DG-007HighProviderPreventive
Privacy-Preserving Data Techniques
When processing personal data for AI training, providers must implement appropriate privacy-preserving techniques such as pseudonymisation, anonymisation, or differential privacy, in compliance with GDPR Article 25.
Articles:Article 10(5)Article 10(6)
Evidence Examples
- Privacy-by-design assessment
- Anonymisation technique description
- DPIA for training data processing
Standards
ISO 42001:2023 §8.2ISO/IEC 27701:2019
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
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