Paper Details

The rapid expansion of cloud computing has introduced increasing complexity in managing Development, Security, and Operations (DevSecOps). Traditional DevSecOps approaches rely heavily on manual processes to monitor, detect, and mitigate security threats while maintaining continuous software delivery. This paper explores the role of Artificial Intelligence (AI) in automating DevSecOps within cloud environments. It examines AI-driven techniques such as machine learning, anomaly detection, automated security testing, and predictive analytics to enhance security, compliance, and operational efficiency. AI enables proactive vulnerability detection, optimized deployment workflows, and improved incident response. Additionally, this study addresses challenges such as model interpretability, data privacy, and integration complexities. By reviewing existing AI-driven DevSecOps frameworks, this research provides insights into the future of autonomous security and development practices in cloud computing.

AI, DevSecOps, Cloud Computing, Automation, Machine Learning, Cybersecurity, Continuous Integration, Continuous Deployment.