SECAI Core · phase 15 of 15 · noun
Data drift
A phenomenon where the statistical properties of input data change over time, potentially reducing the accuracy and reliability of machine learning models or analytical systems.
The Explain card
- Plain English
- Data drift happens when the statistical properties of the input data a model receives change over time, so the model is seeing a world it was not trained on and its accuracy quietly declines.
- Example
- A phishing detection model trained on human-written email is increasingly fed messages written by generative AI. The vocabulary and structure of incoming mail shift, and detection rates fall without any change to the model.
- Why it matters
- Drift degrades defences silently and can be induced deliberately by attackers who feed a system unusual inputs. Monitoring input distributions is how you notice.
- Hook
- The map is the same, but the territory moved.
Word knowledge
How the word is built, where it came from, and what it sits beside in memory.
In a sentence
The monitoring pipeline detected data drift after the production camera began capturing images under newly installed LED lighting.
Why these words
- data Latin data, things given identifies the observations being monitored
- drift Old English drīfan, to drive expresses gradual movement away from an earlier state
Where it came from
- Origin
- an English compound formed from Latin-derived data and Germanic drift
- Entered the language
- early 21st century
- What changed
- Drift developed from physical movement into a figurative term for gradual change, then joined data in machine learning terminology.
How it is spelled
- Pattern
- written as two separate lowercase words
- Pattern
- uses the noun-plus-noun pattern common in technical compounds
- Breaks the pattern
- data is historically plural in Latin but is often treated as a mass noun in modern English
Spelled like
- concept drift
- model drift
- dataset shift
Broken into chunks
-
data
- datum
- database
- metadata
-
drift
- driftwood
- snowdrift
- drifter
What it sits beside
Same subject
- concept drift
- model monitoring
- distribution shift
- feature distribution
Same shape
- concept drift
- model drift
- population drift
machine learning monitoring terms
- data drift
- concept drift
- model drift
distribution change terms
- covariate shift
- label shift
- dataset shift
Where it sits in the deck
Phase 15: MLOps, Continuous Delivery, and Operational Resilience
Close the loop — operational practices, drift management, and delivery discipline that keep deployed AI systems healthy, current, and continuously improving.
