Data Governance — Data Governance

Data Governance — Data Governance

Zen AI Governance — Knowledge Base EU/UK alignment Updated 05 Nov 2025 www.zenaigovernance.com ↗

Data Governance — EU/UK aligned

EU AI Act Compliance Data Governance EU/UK aligned
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Key takeaways
  • Know your data: legal basis, licenses, cohorts, limitations — and prove it with lineage.
  • Bias mitigation is a process, not a one-off: measure, compare, fix and re-measure.
  • Privacy engineering (minimisation, redaction, unlearning) is first-class, not optional.

Provenance & licensing

  • Record collection source, ownership, license terms, and constraints (e.g., non-commercial).
  • Keep a chain-of-custody for each dataset; store vendor attestations for third-party data.

Lawful basis & purpose limitation

  • Justify lawful basis (consent, contract, legitimate interests, etc.); capture purpose statements.
  • Prevent scope creep: changes in use trigger impact assessment and notices where applicable.

Quality & representativeness

  • Coverage analysis; missingness; noise; duplicates; label quality; leakage checks.
  • Document exclusions and their impact on accuracy/fairness; keep gap remediation plans.

Privacy engineering

  • Minimise, pseudonymise and redact; apply DP where appropriate; stop PII in prompts/outputs.
  • Model unlearning strategy; DSR handling (access/erasure); privacy threat modelling.

Dataset lineage & versioning

  • Track dataset versions, transforms, filters, joins; link to model versions and evaluation runs.
  • Keep “as-trained” and “as-deployed” evidence snapshots for audits and incident analysis.

Bias detection & mitigation

  • Define parity metrics per cohort; test pre-training, fine-tuning and inference.
  • Mitigation techniques (re-weighing, sampling, post-processing); document trade-offs.

Security & access controls

  • RBAC/ABAC; approvals for sensitive columns; secrets management; egress controls.
  • Encrypt at rest/in transit; monitor access with anomaly detection.

Retention & deletion

  • Retention schedule per source; defensible deletion; tombstones for audit.
  • Document what cannot be deleted (e.g., derived features) and the risk rationale.

Model-generated data & feedback

  • Separate training vs feedback vs analytics pools; minimise PII; consent for re-use if needed.

Supplier/third-party data

  • Due diligence; SLA for incidents; notice obligations; termination/return-of-data clauses.

Evidence & records

  • Data cards, lineage reports, approval tickets, DPIAs/LIAs, privacy tests, and bias audit results.

Implementation checklist

  • Provenance/licensing recorded; lawful basis documented; bias plan live; lineage and retention enforced.

© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 05 Nov 2025 • This page is general guidance, not legal advice.

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