The integration of DevOps and DevSecOps paradigms represents a transformative shift in contemporary software development, emphasizing automation, security, and continuous delivery. This study investigates the convergence of artificial intelligence (AI), machine learning (ML), and cloud-based strategies within DevOps pipelines to optimize software quality, reduce vulnerabilities, and enhance operational efficiency. Through an extensive synthesis of recent research, including studies on continuous integration/continuous deployment (CI/CD), automated testing, predictive analytics, and AI-driven security frameworks, the paper elucidates how modern development practices can proactively address security, compliance, and performance challenges. Key findings highlight the benefits of AI-assisted monitoring, predictive defect detection, and automated compliance enforcement, while also identifying constraints related to system complexity, data privacy, and integration overheads. This research contributes to theoretical and practical understanding by offering a comprehensive conceptual framework for AI-driven DevSecOps and presents pathways for future research to explore scalable, resilient, and ethically aligned software delivery mechanisms.

DevOps, Continuous Integration, AI-driven Automation

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