The convergence of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps represents a groundbreaking paradigm shift in software development practices. This paper explores the transformative impact of integrating AI/ML technologies into the DevSecOps framework, revolutionizing the way software is designed, developed, and secured. Through a comprehensive analysis of current trends, challenges, and opportunities, the paper elucidates the key strategies and best practices for leveraging AI/ML in DevSecOps. Topics addressed include automated threat detection, predictive analytics for vulnerability management, intelligent automation, and the ethical considerations surrounding AI/ML deployment in security-sensitive environments. By embracing this convergence, organizations can enhance their security posture, accelerate software delivery, and foster a culture of continuous improvement. Case studies and real-world examples are presented to illustrate the practical applications and benefits of AI/ML in transforming DevSecOps practices.
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Pakalapati et al. (2023) studied this question.
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