QM-010MediumProviderCorrective
Continuous Improvement Procedures
Providers must implement procedures for continuous improvement of the quality management system, including internal audits, management reviews, nonconformity management, and corrective action processes.
Articles:Article 17(1)Article 72
Evidence Examples
- Internal audit schedule and reports
- Corrective action log
- Management review minutes
Standards
ISO 42001:2023 §10.3ISO 9001:2015 §10.3
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-004Data Management ProceduresProviders must have documented data management procedures covering the acquisition, preparation, use, and retention of data throughout the AI system…
- 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…
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.
- Post-Market MonitoringProactive collection and review of experience gained from the use of high-risk AI systems, to identify any need for corrective or preventive actions and ensure continued compliance throughout the system lifecycle.
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