Comparative Analysis of AI-Driven Security Approaches in DevSecOps: Challenges, Solutions, and Future Directions

Abstract

The integration of security within DevOps, known as DevSecOps, has gained traction in modern software development to address security vulnerabilities while maintaining agility. Artificial Intelligence (AI) and Machine Learning (ML) have been increasingly leveraged to enhance security automation, threat detection, and compliance enforcement. However, existing studies primarily focus on individual aspects of AI-driven security in DevSecOps, lacking a structured comparison of methodologies. This study conducts a systematic literature review (SLR) to analyze and compare AI-driven security solutions in DevSecOps, evaluating their technical capabilities, implementation challenges, and operational impacts. The findings reveal gaps in empirical validation, scalability, and integration of AI in security automation. The study highlights best practices, identifies research gaps, and proposes future directions for optimizing AI-based security frameworks in DevSecOps.