For Heads-of · Practitioner
AI model drift and degradation
A deployed model's performance silently degrades over time as the input distribution shifts away from its training and validation data.
- high
- operational-resilience
- model-drift
- monitoring
How it happens
The real-world data a model sees gradually diverges from what it was trained and validated on, customer behaviour shifts, a new product category appears, a market condition changes, and the model's accuracy degrades without any change to the model itself.
Why it matters
Drift is a slow failure, not a sudden one, so it tends to be caught late, by an external complaint or an audit, rather than by the system that's supposed to be monitoring it.
Mitigating controls
The controls that address this risk, ranked by effectiveness.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| NIST AI Risk Management Framework (AI RMF 1.0) | Measure | Measure |
| ISO/IEC 42001:2023 | Clause 9 | Performance evaluation |
Related resources
The external sources behind this risk, from the Resources library.