Root Cause Analysis
A structured investigation to determine the underlying reasons for AI system failures or unexpected behaviors, guiding corrective actions.
A post-incident methodology - 5 Whys, fishbone diagrams - that traces failure events (e.g., model misclassifications, system outages) back through data pipelines, model logic, and infrastructure layers to identify the primary defect. Root-cause analysis documents findings and remediation plans, ensures corrective actions address systemic issues, and prevents recurrence. Governance requires RCA for any high-severity incident, with executive review of outcomes.
After a recommendation engine began suggesting inappropriate content, the team performed root-cause analysis: they found a data-ingestion script had merged test and production data, skewing training. They fixed the script, revalidated the model, and added unit tests to the ingestion pipeline - eliminating the risk of future cross-environment contamination.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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