DG-010MoyenFournisseurPréventif
Politique de conservation et de suppression des données
Les fournisseurs doivent établir et documenter des durées de conservation des données d’apprentissage et opérationnelles, avec des procédures de suppression conformes aux principes de limitation de conservation du RGPD et permettant l’exercice des droits des personnes concernées.
Articles:Article 10(2)Article 12(1)
Exemples de preuves
- Data retention schedule
- Deletion procedure documentation
- Data subject rights response log
Normes
ISO 42001:2023 §8.2ISO/IEC 27701:2019
Contrôles associés
- 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…
Termes associés du règlement sur l’IA
- 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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