Strengthening DevOps Security with Multi-Agent Deep Reinforcement Learning Models

Abstract

DevOps practices have significantly changed software development and delivery processes by promoting integration and deployment. However, it comes with many security issues through the increased speed, dynamic, and automation of the developed DevOps pipelines; these are misconfigurations, errors within the dependencies, and various external threats. In this paper, we discuss using Multi-Agent Deep Reinforcement Learning (MADRL) models to enhance security in DevOps environments. In other words, through agent-to-agent cooperation, they learn in the environment to adapt to new threats proactively and provide vulnerability control and real-time response. This research focuses on the significant strengths of MADRL, such as the model's ability to scale naturally to many agents, its ease of envisioning different threat levels, and its flexibility in adapting to most threat scenarios. Using simulation results, it can be proved that the proposed MADRL models can be employed to learn security policies, discover unfamiliar patterns, and control risks to achieve effective DevOps security automation. This work demonstrates the extent to which MADRL can help transform the complexity of the new security problems we encounter in delivering software pipelines in today's sophisticated environment.