EU AI Act data governance requirements

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 20th, 2026 | 13 min read

Quick answer: What are EU AI Act data governance requirements?

EU AI Act data governance requirements are rules for how organizations design, collect, prepare, and document datasets used in high‑risk AI systems. They focus on data quality, bias mitigation, traceability, and documentation so that decisions affecting people's rights and safety can be explained, challenged, and audited over time.

  • Scope and risk focus: Applies most heavily to high‑risk AI (e.g., employment, credit, medical triage) with explicit expectations for training, validation, and testing data.
  • Quality, bias, and evidence: Requires relevant, representative, complete datasets with bias controls, plus record‑keeping and logging to reconstruct outcomes.

Below: understanding obligations, SOP template, worked examples, vendor + GPAI due diligence, evidence pack + monitoring.

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Understanding EU AI Act data governance obligations

For most teams, the EU AI Act’s data governance obligations become manageable once you translate them into a small set of recurring questions.

Answer these questions for every high‑risk AI system, and you’ll cover the core intent behind Article 10 (data and data governance), record‑keeping, and human oversight requirements - EU AI Act.

1. Which systems are high‑risk, and what’s the intended purpose?


Start with an AI system inventory: a list of systems, owners, and business processes that use AI.

For each system, document:

  • Intended purpose and target users
  • Impacted population(s)
  • Decision type (recommendation vs automated decision)
  • Where the decision is used (HR workflow, lending workflow, triage workflow)

If you don’t have an inventory yet, build one in your data catalog and treat “high‑risk” as a discoverable tag.

2. What datasets are used for training, validation, testing, and operation?


High‑risk AI governance fails most often because teams can’t confidently answer “which data fed this outcome?”.

At minimum, your system file should link:

  • Training dataset(s) and version
  • Validation dataset(s) and version
  • Test dataset(s) and version
  • Online / production data feeds (including feature store snapshots)

This is where end‑to‑end data lineage matters.

Lineage gives you a map from raw sources → transformations → features → model artifacts → downstream decisions.

3. How do you demonstrate data quality and representativeness?


The EU AI Act expects training/validation/testing data to be relevant, representative, as free of errors as possible, and complete for the intended purpose.

Operationally, that means you need:

  • A profiling baseline (what “normal” looks like)
  • A set of quality thresholds (what must be true before training or release)
  • Segment coverage checks (who is over/under‑represented)

Tie these checks to a clear owner (data owner or data steward) using a data ownership model.

4. How do you detect and mitigate bias across the lifecycle?


Bias mitigation is not one test.

It’s a lifecycle practice that includes:

  • Detecting bias in historical data (label bias, sampling bias)
  • Checking proxies (features correlated with protected attributes)
  • Evaluating model outcomes by group
  • Monitoring drift and disparity over time

If you already use a risk framework, map your checks to standard functions like NIST AI RMF’s “Map–Measure–Manage–Govern” cycle.

Mapping EU AI Act requirements to implementable controls

Treat the regulation as a set of control objectives you can implement in your data + ML stack.

The table below is a practical starting point.

Requirement area Where it shows up in practice Example controls you can implement Evidence to store
Dataset relevance, representativeness, completeness, error reduction Training/validation/test builds Data profiling; coverage checks; missingness thresholds; label QA sampling Profiling snapshot; quality report; sampling notes; approval record
Data governance and management practices Data sourcing + prep Datasheets; documented prep steps; change control for transformations Datasheet; pipeline docs; git hashes; change tickets
Bias detection and mitigation Feature + model evaluation Group fairness metrics; proxy checks; mitigation strategy; review gate Fairness report; mitigation plan; sign‑off decision
Traceability and record‑keeping Serving + monitoring Dataset versioning; model registry; decision logging Model card; data version IDs; log tables
Human oversight enablement Workflow integration Override workflow; explanation UI; escalation SOP Override logs; training materials; oversight runbook

Store this matrix alongside your broader data governance framework so it becomes part of your operating model instead of a one‑off compliance artifact.

SOP template: high‑risk AI data governance (copy/paste)

Use this SOP as your default for high‑risk systems, then tailor per domain.

1. Purpose and scope


Purpose. Define repeatable governance controls for datasets used to train, validate, test, and operate a high‑risk AI system under the EU AI Act.

