This paper presents PHASE (Passive Human Activity Simulation Evaluation), a novel machine learning framework for quantitatively assessing the behavioral trustworthiness of synthetic user personas that mimic realistic human behavior to enhance the effectiveness of cybersecurity simulation environments (cyber ranges, honeypots, and sandboxes). PHASE analyzes Zeek connection logs to distinguish human and non-human activities with over 90% accuracy, and operates passively without user-side instrumentation or surveillance signatures. Network activities are collected via Zeek network appliances, and a novel labeling technique utilizing local DNS records is proposed. SHAP analysis is used to identify temporal and behavioral features that represent human users, and a case study is presented to identify and improve unrealistic patterns of synthetic users to generate more realistic synthetic users.