In the era of cloud computing and DevOps, ensuring robust security remains a critical challenge for organizations. The dynamic nature of cloud infrastructures and the increasing complexity of cyber attacks pose significant dangers to sensitive data and critical applications. Traditional security approaches struggle to keep pace with the rapid development cycles, leading to vulnerabilities and security gaps. Additionally, integrating AI-driven security practices into DevOps workflows is hampered by communication barriers, skill gaps, and cultural resistance within organizations. This paper proposes leveraging AI-powered techniques within the DevSecOps framework to enhance cloud security. By integrating AI and machine learning (ML) algorithms into security processes, organizations can significantly improve threat detection, vulnerability management, and incident response. AI facilitates real-time analysis of large datasets, enabling proactive identification of security threats, while ML algorithms predict and prevent potential breaches based on historical data. Workflows for security and response are further streamlined via automation. Infrastructure, technology, and expertise investments, as well as cultural changes and better communication techniques to bring security and DevOps teams together, are all necessary for the successful deployment of AI-powered AI TRiSM practices. To close skill gaps and enable staff to embrace AI-driven security procedures, training and upskilling programs are essential. To handle changing risks and legal requirements, security strategies must be continuously assessed and adjusted. Organisations may reduce risks and increase software delivery and creativity by adopting AI-powered DevSecOps, which strikes a balance between security and speed. By using this strategy, businesses can protect their cloud infrastructure, data, and apps while staying ahead of the always changing threat landscape.