Model health monitoring is the continuous assessment of artificial intelligence (AI) or machine learning (ML) systems to ensure they function as intended. In cybersecurity, this process involves tracking model performance, accuracy, and behavior to detect anomalies, data drift, or unexpected outputs that may signal a compromise or attack. By regularly evaluating models, organizations can quickly identify potential threats, such as adversarial attacks or unauthorized modifications, and take corrective actions. Effective model health monitoring helps maintain the reliability, security, and trustworthiness of AI-driven cybersecurity tools, reducing the risk of undetected breaches or failures.