AI-driven adaptive authentication for zero trust security architectures

Zero Trust Security Architectures (ZTSA) represent a paradigm shift in cybersecurity by eliminating implicit trust and enforcing continuous verification. In this paper, we introduce an AI-driven adaptive authentication framework that leverages real-time risk assessment through advanced mathematical modeling and machine learning techniques. Our framework integrates multiple data sources—including user behavior, device integrity, and external threat intelligence—to dynamically adjust authentication protocols. We provide a rigorous mathematical formulation, detailed experimental analysis, algorithm pseudocode, and discussions on ethical, regulatory, and deployment challenges. Extensive ablation studies and sensitivity analysis are included to compare our approach with baseline systems and to understand the impact of key parameters. Additionally, we include scientific plots such as an ROC curve and a calibration plot to further evaluate model performance.