DevSecOps for AI Systems: Security Automation, Model Protection, and Governance Frameworks

Abstract

The rapid adoption of Artificial Intelligence (AI) into the contemporary software ecosystem has augmented the necessity of strong security, governance, and compliance frameworks across the lifecycle of the system. In this paper, an in-depth overview of DevSecOps practices associated with AI systems will be provided, with a focus on automated security testing, model protection, and governance frameworks. It presents intelligent security automation with SAST, DAST, and IAST with the assistance of AIOps to provide real-time monitoring, anomaly detection, and incident resolution in CI/CD pipelines. The paper also analyzes the privacy-saving model protection methods, such as watermarking, federated learning, and encryption-based safe training. The most important security threats in Large Language Models, including prompt injection, data leakage, model extraction, and API misuse, are discussed along with the method of adversarial defence. Besides, the paper outlines deployment-time protection, real-time monitoring, and threat modelling for resilient functioning. Lastly, it discusses governance frameworks, including automation, Infrastructure as Code, regulatory compliance, and Responsible AI in a way that deploys AI across cloud-native environments in a manner that is both trustworthy, ethical, and secure and retains development agility.