Zero Trust Micro-Segmentation in Cloud Environments: A Systematic Literature Review of Strategies, Challenges, and Future Trends

Sammanfattning

Cloud environments, with dynamic scalability and distributed architecture, face security challenges such as lateral movement, unauthorized access, insider threats, and Advanced Persistent Threats (APTs). Traditional perimeter-based defences struggle to address these risks, as they lack visibility and control over east–west traffic between cloud endpoints (VMs/containers). Zero Trust Architecture (ZTA) mitigates these risks through least privilege access and continuous verification. Micro-segmentation, a pillar of ZTA, divides networks into fine-grained isolated segments to enforce granular security policies, thereby reducing the attack surface, containing breaches, enhancing control over east-west traffic, and mitigating lateral movement. This systematic literature review (SLR) analyses 39 primary studies (2016–2025) to summarize the latest techniques and algorithms used to implement ZTA micro-segmentation to secure cloud environments and prevent attacks, while highlighting their security benefits and operational challenges. This review identified 8 different techniques with dominance of 29.1% Software-Defined Networking (SDN) and 27.3% Artificial Intelligence (AI). Research trends point towards techniques such as AI-driven adaptive segmentation and security policies, blockchain-based decentralized trust models, and quantum computing-based anomaly detection, enabling more autonomous, resilient, and context-aware ZTA micro-segmentation in cloud environments.