Scope. Applies to:

  • High‑risk AI systems and their upstream data products
  • Training/validation/testing datasets and production feature feeds
  • Third‑party data providers and model/API vendors

2. Roles (minimum viable RACI)


Role Accountabilities
System owner Owns use case, signs off residual risk, chairs go/no‑go gates
Data owner Owns source datasets, approves data use, retention, and access
Data steward Maintains metadata, quality thresholds, and documentation completeness
Model lead Owns training pipeline, evaluation, and release artifacts
Model risk/validation Independently challenges data/model choices and approves release
Privacy/DPO Reviews lawful basis, DPIA needs, and data subject risk
Security Reviews access controls, logging integrity, incident response readiness

3. Required artifacts (what must exist before training)


  • System file (use case, intended purpose, risk level, owners)
  • Dataset datasheets for each training/validation/test dataset
  • Data preparation specification (steps + code references)
  • Quality and fairness test plan (metrics, thresholds, segments)
  • Logging design (fields, retention, access)

4. Data collection and sourcing procedure


  1. Source listing: list every source system and third‑party feed.
  2. Purpose check: confirm compatibility with intended AI purpose.
  3. Legal basis: record lawful basis and any restrictions.
  4. Sensitive data handling: decide whether sensitive attributes are:
    • Excluded from features
    • Allowed only for fairness testing
    • Allowed as features with explicit justification and controls

5. Data preparation and transformation controls


For each dataset version:

  • Schema validation (types, constraints)
  • Duplicate strategy (dedupe keys and logic)
  • Missingness thresholds + imputation rationale
  • Label quality checks (sampling, inter‑annotator agreement if applicable)
  • Transformation reproducibility (code version, parameters, deterministic steps)

6. Dataset versioning and reproducibility requirements


  • Assign a dataset version ID for every training/validation/test build.
  • Store joins, filters, and sampling decisions as code (or query text) linked to that version.
  • Record model build identifiers (commit hash, container digest, configuration).

7. Bias and fairness evaluation procedure


  1. Define protected groups relevant to the domain.
  2. Run baseline fairness metrics (pre‑mitigation).
  3. Perform proxy analysis (highly correlated features).
  4. Apply mitigations (reweighting, sampling, feature changes, thresholds).
  5. Re‑run metrics and document trade‑offs.

8. Approval gates (workflow + evidence)


Gate Trigger Required evidence Approvers
Gate 1: intake & classification New use case System file + initial risk assessment System owner + risk/compliance
Gate 2: data & design review New/changed datasets Datasheets + prep spec + test plan Data owner + model risk + privacy
Gate 3: pre‑deployment New model version Eval report + fairness report + logging ready System owner + model risk + security
Gate 4: periodic review Monthly/quarterly cadence Monitoring report + incidents + changes System owner + risk

9. Logging and monitoring requirements


Log events must capture:

  • event_timestamp
  • system_id / use_case
  • model_version and data_version
  • decision and score (where applicable)
  • override_flag and override_reason
  • policy_checks_passed (true/false)
  • latency_ms and error_code

10. Evidence retention and audit readiness


  • Retain the system file, dataset versions, evaluations, and decision logs per your policy and applicable law.
  • Ensure auditors can trace from a decision → model version → dataset versions → source lineage.

Worked examples: step‑by‑step governance + evidence

1) Employment screening (CV ranking and shortlist recommendation)


Scope. The model recommends which candidates should be shortlisted for interview. This is a high‑risk use case.

Key datasets.

Dataset Examples of fields Common governance risks
Applications table role applied, experience, skills, location Proxy discrimination, missingness bias
CV text education, employers, keywords Sensitive proxies, unstructured leakage
Outcomes/labels interview, hire, performance rating Historical bias in labels

Step‑by‑step governance workflow.

  1. Gate 1 (intake): Document intended purpose and define human oversight (recommendation only).
  2. Data inventory: Register datasets and owners; tag sensitive fields (PII).
  3. Preparation: Remove direct protected fields; document text redaction rules.
  4. Quality checks: Validate completeness by job family and region; sample label correctness.
  5. Fairness checks: Compare shortlist rates and false‑negative rates across groups.
  6. Mitigation: Rebalance training data; adjust thresholds; document trade‑offs.
  7. Gate 3 (pre‑deploy): Confirm logging of recommendations and recruiter overrides.
  8. Monitoring: Track disparity metrics monthly; review quarterly with HR + legal.

Evidence pack items (minimum).

  • System file + oversight design
  • Dataset datasheets (including proxy analysis notes)
  • Fairness evaluation report (pre/post mitigation)
  • Decision log: approvals at Gate 2 and Gate 3
  • Recruiter training and escalation SOP

2) Credit scoring (loan application decision support)


Scope. Model produces a score and reason codes used by credit officers. This is a high‑risk use case.

Key datasets.

Dataset Examples of fields Common governance risks
Application income, employment, assets, liabilities Misreporting, incomplete data
Transactions inflows/outflows, delinquency signals Drift, sampling bias
Bureau feed score, defaults, inquiries Third‑party lineage, contractual limits

Step‑by‑step governance workflow.

  1. Gate 1: Confirm purpose and whether any automated decisions occur.
  2. Third‑party due diligence: Document bureau feed terms and refresh cadence.
  3. Preparation: Define feature eligibility list; exclude prohibited proxies.
  4. Quality checks: Freshness SLAs; missingness thresholds; outlier handling.
  5. Fairness checks: Approval and false‑negative rates by segment; calibration checks.
  6. Gate 3: Validate reason codes and adverse action notice mapping.
  7. Monitoring: Track approval rate disparity; defaults by segment; drift in features.

