A Multi-Tenant Cloud Security Framework Using Zero-Trust Architecture and AI-Based Anomaly Detection

Abstract

The concept of cloud computing has revolutionized information processing across the world due to the services that are elastic, scalable and cost effective. Nevertheless, multi-tenancy brings in critical risks, such as, unauthorized lateral movement, data leakage by tenants, insider threats, privileged escalation and advanced cyberattacks. Existing perimeter security models fail to perform properly in dynamic, distributed and cloud environments, in which there is no clearly defined trust boundaries. In this paper, I suggest a Multi-Tenant Cloud Security Framework (MTCSF), which integrates Zero-Trust Architecture (ZTA) and AI-Based Anomaly Detection to develop an active and adaptable defense system. The framework provides the continuity of authentication, the regulation of least-privilege access, micro-segmentation, and encryption of the lifecycle data. An anomaly detection engine is a hybrid AI-based system of supervised deep learning and unsupervised clustering that determines the anomalies (insider abuses or anomalous patterns of resource access) in real time. The system will also utilize a Security Orchestration, Automation, and Response (SOAR) layer that will automatically start mitigation actions. The simulated multi-tenant cloud testbed with benchmark datasets (NSL-KDD and CIC-IDS 2021) of simulated threat scenarios such as DDoS, privilege abuse, and malicious file transfer were experimented. Findings indicate better detection and were lower than false positives with the existing cloud security systems. The most important key performance indicators are a detection rate of 98.21 percent, a false-positive rate of 1.79 percent, a shorter switching time to threat responses, and low latency overhead (less than 3 ms). The suggested MTCSF fulfills architectural and operational security vulnerabilities. The contributions involve: (1) a Multi-tenant access control Zero-trust enforcement model, (2) a hybrid AI abnormality detector to be incorporated into security processes, Detection and isolation of threats tailored to the homes occupied by tenants. This study has shown that trust-agnostic and intelligent control are beneficial to improve confidentiality, integrity, and availability of multi-tenant clouds and to satisfy regulatory compliance and tenant isolation needs. Optimized model scalability and increased cross-cloud sharing of threat intelligence will be optimized in future work