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

DevSecOps, which integrates security practices into every phase of DevOps, is increasingly adopted to balance rapid delivery with security needs. Artificial Intelligence (AI) and Machine Learning (ML) techniques are frequently applied in DevSecOps pipelines to automate security checks, improve threat detection, and enforce compliance. Yet, most existing research examines these techniques in isolation without comparing approaches. To address that gap, we performed a systematic literature review (SLR) of AI-driven security solutions in DevSecOps. We evaluated each approach's technical capabilities, implementation challenges, and operational impact. Our review found notable shortcomings in current approaches - particularly in real-world validation, scalability, and seamless integration of AI into development pipelines. We also highlight best practices observed in the literature, identify open research questions, and suggest directions for future work in order to advance AI-enabled DevSecOps.