monitoring
This glossary entry explains monitoring for AI governance and model risk programs. The sections below summarize what the term means in plain language, why chief AI officers and cross-functional committees track it, where teams often get confused, and—when you are signed in—how it shows up across major industries and in expectations tied to the EU AI Act and NIST AI RMF. Use related links at the end of the page to explore neighboring concepts without losing context.
What It Means
Monitoring is the ongoing process of checking whether your AI systems, vendors, and processes are performing as expected and meeting your requirements. It involves regularly observing key metrics, behaviors, and outcomes to catch problems early and ensure everything stays on track. Think of it as your early warning system that tells you when something is starting to go wrong before it becomes a major issue.
Why Chief AI Officers Care
Without proper monitoring, AI systems can drift in performance, develop biases, or fail silently, leading to poor business decisions, regulatory violations, or customer harm. Monitoring is essential for maintaining AI system reliability, demonstrating compliance to auditors, and catching issues like model degradation or data quality problems before they impact business operations. It's also your primary tool for proving to executives and regulators that your AI systems are working safely and effectively.
Real-World Example
A bank's AI loan approval system is monitored daily for approval rates, demographic bias, and prediction accuracy. When monitoring alerts show the approval rate for certain zip codes suddenly dropped 15% compared to historical patterns, the team discovers a data pipeline error that was feeding incorrect income data, allowing them to fix it before it affects more customers or triggers regulatory scrutiny.
Common Confusion
People often confuse monitoring with testing - monitoring is continuous observation of live systems in production, while testing is checking systems before they go live. Another common mistake is thinking monitoring is just about technical performance metrics, when it should also track business outcomes, fairness, and compliance measures.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, monitoring involves continuously tracking AI system performance against clinical outcomes, patient safety...
Finance: In finance, monitoring involves continuously tracking AI model performance against risk thresholds, regulatory requireme...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Examination of the status of the activities of a supplier and of their results by the acquirer or a third party."Source: IEEE_Soft_Vocab
"Continual checking, supervising, critically observing or determining the status in order to identify change from the performance level required or expected."Source: SP800-160
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