AI-Driven Zero Trust Architecture: A Scalable Framework for Threat Detection and Adaptive Access Control

Abstract

The rapid growth of complex cyber threats has rendered perimeter-based security models ineffective in protecting the enterprise environment. Zero Trust Architecture (ZTA) has become a paradigm shift in the industry, eliminating implicit trust and mandating rigorous verification for every access request. However, ZTA implementations traditional to this day have limitations associated with scaling, flexibility, and real-time detection of changing patterns of attacks. This study proposes an AI-based Zero Trust platform that leverages artificial intelligence and machine learning to enhance security by expanding threat detection and supporting dynamic access controls across various infrastructural settings, including the cloud, internet-connected devices, and key enterprise systems. This framework proposes multi-layered intelligence that incorporates behavioral analytics, anomaly detection, and dynamic policy orchestration, in which continuous verification and risk-based access decisions are enabled. Its architecture, involving the utilization of AI-based automation and cloud-native scale, is applicable to minimize false positives, address insider and outside threats, and be dynamic to react to contextual risk indicators. The results of the proposed study support the idea that AI integration with ZTA is essential to becoming cyber resilient against advanced cyber-attacks, therefore, creating a scalable, intelligent, and future beyond one cybersecurity model.