An enterprise is establishing an AI Trust, Risk, and Security Management (AI TRiSM) program. As part of this program, the security team needs to implement continuous monitoring of AI model outputs for concept drift and anomalous behavior changes post-deployment. Which Azure service provides model monitoring capabilities for production AI models?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you use Azure Machine Learning model monitoring because it continuously checks deployed models in production for data drift, prediction drift, and data quality issues. It compares production data against a reference baseline and alerts you when statistical drift shows up, which is exactly what AI TRiSM continuous monitoring needs.
Full Explanation
Azure Machine Learning model monitoring is correct because its model monitoring feature (preview) continuously monitors deployed models for data drift, prediction drift, and data quality issues in production. It compares production data distributions against a reference baseline and sends alerts when statistical drift is detected, which is a core requirement of AI TRiSM's continuous monitoring pillar. Microsoft Defender for Cloud CSPM is wrong because it assesses the security posture of Azure resources, such as misconfigurations and compliance, not ML model output quality and drift. Azure Application Insights is wrong because it is an APM (Application Performance Monitoring) tool for web applications; while it can monitor API latency and failures, it does not perform ML-specific statistical drift detection on model inputs and outputs. Microsoft Sentinel analytics rules are wrong because they detect security threats from log data and are not designed for statistical model performance monitoring.