Autonomous Cyber Defense Using Reinforcement Learning Across Scalable Multi-Cloud Enterprise Environments

Abstract

The increasing adoption of scalable multi-cloud infrastructures has created complex cybersecurity challenges for modern enterprises. Organizations distribute applications, data, workloads, and services across multiple cloud providers to achieve scalability, availability, flexibility, and cost optimization. However, this distributed architecture expands the attack surface and makes conventional security approaches increasingly difficult to manage. Manual threat investigation and rule-based response mechanisms may be unable to respond rapidly to dynamic and sophisticated cyber threats. This paper proposes an Autonomous Cyber Defense framework based on Reinforcement Learning (RL) for scalable multi-cloud enterprise environments. The proposed framework uses reinforcement learning agents to continuously observe security conditions, evaluate potential threats, select appropriate defensive actions, and learn from the outcomes of previous decisions. The architecture integrates multi-cloud monitoring, identity and access management, anomaly detection, security orchestration, zero-trust principles, automated containment, and cyber-resilience mechanisms. A centralized policy layer coordinates distributed security agents while maintaining cloud-specific enforcement capabilities. The methodology employs a design-oriented research approach involving threat modeling, simulated multi-cloud environments, RL agent development, scenario-based experimentation, and comparative evaluation. Performance is assessed using detection accuracy, response time, false-positive rate, resource utilization, attack containment, recovery time, and policy compliance. The proposed framework aims to improve adaptive threat response, reduce security-operation workload, and strengthen enterprise resilience while maintaining human oversight over high-impact security decisions.