Intelligent Risk Assessment in Multi-Tenant Cloud Environments Using Deep Reinforcement Learning and Adaptive Security Policies

The rapid proliferation of multi-tenant cloud environments has revolutionized IT service delivery, offering significant cost savings and scalability through shared infrastructure. However, this shared nature simultaneously amplifies security vulnerabilities, exposing tenants to complex and evolving cyber threats. Addressing these challenges necessitates risk assessment frameworks capable of continuous learning and dynamic adaptation to the ever-changing cloud threat landscape. This paper presents an innovative framework that harnesses deep reinforcement learning (DRL) to perform intelligent risk assessment, coupled with adaptive security policies tailored for multi-tenant clouds. The DRL agent continuously interacts with the cloud environment, learning to identify subtle threat patterns and evolving attack vectors in real time. By integrating these insights, the framework dynamically adjusts security policies based on the assessed risk levels, tenant-specific contexts, and operational conditions, thereby optimizing protection without compromising performance. Comprehensive experiments demonstrate that this approach significantly enhances the accuracy of threat detection and improves the efficiency of policy enforcement compared to conventional static methods. Ultimately, the proposed model elevates the security posture of multi-tenant cloud environments by delivering proactive, context-aware risk management that can swiftly respond to emerging threats and evolving tenant behaviours. This contribution offers a promising direction for future cloud security solutions aimed at safeguarding increasingly complex and dynamic cloud ecosystems.