# AI for DevSecOps: A Landscape and Future Opportunities

arXiv: https://arxiv.org/abs/2404.04839 · HTML: https://mlsanity.com/paper/2404.04839/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 224 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 30 of 57)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair](https://mlsanity.com/paper/1901.01808/) (2018)
- [Deep Learning based Vulnerability Detection: Are We There Yet?](https://mlsanity.com/paper/2009.07235/) (2020)
- [An Overview of Deep Semi-Supervised Learning](https://mlsanity.com/paper/2006.05278/) (2020)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation](https://mlsanity.com/paper/1812.08693/) (2018)
- [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (2021)
- [Log-based Anomaly Detection Without Log Parsing](https://mlsanity.com/paper/2108.01955/) (2021)
- [A Syntax-Guided Edit Decoder for Neural Program Repair](https://mlsanity.com/paper/2106.08253/) (2021)
- [Data Quality for Software Vulnerability Datasets](https://mlsanity.com/paper/2301.05456/) (2023)
- [D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis](https://mlsanity.com/paper/2102.07995/) (2021)
- [An Empirical Study of Deep Learning Models for Vulnerability Detection](https://mlsanity.com/paper/2212.08109/) (2022)
- [Applying CodeBERT for Automated Program Repair of Java Simple Bugs](https://mlsanity.com/paper/2103.11626/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2507.22659/) (2025)
- [MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection](https://mlsanity.com/paper/2510.04397/) (2025)
- [Web Technologies Security in the AI Era: A Survey of CDN-Enhanced Defenses](https://mlsanity.com/paper/2512.06390/) (2025)
- [Blockchain and AI: Securing Intelligent Networks for the Future](https://mlsanity.com/paper/2604.06323/) (2026)

## 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.