Apply data governance and quality criteria to training, validation and testing data

Practical action

Produce a data-provenance and bias-assessment record for each data set used to build the system.

Who does it apply to?

Providers of high-risk AI systems that involve model training.

Actors

Provider / developer

Sectors

Cross-sector / all sectors

Use cases

AI in hiring and employmentBiometrics and facial recognitionAI in healthcareAI in finance, credit and insurance

Applies from: 2 December 2027

Which controls meet this duty?

Satisfies: the control, operated properly, does the work the duty asks for. Supports: it contributes but the duty needs more. Each control page lists every other duty it serves, so work done once can be counted once.

• satisfiesProcessData governance lead · once per ai system
  Data governance and dataset documentation

  Serves 8 recorded duties · evidence: Dataset documentation sheet, Data quality and bias check report, Dataset approval for use

  Dataset documentation, representativeness and bias checks per dataset.

• supportsProcessModel development lead · at launch and on material change
  Training-data provenance and copyright register

  Serves 4 recorded duties · evidence: Training source register, Copyright and rights-reservation policy, Public summary of training content

  Origin and collection records for each source.

Templates that cover this duty

Generated from the records. Free · no account · links emailed to your work address. This duty is cited in each file with its source reference and a link back here.

• EU AI Act Role and Risk Classifier

  Checklist · XLSX · v4

• EU AI Act Article 50 Transparency Kit

  Kit · DOCX and XLSX · v32

• ISO/IEC 42001 Gap Assessment and Statement of Applicability

  Assessment · XLSX · v3

• NIST AI RMF ↔ EU AI Act ↔ ISO/IEC 42001 Crosswalk

  Crosswalk · XLSX · v3

All 7 templates that cover it →

What evidence would a reviewer expect?

Framework mappings

Original editorial crosswalks. They cite clause numbers only and reproduce no standard text; confidence reflects how direct the mapping is.

See every European Union duty mapped this way →

Frequently asked questions

Cite this record

AIPolicyTracker (2026). “Apply data governance and quality criteria to training, validation and testing data (EU AI Act)” [AIPT-OBL-0007, not yet verified]. https://aipolicytracker.org/id/AIPT-OBL-0007 (accessed 27 October 2026). Data licensed CC BY 4.0.

Permanent id AIPT-OBL-0007: it never changes or gets reused, and its address keeps working if this page moves.

Cite the official text alongside it: Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence, Official Journal of the European Union, https://eur-lex.europa.eu/eli/reg/2024/1689/oj.

@misc{aipolicytracker2026_applydatagovernanceandqu,
  author       = {{AIPolicyTracker}},
  title        = {{Apply data governance and quality criteria to training, validation and testing data (EU AI Act)}},
  year         = {2026},
  howpublished = {\url{https://aipolicytracker.org/obligations/eu-ai-act-data-governance}},
  url          = {https://aipolicytracker.org/obligations/eu-ai-act-data-governance},
  urldate      = {2026-10-27},
  note         = {Data licensed CC BY 4.0. Accessed 27 October 2026}
}

Change history

Similar obligations in other instruments

• Deployers must ensure input data they control is relevant and representative — EU AI Act, European Union
• Manage data quality, model development and monitoring across the lifecycle — Singapore Model AI Governance Framework, Singapore (voluntary)