A Framework for Autonomous, Cross-Cloud Threat Mitigation Using Multi-Agent Reinforcement Learning

Abstract

The rapid enterprise adoption of multi-cloud, microservice architectures introduces unprecedented complexity and security challenges. Traditional, reactive security models are proving inadequate, as code changes can propagate to global production systems within minutes, leaving minimal time for after-the-fact audits. Existing security solutions often operate in silos, failing to provide a coordinated and autonomous defense posture capable of addressing threats that span heterogeneous cloud environments. This paper introduces a novel framework for autonomous, cross-cloud threat mitigation that utilizes Multi-Agent Reinforcement Learning (MARL). In our proposed system, lightweight, self-defending artificial intelligence agents are deployed within each cloud environment to act as intelligent sentinels inside the software-delivery pipeline. These agents learn collaboratively to identify and remediate security risks in real-time, functioning as self-healing remediation agents. Through simulated multi-cloud failure scenarios, we demonstrate that this approach can significantly reduce mean-time-to-resolution for security incidents, projecting improvements comparable to the 60\% reduction in vulnerability patch time observed in related empirical studies.