Monitor the quality and reliability of data feeds powering your AI systems. Poor feed quality is a leading indicator of model drift and regulatory risk.
AEGISFeed Health
WHO
Data Engineer or Model Risk Analyst — monitoring the health of data feeds that AI systems depend on
WHEN
When a model alert fires due to data quality, during scheduled feed reviews, or before model validation
WHY
Data feed failures are a leading cause of model drift and decision errors. SR 11-7 requires that data inputs to models are monitored and validated.
HOW
1. Review feed status by model 2. Identify stale or failed feeds 3. Check data quality metrics 4. Escalate feed failures that affect high-risk models