QM-004HochAnbieterPräventiv
Verfahren zur Datenverwaltung
Anbieter müssen über dokumentierte Datenverwaltungsverfahren verfügen, die die Erfassung, Aufbereitung, Nutzung und Aufbewahrung von Daten während des gesamten Lebenszyklus des KI-Systems abdecken und mit den Data-Governance-Anforderungen von Artikel 10 im Einklang stehen.
Artikel:Article 17(1)(c)Article 10
Nachweisbeispiele
- Data management procedure SOP
- Data lifecycle flowchart
- Data quality gate checklist
Normen
ISO 42001:2023 §8.2ISO/IEC 5259
Verwandte Kontrollen
- QM-001Quality Management System EstablishmentArticle 17(1) requires providers of high-risk AI systems to put in place a quality management system that ensures compliance with the requirements of the…
- QM-002Regulatory Compliance StrategyProviders must establish and document a strategy for achieving and maintaining compliance with applicable regulatory requirements, including the EU AI…
- QM-003Design Control ProceduresProviders must implement documented design control procedures that ensure regulatory and performance requirements are systematically incorporated from the…
- QM-005Staff Training and Competency ProceduresArticle 17(1)(d) requires providers to implement procedures for training personnel involved in AI system development, testing, and monitoring, with…
- QM-006Pre-Market Testing and Validation ProceduresProviders must implement documented procedures for pre-market testing and validation of high-risk AI systems, including the metrics to be achieved, the…
- QM-007Post-Market Monitoring PlanArticle 17(1)(f) requires providers to implement a post-market monitoring plan that specifies the data to be collected, the monitoring frequency, the…
Verwandte KI-VO-Begriffe
- Quality Management SystemA documented system that providers of high-risk AI systems must establish, implement, document, and maintain covering: the regulatory compliance strategy, design and development processes, data governance procedures, risk management, post-market monitoring, and incident reporting.
- Data GovernancePractices and policies applicable to training, validation, and testing data sets used for high-risk AI systems, covering the design choices, data collection, and preparation processes, and ensuring datasets are relevant, sufficiently representative, and free of errors and complete.
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