data drift
This glossary entry explains data drift 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
Data drift occurs when the real-world data your AI model receives changes over time compared to the data it was originally trained on. This mismatch causes the model's predictions to become less accurate and reliable, even though the model itself hasn't changed.
Why Chief AI Officers Care
Data drift can silently degrade business performance, leading to poor customer experiences, incorrect business decisions, and potential compliance violations. Without monitoring for drift, organizations may unknowingly rely on AI systems that are making increasingly inaccurate predictions, potentially causing significant financial or reputational damage.
Real-World Example
A credit scoring model trained on pre-pandemic data suddenly becomes less accurate during COVID-19 because customer spending patterns, employment rates, and financial behaviors changed dramatically. The model continues generating credit scores, but they no longer reflect actual risk levels, potentially leading to increased defaults or missed opportunities.
Common Confusion
People often confuse data drift with model degradation due to technical bugs or assume that if a model worked well initially, it will continue working indefinitely. Data drift is specifically about changes in the input data patterns, not problems with the model's code or infrastructure.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, data drift commonly occurs when patient demographics, treatment protocols, or diagnostic equipment cha...
Finance: In finance, data drift commonly occurs when market conditions, customer behaviors, or economic environments shift from t...
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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
"The change in model input data that leads to model performance degradation."Source: Microsoft_Azure_documentation
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