The false positive rate of AI models for detecting ENS domain spoofing in phishing attacks varies depending on the model and dataset. Studies on phishing detection report false positive rates as low as 0.04% for Support Vector Machines and Naïve Bayes, with accuracies reaching 99.96%. Ensemble models like Random Forest and XGBoost achieve similar precision, with false positive rates below 1%. However, sophisticated spoofing techniques, such as AI-generated phishing, can increase false positives due to subtle domain manipulations. Advanced models like EXPLICATE, using explainable AI, report 98.4% accuracy but highlight challenges in distinguishing legitimate urgent communications. Continuous training and feature reduction are critical to minimizing false positives in ENS domain spoofing detection.