Fixing data architecture vs. upgrading detection models
🔍 Security teams often default to retraining AI models when detections fail, but the real root cause is usually upstream data issues. Fragmented telemetry, inconsistent schemas and stale baselines degrade ML effectiveness long before models see events. Standardizing schemas, monitoring data quality at ingestion and applying governance to security telemetry are practical priorities that restore detection reliability without wholesale platform replacements.
