Zero-Trust Security Enforcement through AI-Powered Anomaly Detection in Cloud Systems

Abstract

The adoption of cloud computing has increased compared to a quick pace, and the sophistication of cyber threats have made the conventional perimeter-based security model to be inadequate. Based on the idea of never trust, always verify, Zero Trust Architecture (ZTA) has proved to be an effective model of securing the cloud environment in modern times through ongoing authentication, minimal privileges, micro-segmentation, and real-time monitoring. This paper examines the central concepts, rational aspects, and design features of Zero Trust in clouds, and the ways it can be compliant with the NIST guidelines. In addition, the article compares and contrasts the more conventional statistical, rule-based, and signature-based approaches to anomaly detection with AI-driven methods that use machine learning and deep learning models such as supervised, semi-supervised, and unsupervised models, autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Incorporation of artificial intelligence in Zero Trust can boost adaptative threat detection, behaviour analytics and automated responses mechanism. Also, the paper focuses on how this applies to cloud systems, remote work forces, IoT, microservices, and SASE environments, and outlines difficulties in implementation, including technical complexity, organizational resistance, financial limitations, and regulatory alignment. The results report the presence of an AI-based anomaly detector and Zero Trust principles as a dynamic, contextual, and resilient security system that able to respond to the changing threat in cloud-based infrastructures.