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

Farid Binbeshr, Muhammad Imam

TL;DR

This paper conducts a systematic literature review to compare AI-driven security approaches within DevSecOps. It analyzes 18 primary studies (2015–2024) across five AI usage clusters, evaluating technical capabilities, implementation requirements, and operational impacts. Key findings highlight the growth of shift-left security, privacy challenges in multi-cloud deployments, and the need for empirical validation and lightweight, scalable models. The authors propose future directions including lightweight edge models, federated and privacy-preserving methods, and human-centric security training to improve practical adoption in real-world DevSecOps settings.

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.

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

TL;DR

This paper conducts a systematic literature review to compare AI-driven security approaches within DevSecOps. It analyzes 18 primary studies (2015–2024) across five AI usage clusters, evaluating technical capabilities, implementation requirements, and operational impacts. Key findings highlight the growth of shift-left security, privacy challenges in multi-cloud deployments, and the need for empirical validation and lightweight, scalable models. The authors propose future directions including lightweight edge models, federated and privacy-preserving methods, and human-centric security training to improve practical adoption in real-world DevSecOps settings.

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.
Paper Structure (30 sections, 5 figures, 4 tables)

This paper contains 30 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Database Selection and Search Strategy
  4. Search String and Refinement
  5. Inclusion Criteria
  6. Study Selection
  7. Quality Assessment
  8. Data Extraction Strategy
  9. Results
  10. Study Selection Results
  11. Study Characteristics
  12. Quality Assessment Results
  13. Comparative Analysis
  14. Technical Capability Analysis
  15. Implementation Requirements Analysis
  16. ...and 15 more sections

Figures (5)

  • Figure 1: Flowchart showing the systematic selection process for the SLR. Initially, 144 articles were identified from six databases. After removing duplicates and irrelevant studies, 18 articles were included in the final review.
  • Figure 2: Bar chart displaying the quality scores of the 18 selected studies, assessed using a predefined checklist with scores ranging from 0 to 9. Most studies scored between 4.5 and 9, reflecting high methodological quality. However, some studies lacked detailed explanations of limitations, highlighting areas for improvement in reporting transparency.
  • Figure 3: Technical capabilities enhancing DevSecOps, covering key areas including security frameworks, threat modeling, privacy frameworks, automation, compliance, and CI/CD security.
  • Figure 4: Overview of key challenges in integrating ML-based security into DevOps, including computational overhead, tool compatibility, scalability issues, and automation challenges.
  • Figure 5: Diagram illustrating the benefits (e.g., faster deployment) and challenges (e.g., CI/CD delays) of security automation in DevOps, along with human factors like developer training needs.