AI Drift Detection is the practice of continuously monitoring AI systems to identify when their performance degrades due to changes in the underlying data, environment, or usage patterns. Drift is one of the most common causes of AI system failures in production and can introduce security, fairness, and compliance risks.
Types of AI drift include: data drift (the statistical properties of input data change from what the model was trained on), concept drift (the relationship between inputs and outputs changes — e.g., user behavior evolves), model drift (gradual degradation of model performance over time), feature drift (changes in the availability or quality of input features), and label drift (changes in the distribution of target variables).
The enterprise impact of undetected drift is significant: declining accuracy in AI-powered decisions, emerging bias as model performance degrades unevenly across groups, compliance violations if model behavior diverges from documented specifications, customer experience degradation, and security risks from unexpected model behavior.
Drift detection methods include: statistical tests comparing current data distributions to training baselines, performance monitoring against established metrics, automated alerts when drift exceeds defined thresholds, regular model revalidation schedules, A/B testing between current and retrained models, and integration with AI observability platforms for continuous monitoring. Organizations should establish drift monitoring as part of their model governance lifecycle.
