Autonomous Identity-Based Threat Segmentation in Zero Trust Architectures

Abstract

Zero Trust Architectures (ZTA) fundamentally redefine network security by adopting a "trust nothing, verify everything" approach that requires identity verification for all access. Conventional discrete access control measures have proven inadequate since they do not consider evolving user activities and contextual threats, leading to internal threats and enhanced attacks. This research applies the proposed AI-driven, autonomous, identity-based threat segmentation in ZTA, along with real-time identity analytics for fine-grained, real-time mechanisms. Some of the sharp practices include using the behavioral analytics approach to provide real-time risk scores, such as analyzing the patterns used for logging into the system, the access sought, and the resources used. Permissions are adjusted using machine learning models that take into account context-aware factors like geolocation, device type, and access time. Automated threat segmentation helps analysts identify multiple compromised identities in real-time, thus minimizing the likelihood of a breach advancing. The system's use cases are based on real scenarios; for example, insider threats in global offices demonstrate how compromised accounts are detected and locked. This work outlines measures to address privacy issues, false positives, and scalability concerns. This research enhances the security of other critical areas of computer systems by providing dynamic access governance, minimizing insider threats, and supporting dynamic policy enforcement while ensuring that the needed balance between security and user productivity remains a top priority. We prove via comparative analyses that the model is precise and scalable.