In today’s cybersecurity landscape, the traditional perimeter-based defense model has become obsolete, giving rise to the Zero Trust Architecture (ZTA), where no entity—whether internal or external—is automatically trusted. While ZTA provides a robust security posture, its effectiveness heavily depends on accurate and context-aware access evaluation. Conventional authentication techniques, such as static credentials and multi-factor authentication (MFA), are often insufficient to detect subtle identity compromise or insider threats. This paper introduces a novel framework that leverages Artificial Intelligence (AI) and behavioral fingerprinting to enable continuous and adaptive access evaluation within a Zero Trust environment. Behavioral fingerprinting, which includes unique user-specific patterns such as keystroke dynamics, mouse movement patterns, application access sequences, and response times, is used to construct a dynamic trust profile for each user. Our system continuously collects telemetry data, extracts behavioral features, and uses supervised and unsupervised learning models to assess risk in real-time. By combining these insights with contextual parameters (such as geolocation, device hygiene, and network indicators), our AI engine computes a Behavioral Trust Score (BTS) to grant, deny, or conditionally allow access. The results from our prototype demonstrate a significant improvement in detecting anomalous behavior compared to traditional rule-based systems, with a notable reduction in false positives and latency. Our contributions aim to enhance the granularity and responsiveness of Zero Trust security models while maintaining user transparency and compliance.

Zero Trust Architecture Behavioral Fingerprinting Adaptive Access Control Behavioral Trust Score User Behavior Analytics Insider Threat Detection Continuous Authentication Federated Learning AI in Cybersecurity