Enhancing Cloud Access Security: An Adaptive CASB Framework for Multi-Tenant Environments

Abstract

In the context of multi-tenant cloud systems, security concerns, data loss, and unauthorized access are becoming more of a challenge for enterprises as they increase their use of SaaS platforms. In addition to traditional perimeter defenses, modern dispersed cloud ecosystems need security measures to prevent unauthorized access by users using diverse devices and ever-changing networks. This study introduces a context-aware CASB system that uses user identity profiling, behavioral analytics, and risk scoring to dynamically enforce data protection policies. In response to context signals such as access location, device trust level, previous activity patterns, and abnormal behavior, the suggested system constructs an adaptive policy enforcement layer capable of fine-tuning access decisions during runtime. An anomaly detection layer that is built on machine learning that is specifically designed to learn about each user's baseline and warn them to potentially dangerous actions before any data is stolen or misused. While keeping operational overhead minimal, this architecture seamlessly integrates with business identity management systems, Data Loss Prevention (DLP) solutions, and Next-Generation Firewalls (NGFWs). The result is a single point of policy orchestration. Experiments conducted on cloud settings with several tenants have shown that stringent security measures may be implemented with a 35% decrease in the amount of unwanted access while incurring just an extra 8% latency cost, as compared to other solutions. In order to protect companies from new cloud security risks, this article presents a scalable and modular paradigm for CASB that uses policy-driven access control that takes context into account. This approach lays the groundwork for smart cloud access governance that adapts to user intent, behavior patterns, and danger environment dynamics.