Evidence pack items (minimum).

  • Data contracts / vendor docs for bureau feed
  • Feature list with rationale and exclusions
  • Model card + validation report
  • Reason code dictionary used in customer communications
  • Monitoring dashboards and threshold rules

3) Medical triage (decision support for escalation priority)


Scope. The model suggests triage level; clinicians can override. This is a high‑risk use case.

Key datasets.

Dataset Examples of fields Common governance risks
Vitals heart rate, SpO2, BP Sensor errors, missingness
Symptoms structured + free text Documentation inconsistency
Outcomes admission, ICU, discharge Label leakage, site variability

Step‑by‑step governance workflow.

  1. Gate 1: Define safety objectives (minimize under‑triage) and oversight.
  2. Data governance: De‑identify where possible; define retention and access.
  3. Preparation: Normalize coding across sites; document inclusion/exclusion criteria.
  4. Quality checks: Site‑level data quality and missingness monitoring.
  5. Clinical validation: Stratified performance by age/sex/site; retrospective study design.
  6. Gate 3: Shadow mode rollout plan and escalation procedures.
  7. Monitoring: Under‑triage and over‑triage rates; override rates; incident reviews.

Evidence pack items (minimum).

  • Clinical governance approvals / ethics review
  • Validation report stratified by key cohorts
  • Override runbook and clinician training docs
  • Incident log + root cause templates

Vendor, third‑party data, and GPAI due diligence

If you use third‑party datasets, model APIs, or a general‑purpose AI (GPAI) model in a high‑risk workflow, you still need your own evidence trail.

Use the checklist below to standardize procurement + integration.

Due diligence checklist (actionable)


Area What to ask What you need to store
Role and responsibility Are you the provider, deployer, or both? Written responsibility matrix
Training data transparency What data types and sources were used? Datasheet/model card
Limitations Where does the model fail? Known limitations + mitigations
Safety and bias testing What tests exist and for which populations? Evaluation summaries
Change management How will you be notified of updates? Change notification clause
Logging access Can you access inputs/outputs logs? Logging schema + retention
Sub‑processors Which vendors/models are used downstream? Sub‑processor list
Security Encryption, IAM, incident SLAs Security reports/certs

Contract clauses to request


  • Audit cooperation and evidence support
  • Change notification + revalidation rights
  • Access to logs and explanation artifacts
  • Sub‑processor disclosure and approval rights
  • Incident reporting timelines

Technical integration controls


  • Route vendor calls through a controlled gateway that enforces logging.
  • Minimize personal data sent out of your environment.
  • Mirror vendor outputs into your monitoring tables.

Evidence pack + monitoring blueprint

A strong EU AI Act posture depends on having an evidence pack that stays current.

Think of it as a living folder linked to live assets.

Evidence pack structure


  1. System overview (purpose, risk level, owners)
  2. Data governance (datasheets, prep specs, quality baselines)
  3. Model governance (model cards, validation, fairness)
  4. Operational controls (SOPs, access controls, change log)
  5. Monitoring & incidents (dashboards, alerts, incident reports)

Monitoring metrics (starter set)


Category Example metrics
Data quality freshness SLA breaches, missingness %, schema drift count
Model performance AUC/accuracy (as relevant), calibration error, error rate
Fairness selection rate parity, FNR/FPR disparity, override rate by group
Operations latency p95, error rate, incident count, complaint volume

Example decision log table schema


create table ai_decision_log (
  event_timestamp timestamp,
  system_id string,
  use_case string,
  model_version string,
  data_version string,
  subject_id_hash string,
  decision string,
  score double,
  override_flag boolean,
  override_reason string,
  policy_checks_passed boolean,
  latency_ms int,
  error_code string
);

Periodic review cadence


Frequency What to review Who attends
Weekly Incident triage and alerting On‑call + system owner
Monthly Fairness dashboards + drift reports Model lead + risk/compliance
Quarterly Full system review + policy updates System owner + legal + privacy + security
Annual Strategic governance roadmap Executive sponsor + all stakeholders

Takeaway: Build for auditability from day one

EU AI Act data governance is not a checkbox exercise. It’s a capability that folds into day‑to‑day work.

Start by making three things visible:

  1. System inventory — know which AI systems are high‑risk and who owns them.
  2. Dataset + model registry — link every decision to a versioned dataset and model.
  3. Decision logs — capture the trail so you can answer “why was this decision made?” months later.

From there, layer in SOPs, fairness checks, and monitoring.

The goal is not to document everything. The goal is to document the right things so that when a regulator, auditor, or affected person asks a question, you have a confident, evidence‑backed answer.

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