Model drift refers to the degradation of an AI model’s performance over time due to changes in the data it encounters during deployment. As environments evolve, user behaviour shifts, or new types of inputs emerge, a model may become less accurate, reliable, or relevant, posing a risk to both operational performance and safety.

In AI assurance, model drift is a critical concern because even a well-validated model can fail if the real-world context it operates in shifts significantly from the training environment. For instance, an object detection model deployed in a defence surveillance system may start encountering new camouflage techniques or unfamiliar terrain, resulting in increased false negatives.

Model drift can be categorised into two main types:

• Data drift: Occurs when the input data distribution changes over time, while the relationship between inputs and outputs remains stable.

• Concept drift: Occurs when the relationship between inputs and outputs changes, often due to shifts in operational context, policy, or external behaviour.

Effective assurance involves monitoring AI systems for signs of drift, which may include:

• Increased error rates or misclassifications

• Declining performance metrics

• Changes in input patterns or user feedback trends

To address drift, assurance teams implement mechanisms such as:

• Drift detection tools that compare incoming data to historical baselines

• Retraining pipelines that regularly update the model using fresh data

• Fallback protocols that allow for safe system behaviour during degraded performance

Documenting how drift is monitored and managed is essential for regulatory compliance and stakeholder confidence. For high-stakes applications, real-time alerts and human oversight may be required when drift is detected.

Model drift is particularly relevant in dynamic environments such as autonomous navigation, financial fraud detection, or emergency response systems — where even subtle environmental changes can affect model performance. Assurance practices ensure that drift does not lead to silent failures or unintended harms.