QM-004HighProviderPreventive
Data Management Procedures
Providers must have documented data management procedures covering the acquisition, preparation, use, and retention of data throughout the AI system lifecycle, aligned with Article 10 data governance requirements.
Articles:Article 17(1)(c)Article 10
Evidence Examples
- Data management procedure SOP
- Data lifecycle flowchart
- Data quality gate checklist
Standards
ISO 42001:2023 §8.2ISO/IEC 5259
Related controls
- 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…
Related EU AI Act terms
- 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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