Modern cloud-native service-oriented architectures (SOAs) offer significant scalability, modularity, and agility—but these benefits come with complex and evolving security risks. Traditional DevSecOps approaches, which integrate security into continuous integration and delivery (CI/CD) workflows, often lack the adaptability and speed required to protect such dynamic environments. This paper proposes a comprehensive AI-Augmented DevSecOps framework that embeds machine learning across multiple pipeline stages—from static code analysis to real-time anomaly detection and automated policy enforcement. The framework integrates supervised and unsupervised learning models, including LSTM networks and autoencoders, and leverages explainable AI (XAI) to ensure interpretability and compliance. Experimental validation is conducted in a hybrid Kubernetes and AWS Lambda environment using the CICIDS2017 dataset and open-source security tools. Results demonstrate over 95% detection accuracy with a false positive rate below 5%, along with improved deployment safety and resource optimization. By addressing key challenges like concept drift, adversarial robustness, and regulatory compliance, this work advances the integration of AI into DevSecOps for securing cloud-native SOAs.

Akshay Mittal is an accomplished technology leader, IEEE Senior Member, and Staff Software Engineer at PayPal with over a decade of experience in full-stack development, cloud architecture, and secure software engineering. He leads the design and deployment of high-performance cloud-native systems that power mission-critical financial services globally, with deep expertise in distributed architectures, DevSecOps, and performance engineering.
 Currently pursuing a PhD in Information Technology at the University of the Cumberlands, Akshay’s research focuses on AI/ML-driven security and automation for cloud-native environments. His academic contributions include explainable threat detection models, adaptive policy validation pipelines, and automated remediation frameworks—bridging academic theory with production-grade solutions in serverless and microservices-based ecosystems. Akshay has authored multiple peer-reviewed research papers on topics such as cloud security, AI-Augmented DevSecOps, and containerized software engineering. His publications are accessible via his ResearchGate profile, reflecting his active engagement with both scholarly and applied research communities. 
He is a frequent speaker at major technology conferences, including Developer Week, Kubernetes Austin, and IEEE-sponsored workshops, and recently participated in Google’s AI Workshop in Austin to explore cutting-edge developments in AI infrastructure. 
As a passionate advocate for open collaboration and mentorship, Akshay serves as Organizer of the Kubernetes Austin Chapter (under CNCF), co-leads the GenAI Collective Austin, advises the ACM chapter at Texas State University, and contributes to IEEE/ACM educational initiatives. His leadership fosters hands-on learning, community growth, and global knowledge-sharing in the AI and cloud-native domains. 
With a unique blend of technical innovation, impactful research, and sustained community leadership, Akshay is committed to advancing global excellence and empowering the next generation of technologists.