Integrating Zero Trust Architecture with Automation and Analytics for Resilient Cybersecurity

In response to the limitations of traditional perimeter-based security models and existing Zero Trust implementations focused primarily on federated identity or distributed learning, this paper proposes a novel, unified cybersecurity framework that tightly integrates Zero Trust Architecture (ZTA) with risk-adaptive automation and machine learning-driven analytics. Unlike prior works, the proposed model introduces a Risk-Adaptive Access Control (RAAC) system that dynamically computes access decisions using realtime contextual and behavioral risk scores. It also leverages unsupervised anomaly detection via Isolation Forests to identify deviations from normal activity, triggering automated incident responses through a customizable SOAR module. The framework was prototyped using open-source tools and evaluated on benchmark cybersecurity datasets simulating diverse user behaviors and access patterns. Compared to traditional static access control systems, the integrated model demonstrated significant gains: 96.4% detection accuracy, 3.1% false positive rate, 2.3 seconds average response time, and 94.6% access decision precision. These outcomes underscore the model's superior ability to detect advanced threats, minimize response latency, and adapt to evolving risk profiles without human intervention. This work contributes a scalable and modular solution for enterprises and government environments, offering a resilient cybersecurity approach that evolves in real time. Deployment considerations and pathways for incorporating predictive analytics are also discussed.