mlsanityData to 7 Oct 2026

AI for DevSecOps: A Landscape and Future Opportunities

arXiv 2404.04839 ↗First posted 7 Apr 2024cs.SE cs.AIcited by 3

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Abstract (arXiv)

DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm has gained prominence, urging practitioners to incorporate security practices seamlessly into the DevOps workflow. However, integrating security into the DevOps workflow can impact agility and impede delivery speed. Recently, the advancement of artificial intelligence (AI) has revolutionized automation in various software domains, including software security. AI-driven security approaches, particularly those leveraging machine learning or deep learning, hold promise in automating security workflows. They reduce manual efforts, which can be integrated into DevOps to ensure uninterrupted delivery speed and align with the DevSecOps paradigm simultaneously. This paper seeks to contribute to the critical intersection of AI and DevSecOps by presenting a comprehensive landscape of AI-driven security techniques applicable to DevOps and identifying avenues for enhancing security, trust, and efficiency in software development processes. We analyzed 99 research papers spanning from 2017 to 2023. Specifically, we address two key research questions (RQs). In RQ1, we identified 12 security tasks associated with the DevSecOps process and reviewed existing AI-driven security approaches, the problems they addressed, and the 65 benchmarks used to evaluate those approaches. Drawing insights from our findings, in RQ2, we discussed state-of-the-art AI-driven security approaches, highlighted 15 challenges in existing research, and proposed 15 corresponding avenues for future opportunities.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 57 of its 224 references that are in the map. First posted on arXiv 7 Apr 2024.

Builds on

57 of 224 references.

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Modelscited by 8
  2. 2025MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detectioncited by 0
  3. 2025Web Technologies Security in the AI Era: A Survey of CDN-Enhanced Defensescited by 0