, real-time code analysis, identifying potential vulnerabilities and security risks throughout the development lifecycle. By seamlessly integrating with existing CI/CD tools and cloud platforms, our solution enables organizations to enforce security policies, detect threats, and remediate issues without compromising development velocity. We evaluate the effectiveness of our framework through a series of case studies across diverse software projects, demon- strating significant improvements in threat detection accuracy, reduced false positives, and overall security posture. Our results indicate that the proposed AI-driven approach can enhance code security by up to 40% compared to traditional static analysis tools, while maintaining the agility of modern development prac- tices. This research contributes to the evolving field of DevSecOps by offering a scalable, intelligent solution for embedding security into the heart of cloud-native software development processes.

Index Terms—DevSecOps, AI-driven security, cloud-native, CI/CD pipelines, secure code analysis