Data Drift

Data Drift is a change in the statistical properties of model inputs after deployment — meaning the data the model sees in production differs from the data it was trained on. Common forms include feature drift (input distributions shift), covariate shift (one or more features shift), and label drift (the proportion of outcome categories changes). Real-world examples include fraud-detection models trained on pre-pandemic data encountering very different transaction patterns post-pandemic, medical AI trained on one hospital's patient population encountering different demographics elsewhere, and language models trained on older text encountering newer vocabulary. Detection methods include population stability index (PSI), Kolmogorov-Smirnov tests, Jensen-Shannon divergence, and embedding-distance metrics. Tools include Evidently AI, Arize, WhyLabs, and built-in capabilities in MLOps platforms. AI governance, AI compliance, and AI risk management programs require continuous data-drift monitoring as a core responsibility for every production AI system — supporting responsible AI deployment across long-running enterprise AI workloads.

Centralpoint Centralises Drift Signals Across Models: Oxcyon's Centralpoint AI Governance Platform captures input distributions across OpenAI, Gemini, Llama, and embedded models — making data drift identifiable. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds drift-aware chatbots into your portals via one JavaScript line.


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