AI Driven Behavioral Biometrics for Adaptive Zero Trust Architectures

Traditional perimeter-based security models have proven inadequate against modern threats, leading organizations to adopt Zero Trust (ZT) architectures that continuously verify every access request. In parallel, behavioral biometrics, which analyze users’ unique patterns of interaction (e.g. typing rhythms, mouse dynamics, touch gestures) offer continuous authentication without explicit user actions. This paper explores integrating AI driven behavioral biometric profiling into Zero Trust frameworks to enhance adaptive, risk aware authentication. We propose a system design where user device behavioral streams are processed by machine learning models to produce real time trust scores, which feed into ZT policy decision points. The research questions address (1) how effectively AI can model user behavior for continuous verification, (2) how to architect this within ZT policy engines, and (3) the performance and usability tradeoffs in high risk sectors. Through a comprehensive literature review (pre-2023 IEEE/Springer sources) and simulated evaluation, we examine use cases in banking, defense, and healthcare. Our results, illustrated in accompanying tables and figures, show that integrating behavioral biometrics can achieve high authentication accuracy (≈95–99%) with low false accept/reject rates (FAR, FRR ≤3%), while improving dynamic risk assessment. We discuss how these metrics compare to existing studies and the implications for Zero Trust policy enforcement. This work demonstrates that AI enhanced continuous authentication can significantly bolster Zero Trust defenses by providing adaptive, context aware access control, while highlighting challenges in privacy and usability.