Application of AI and ML in the Field of DevSecOps

Abstract

Security is a paramount concern in DevOps. The adoption of Infrastructure as Code (IaC) has increased the potential impact of even minor flaws, particularly in critical domains like healthcare and maritime applications. Existing solutions typically focus on either Static Application Security Testing (SAST) or run-time behavior analysis. This paper introduces the IaC Scan Runner, an open-source tool developed in Python for inspecting various IaC languages during application design, and LOMOS, a run-time anomaly detection tool. Both tools work together to enhance the security of DevOps processes. In today’s rapidly evolving technological landscape, the vulnerability of infrastructure and applications is growing due to a combination of factors. Attackers are becoming more sophisticated, leveraging improved intelligence to exploit weaknesses. At the same time, there is a lack of technical capability in many organizations to effectively secure their systems. This paper explores a dual approach to cybersecurity: static security monitoring through rule matching and the application of self-supervised machine learning. By combining these approaches, organizations can better defend against cyber threats. One area of focus is supply chain resilience and smart logistics, where the integration of these methods is particularly critical. This approach emphasizes a self-learning and self-healing approach, allowing systems to adapt and respond to new threats autonomously. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps practices is essential for improving security, efficiency, and innovation in software development and deployment. This paper delves into strategies and best practices for leveraging AI/ML within the DevSecOps framework. It discusses automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. However, this integration also presents challenges, such as data privacy, algorithm transparency, and ethical implications. The paper addresses these challenges and showcases how organizations can use AI/ML to optimize their DevSecOps pipelines, mitigate security risks, and foster continuous improvement. The adoption of Infrastructure as Code (IaC) has increased the potential impact of even minor flaws, especially in critical domains like healthcare and maritime applications. Existing solutions typically focus on either Static Application Security Testing (SAST) or run-time behavior analysis.