Versatile AI Risk Assessment

Glossary Threats and weaknesses

Model and data drift

Changes in data or context of use can affect the quality of model outputs.

As of: September 2026 · Glossary with 28 terms

What is Model and data drift?

Drift is the divergence between model and reality: input data shifts away from the training data, terms change meaning, usage patterns move. This can reduce the quality of results even if the system itself remains unchanged.

Drift can develop gradually and remain unnoticed. Regular measurements help detect changes early. Record reference values at release and define when the responsible people should intervene. With purchased models there is the added point that the provider can swap the model out in the background.

OperationReliabilityMonitoring

More terms from the subject area Threats and weaknesses.

Prompt Injection Manipulated input or planted content redirects a language model. Hallucination (confabulation) Plausible sounding but factually wrong output, inherent to the technology. Data Poisoning Manipulated training or reference data steers a model wrong on purpose.

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“Model and data drift”. Versatile AI Risk Assessment, glossary of AI risk analysis, as of September 2026.
https://www.versatile-ai-risk-assessment.com/en/wissensbasis/glossary/drift/

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