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Data drift

By Updated

A change in the distribution of the inputs a system sees in production relative to the data it was validated on, such as a new customer segment or a changed upstream form 1. It shows in the inputs before any label arrives, so it is monitored directly.

Developed in
ch. 11, Contrast pairs
Chapters
ch. 11, AI Defined · ch. 15, Deployment
Contrast with
Concept drift
Source
1 numbered reference, listed below

Commonly confused

  • Data drift versus Concept drift

    Data drift
    A change in the distribution of the inputs a system sees in production relative to the data it was validated on, such as a new customer segment or a changed upstream form.
    Concept drift
    A change in the relationship between a system's inputs and the correct output, so the same input should now get a different answer.

    The differenceThe inputs change, against the relationship between inputs and the right answer changing.

    Why it mattersThe first shows in input monitors before labels arrive; the second only in outcomes on fresh labels.

Where it is used

3 chapters of the Body of Knowledge use the term. Each link opens the first section that does.

Data drift: mentions per chapter3 mentions of the term across 3 of the 23 chapters of the Body of Knowledge; the chapters the glossary cross-references are drawn solid.Cross-referenced chapterOther chapterCross-referenced, not named11 AI Defined: 1 mention11 AI Defined113 Risk Management: 1 mention13 Risk Management115 Deployment017 Incidents: 1 mention17 Incidents1No mentions in the other 19 chaptersSource: the Body of Knowledgev0.5.0
Data drift: mentions per chapter3 mentions of the term across 3 of the 23 chapters of the Body of Knowledge; the chapters the glossary cross-references are drawn solid.Cross-referenced chapterOther chapterCross-referenced, not named0001020304050607081011 AI Defined: 1 mention1111213 Risk Management: 1 mention11314151617 Incidents: 1 mention117181920212223Source: the Body of Knowledge v0.5.0
Mentions per chapter How often each chapter names the term, with the chapters the glossary cross-references drawn solid. The longest bar is where to read the term in use; bars spread across many chapters mark a term that runs through the discipline.
Mentions as a table
Mentions of Data drift per chapter
ChapterMentionsCross-referenced
00 Preface0No
01 Definition0No
02 Why Now0No
03 Values & Principles0No
04 The Stack0No
05 Patterns0No
06 The Role0No
07 Maturity Model0No
08 Regulatory Map0No
10 Reading List0No
11 AI Defined1Yes
12 Governance Program0No
13 Risk Management1No
14 Development0No
15 Deployment0Yes
16 Fairness & XAI0No
17 Incidents1No
18 EU AI Act0No
19 Privacy & AI0No
20 Existing Law0No
21 AI Laws Worldwide0No
22 Principles & Standards0No
23 AI Agents0No
Data drift: related terms and patternsThe term at the centre; related terms on solid edges, the terms it is contrasted with on dashed edges, and the patterns that use it most in the colour of their layer.RelatedContrastData driftRelated terms (4)Drift · RelatedDriftExplainability · RelatedExplainabilityOpacity · RelatedOpacityTransparency · RelatedTransparencyContrast with (1)Concept drift · ContrastConcept drift
Data drift: related terms and patternsThe term at the centre; related terms on solid edges, the terms it is contrasted with on dashed edges, and the patterns that use it most in the colour of their layer.RelatedContrastRelated terms (4)Drift · RelatedDriftExplainability · RelatedExplainabilityOpacity · RelatedOpacityTransparency · RelatedTransparencyContrast with (1)Concept drift · ContrastConcept driftData drift
The term's neighbourhood Terms related to this one on solid edges, terms it is often confused with on dashed edges, and the patterns that use it most in the colour of their layer. Follow a dashed edge to check the distinction before you use either term.
Neighbourhood as a table
Neighbourhood of Data drift
FamilyItemRelation
Related termsDriftRelated
Related termsExplainabilityRelated
Related termsOpacityRelated
Related termsTransparencyRelated
Contrast withConcept driftContrast
  • Explainability

    In NIST's framing, a representation of the mechanisms behind a system's operation: how a decision was made.

  • Opacity

    The inability of a person to follow how a system reached an output.

  • Transparency

    In NIST's framing, how far information about an AI system and its outputs reaches the people who interact with it: what happened.

  • Drift

    The gradual divergence of a model's inputs, outputs or performance from its validated baseline over time; a runtime signal that a control or eval must catch.

Sources

  1. [1] "Learning under Concept Drift: A Review" (IEEE TKDE 31(12); detection, understanding and adaptation; arXiv 2004.05785). Lu et al.. 2018. https://arxiv.org/abs/2004.05785 (verified: primary)

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Data drift. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/data-drift. CC BY 4.0

BibTeX

@misc{aige2026datadrift,
  author  = {Jorge García Aibar},
  title   = {{Data drift}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/data-drift}
}