ki-monitoring-konzept

Create an AI monitoring concept by defining risk classes, KPI metrics for drift and fairness, logging protocols, human-in-the-loop roles, escalation matrices, and audit processes aligned with ISO/IEC 42001 and NIST AI RMF requirements.

Track KPI metrics covering performance, data drift, concept drift, fairness, robustness, availability, and logging to ensure operational reliability and continuous compliance for production AI systems.

Yes, an AI monitoring concept includes human-in-the-loop oversight and escalation processes to satisfy GDPR requirements including Art. 22 DSGVO and Art. 32 DSGVO for automated decision-making and data security.

Set up post-market surveillance logging by defining robust logging protocols, threshold values, and functional requirements that satisfy Art. 12 KI-VO and Art. 14 KI-VO for high-risk AI systems.

AI governance audit processes require defined roles across researcher, drafter, and reviewer functions, plus documentation references specifying escalation matrices, KPI tables, and threshold values to verify compliance.

Yes, the monitoring concept covers risk classes for both high-risk AI systems and others, specifying functional requirements, thresholds, and documentation references applicable across different risk levels.