Abstract
This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques within DevSecOps frameworks to enhance system resilience. In today's dynamic and rapidly evolving technological landscape, resilience has become a critical aspect of software development and operations. DevSecOps, an evolution of the DevOps methodology, emphasizes the importance of integrating security practices throughout the software development lifecycle. By leveraging AI/ML capabilities within DevSecOps frameworks, organizations can proactively identify and mitigate security threats, optimize system performance, and enhance overall resilience. This paper discusses various strategies for incorporating AI/ML algorithms into DevSecOps workflows, including anomaly detection, predictive analytics, and automated incident response. Furthermore, it examines the challenges and considerations associated with implementing AI/ML-driven approaches within DevSecOps environments, such as data privacy concerns, model interpretability, and algorithmic biases. Through a comprehensive exploration of these concepts, this paper provides insights into building resilient systems by harnessing the power of AI/ML within DevSecOps frameworks.
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