Zero Trust Cloud Security Architecture Using Deep Reinforcement Learning for Adaptive and Autonomous Cyber Defense

Abstract

Zero Trust Cloud Security Architecture has emerged as a critical approach for protecting distributed enterprise environments in which users, devices, applications, workloads, and data operate across multi-cloud, hybrid-cloud, and cloud-native infrastructures. Traditional rule-based security mechanisms often struggle to respond effectively to rapidly changing threats, dynamic workloads, and complex access relationships. This paper proposes a Zero Trust Cloud Security Architecture using Deep Reinforcement Learning (DRL) to enable adaptive and autonomous cyber defense. The proposed architecture integrates continuous identity verification, least-privilege access control, micro-segmentation, behavioral analytics, threat intelligence, security policy enforcement, and DRL-based decision-making. The DRL agent continuously observes security states, evaluates contextual risk, and selects defensive actions such as access restriction, policy modification, workload isolation, session termination, traffic redirection, and additional authentication. A reward mechanism balances security effectiveness, service availability, response latency, and operational cost. The methodology combines cloud security telemetry, simulated attack scenarios, reinforcement learning, and performance evaluation using detection accuracy, response time, false-positive rate, policy adaptation, and resource overhead. The proposed approach aims to transform Zero Trust from a predominantly static security model into an intelligent, continuously adaptive defense system capable of autonomous response to evolving cyber threats.