DG-008MediumProviderPreventive
Data Versioning and Change Control
Providers must maintain versioned records of training, validation, and testing datasets used in each version of the AI system, enabling reproducibility of results and traceability of data changes that may affect system behaviour.
Articles:Article 10(2)Article 12(1)
Evidence Examples
- Dataset version registry
- Data change log
- Dataset hash/fingerprint records
Standards
ISO 42001:2023 §8.2ISO/IEC 5259-3
Related controls
- DG-001Training Data Quality RequirementsArticle 10(3) requires that training, validation, and testing data sets are subject to data governance practices that ensure relevance,…
- DG-002Bias Detection and CheckingProviders must examine training, validation, and testing datasets for possible biases that could affect health, safety, or fundamental rights, and…
- DG-003Data Documentation and ProvenanceProviders must document the origin, collection methodology, labelling process, and relevant characteristics of all data sets used in training, validation,…
- DG-004Data Representativeness AssessmentArticle 10(3) requires that training data sets are sufficiently representative of the intended population and use-case context, and providers must…
- DG-005Data Relevance and Completeness CheckProviders must verify that data sets used for high-risk AI systems are relevant and complete for the system's intended purpose, documenting any known gaps…
- DG-006Data Annotation Quality AssuranceWhere data labelling or annotation is performed, providers must implement quality assurance procedures to ensure consistency, accuracy, and…
Related EU AI Act terms
- Logging CapabilitiesThe automatic recording of events by a high-risk AI system during its operation — required under Article 12 to enable monitoring of its operation, post-hoc investigation of incidents, and to support the post-market monitoring obligations of providers and deployers.
- 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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