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In today's rapidly evolving technological landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps practices has emerged as a critical enabler for enhancing security, efficiency, and innovation in software development and deployment processes. This paper explores the strategies and best practices for harnessing the full potential of AI/ML within the DevSecOps framework. Beginning with an overview of DevSecOps principles and the role of AI/ML, the paper delves into specific strategies such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Furthermore, it examines key challenges and considerations associated with implementing AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through case studies and real-world examples, the paper illustrates how organizations can leverage AI/ML technologies to optimize their DevSecOps pipelines, mitigate security risks, and foster a culture of continuous improvement. By embracing these strategies and best practices, organizations can unlock the full potential of AI/ML to drive innovation, resilience, and agility in their DevSecOps initiatives.
Pakalapati et al. (Wed,) studied this question.