Autonomous IT Risk Mitigation Using Deep Reinforcement Learning in Cloud Environments

Abstract

Cloud computing now lies at the heart of key enterprise IT, providing scalable, flexible management of resources. Yet, it's the very dynamism and inherent complexity of cloud setups that bring real operational risks in the form of security breaches, outages, and performance dips. Traditional mitigation-risk, through static rules, manual tweaks, or reactive monitoring, frequently fails to keep pace with fast-changing conditions and a growing scale.

This paper develops an intelligent autonomous IT risk mitigation framework with deep reinforcement learning, motivated for cloud environments. It enables intelligent agents to continually monitor the states of the system, estimate risk status, and perform mitigation actions such as resource reallocation, access tightening, and workload throttling. Learning by interaction with the cloud to steer towards optimal policies, the agent proactively reduces risk exposure with no sacrifice of service performance.

We ran simulation-based tests, that emulates enterprise-style cloud scenarios: time-varying workloads, security threats, and resource limitations. The results here demonstrate that DRL-driven mitigation significantly cuts down the number of risk incidents and response times compared to rule-based or purely reactive methods. We also provide easy-to-read, interpretable visuals of the tracking of risk reduction, policy convergence, and the trade-offs with performance. Real-time operation scenarios further illustrate the readiness of this approach for production use.

In all, it can be inferred from the findings that autonomous mitigation guided by DRL represents a scalable and adaptive means of handling IT risk in cloud infrastructures, allowing organizations to move from reactive risk handling to proactive, self-optimizing cloud operations.