WorkDifferentWithAI.com Academic Paper Alert!
Written by Michael Fu, Jirat Pasuksmit, Chakkrit Tantithamthavorn
Category: AI for IT
Article Section: missing
Publication Date: 2024-04-07
SEO Description: Exploring AI’s role in enhancing DevSecOps for better security and efficiency in software development.
Keywords
DevSecOps, Artificial Intelligence, software development, security workflows, automation
AI-Generated Paper Summary
Generated by Ethical AI Researcher GPT
Summary:
The paper titled “AI for DevSecOps: A Landscape and Future Opportunities” explores the integration of Artificial Intelligence (AI) into DevSecOps (Development, Security, and Operations). It focuses on enhancing security within the DevOps framework through AI technologies, particularly leveraging machine learning and deep learning approaches. The authors, Michael Fu, Jirat Pasuksmit, and Chakkrit Tantithamthavorn from Monash University and Atlassian, review 99 papers to map the current landscape of AI-driven security techniques and identify key challenges and future research opportunities in this field. They emphasize the potential of AI to automate security tasks within DevOps, thus maintaining agility and efficiency in software development and deployment processes.
Degree of Ethical Match: 5
The paper’s alignment with ethical AI practices is strongly evident. It promotes AI’s role in automating security within DevOps, addressing crucial ethical considerations such as enhancing trust, accountability, and security in software development.
Author Caliber:
The authors are affiliated with credible institutions, Monash University and Atlassian, adding substantial authority to their findings. Their focus on a practical application of AI in security reflects a strong understanding and expertise in both the domains of AI and software engineering.
Novelty & Merit:
- The paper fills a significant gap by systematically reviewing AI-driven security techniques specifically for DevSecOps.
- It extends beyond theoretical discussions to practical implications, suggesting enhancements in real-world software development environments.
- The inclusion of a large and recent dataset of research (99 papers) supports the comprehensive nature of the review.
Findings and Conclusions:
- AI can significantly automate security within DevSecOps, enhancing both efficiency and reliability.
- The integration of AI in security tasks helps in maintaining the speed of DevOps without compromising security protocols.
- There are still substantial challenges, such as the complexity of integrating AI into existing systems and the need for ongoing adaptation to new security threats.
Commercial Applications:
- Development of AI-powered tools for real-time security threat detection and response within software development pipelines.
- Creation of AI-based solutions for automated security testing and vulnerability detection, reducing the need for manual security reviews.
- Enhancement of continuous integration/continuous deployment (CI/CD) pipelines with AI-driven security measures, improving the reliability of software releases.
This paper presents a detailed exploration of AI applications in DevSecOps, emphasizing how AI can improve security processes within DevOps, making it a valuable resource for developers, security professionals, and organizations aiming to integrate advanced technological solutions in their operations.
Author’s Abstract
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 DevOps process and reviewed existing AI-driven security approaches. In RQ2, we discovered 15 challenges encountered by existing AI-driven security approaches and derived future research opportunities. Drawing insights from our findings, we discussed the state-of-the-art AI-driven security approaches, highlighted challenges in existing research, and proposed avenues for future opportunities.