Drift
The degradation of a system's relationship with its objective over time, as conditions move away from those it was built and tested under. Distinguished from a wrong answer: drift produces gradually less right ones.
Why it matters
Applies to the model risk function, and after SR 26-2 to whoever the institution designates for systems outside model risk management — which for agents is everyone or no one, depending on whether anyone made the designation. It bites from the day of deployment and accelerates in regime change, meaning it is worst exactly when the loss is largest. It fails as a control when detection has no pre-committed consequence: a system that flags drift into a meeting three weeks out has been documented, not governed. Of 230 financial services control objectives, only 11 address it.
What it is not
These are routinely confused with Drift. The distinctions are not pedantic — each one has consequences for how a system is governed.
Data drift is inputs shifting. Model drift is the input-output relationship shifting. Inputs can move with the relationship intact.
Failure is discrete and visible. Drift is continuous and is specifically the failure mode that does not announce itself.
Relationships
Typed edges into the rest of the ontology. These are what make the canon traversable rather than merely readable.
| Verb | Target | Meaning |
|---|---|---|
addresses | Oversight | The subject speaks to the object as a question or concern. |
relatedTo | Model Drift | An association too weak or too general for a stronger verb. |
relatedTo | term:kill-criteria-ref | An association too weak or too general for a stronger verb. |
Record
| Canonical identifier | QIS-TERM-00027 |
| Status | Canonical industry term |
| Adoption | Widely used |
| Domain · Layer | MARQUE questions · Governance |
| Origin | MARQUE question 03. Defined in the AIEOG Shared AI Lexicon as AI drift or decay. |
| Semantic aliases | None recorded. |
| First published | 2026-08-02 |
| Last reviewed | 2026-08-02 · 180-day cycle |
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