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    Model Drift Monitoring

    Ongoing surveillance of input data and model outputs to detect performance degradation post-deployment.

    Reviewed by Christian Espinosa, Founder, Blue Goat CyberLast reviewed May 5, 2026

    Definition

    Drift monitoring tracks data drift (input distributions changing) and concept drift (the relationship between inputs and outputs changing). For locked algorithms this triggers retraining; for adaptive algorithms it gates change-control under a PCCP.

    What this means in practice

    FDA TPLC and PCCP guidance both expect documented drift monitoring with thresholds, alerts, and a corrective-action playbook.

    Primary references

    3 sources
    Link health: 2 verified 1 bot-blocked· last checked 2026-05-09
    1. 1
      FDA AI/ML Action Plan
      Bot-blocked
      FDAfda.gov
    2. 2
      FDA - Software as a Medical Device (SaMD)
      Verified
      FDAfda.gov
    3. 3
      FDA - AI/ML-Enabled Medical Devices
      Verified
      FDAfda.gov

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