What is Model Drift Detection?

Model drift occurs when the inputs or conditions under which an AI model operates change significantly—causing its performance to degrade. There are several types of drift:

• Data drift: Input data characteristics change (e.g., customer behavior, text formats)
• Concept drift: The relationship between inputs and outputs changes
• Model drift: The model itself changes due to retraining, fine-tuning, or updates

If left undetected, drift can cause models to make poor decisions, generate irrelevant content, or behave unpredictably in production. This is especially dangerous in AI systems tied to financial, healthcare, or security-sensitive workflows.

Monitoring for drift ensures that models remain accurate, safe, and aligned with real-world conditions. It also supports compliance by demonstrating control over production AI behavior.

How PointGuard AI Helps:
PointGuard detects drift in models, prompts, and outputs through real-time monitoring and historical comparison. It alerts teams to changes in behavior, usage context, or risk score over time, and integrates this into dashboards and remediation workflows. This ensures models remain trustworthy long after deployment.
Learn more: https://www.pointguardai.com/ai-runtime-defense