Data governance for model development and operation

The data governance for model development and operation requirement means you must define and run controls over AI data quality, provenance, access, and lifecycle so models are trained and operated on reliable, authorized, traceable data. Operationalize it by mapping every dataset used in training and production to owners, controls, and evidence that you can show in an ISO/IEC 42001 audit ¹.

Key takeaways:

• Build a dataset inventory tied to each model, then enforce quality, provenance, and lifecycle gates in the ML pipeline ¹.
• Assign accountable owners for data and model pipelines; auditors will ask “who approves,” “how you know,” and “where’s the proof.”
• Keep evidence that controls ran (not just policies): lineage logs, quality test results, access reviews, and change approvals.

Compliance teams get stuck on “data governance” because it can expand into an enterprise data program. ISO/IEC 42001 doesn’t require perfection; it requires you to show that your AI management system controls the data feeding model development and live operation so outcomes remain reliable and responsible ¹. That narrows the scope: focus on the datasets that can materially influence model behavior in training, validation, deployment, monitoring, and retraining.

For a CCO, GRC lead, or compliance officer, the fastest path is to define a data control baseline for AI pipelines and then prove it runs consistently. Treat each model as a bounded system: inputs, transformations, outputs, and feedback loops. Then implement four “always-on” control lanes: (1) quality controls (is the data fit for purpose), (2) provenance and lineage (where it came from and how it changed), (3) lifecycle controls (retention, versioning, deletion), and (4) access and use restrictions (who can access what, and why).

This page gives requirement-level implementation guidance you can put into a control register, test plan, and audit evidence pack for the data governance for model development and operation requirement ¹.

Regulatory text

What we can cite: “Baseline implementation-intent summary derived from publicly available framework overviews; licensed standard text is not reproduced in this record.” The requirement intent is: “Ensure data governance controls support reliable and responsible AI outcomes.” ¹

Operator interpretation: You must implement and operate data governance controls across the AI lifecycle (development and operation) so that the data used to train, evaluate, deploy, and monitor models is:

• Appropriate (fit-for-purpose quality),
• Traceable (known provenance and lineage),
• Controlled (authorized access and permitted uses),
• Managed over time (versioning, retention, deletion, and change control).

Auditors will not accept “we have a data policy” alone. They will test whether you can show dataset-level controls mapped to models and evidence that those controls ran for both training and production pipelines ¹.

Plain-English interpretation (what this requirement demands)

For every AI system you develop or operate, you need defensible answers to five questions:

1. What data did the model learn from, and what data does it see in production?
2. Where did that data come from, and what transformations occurred?
3. What checks prove the data is accurate enough and relevant enough for the intended use?
4. Who can access, change, or approve data and features, and under what rules?
5. How do you manage data changes over time without silently changing model behavior?

If you can’t answer these with records, you have a governance gap that threatens reliability and responsible outcomes ¹.

Who it applies to (entity and operational context)

Entities:

• AI developers building models in-house or through third parties.
• AI system operators deploying/hosting models, including models sourced externally, where you control data inputs and operational monitoring ¹.

Operational contexts that trigger the requirement:

• Training or fine-tuning models on internal, third-party, or customer data.
• Feature engineering pipelines (raw → curated → feature store).
• Production inference pipelines (live inputs, enrichment, post-processing).
• Human feedback loops (labeling, moderation, RLHF-style feedback).
• Retraining triggers and continuous improvement processes.

A common edge case: you operate a third-party model but supply your own data for prompts, RAG, or scoring. You still own governance of your data flows and the quality/provenance controls around them.

What you actually need to do (step-by-step)

Step 1: Define governance scope per model (build the “AI data boundary”)

Create a one-page “Data Boundary” for each model/system:

• Training datasets and versions
• Validation/test datasets
• Production input data sources
• Feedback/labeling datasets
• Derived datasets (curated tables, embeddings, features)
• Key transformations (ETL jobs, feature pipelines)

Practical tip: Start with “models in production or near-production.” Development-only prototypes can follow the same template later.

Step 2: Create a dataset inventory that maps to models

Minimum fields to track:

• Dataset name and unique identifier
• Business owner and technical owner
• Source (internal system, third party, customer-provided)
• Purpose (training, evaluation, inference enrichment, monitoring)
• Data classification and restrictions (confidential, regulated, contractual limits)
• Refresh cadence and versioning approach
• Downstream dependencies (models and pipelines)

This inventory becomes your audit index for the data governance for model development and operation requirement ¹.

Step 3: Establish data quality controls as pipeline gates

Define quality dimensions relevant to model risk and intended use:

• Completeness, validity, timeliness, consistency
• Label quality (for supervised tasks)
• Drift-sensitive fields (inputs that change over time)
• Representativeness checks where relevant to responsible outcomes

Operationalize as gates:

• Pre-ingestion checks (schema, null thresholds, duplicates)
• Pre-training checks (label audits, outlier detection)
• Pre-deployment checks (train/serve skew checks)
• Production checks (input validation, anomaly detection, drift monitoring)

Keep the tests automated where feasible and require an approval workflow for waivers.

Step 4: Implement provenance and lineage controls (traceability you can prove)

You need to show:

• Original source system or third-party provider
• Collection method and any constraints (consent, contractual, policy limits)
• Transformation steps and code versions
• Dataset versions used for each training run and deployed model version

Evidence expectations:

• Lineage tooling outputs, pipeline logs, and run manifests are stronger than narratives.
• If you lack tooling, use structured runbooks plus immutable logs in your data platform.

Step 5: Apply lifecycle controls (retention, deletion, versioning, and change control)

For each dataset class used by models, define:

• Retention period and justification (business need, legal hold, contractual constraints)
• Deletion process (including derived artifacts like features/embeddings where feasible)
• Versioning rules (how new data versions are created, labeled, and approved)
• Change control triggers (schema changes, label definition changes, source changes)

Tie lifecycle events to model governance:

• A dataset schema change should trigger a model impact assessment.
• A source vendor change should trigger a risk review.

Step 6: Restrict access and enforce “approved use”

Implement least privilege and enforce separation where appropriate:

• Training data access vs. production ops access
• Labeling workforce access boundaries
• Third-party access controls if they process or annotate data

Also control purpose limitation: prevent a dataset approved for one model/use case from quietly being reused for another without approval.

Step 7: Embed controls into MLOps (make it hard to bypass)

You want controls that run as part of normal engineering work:

• CI/CD checks for data pipeline changes
• Required fields in training job configs (dataset IDs, versions, approvals)
• Release checklist requiring quality + lineage artifacts
• Monitoring alerts tied to the same dataset identifiers in the inventory

Where Daydream fits naturally: teams often have the controls but can’t assemble audit-ready evidence quickly. Daydream can act as the system of record to map each model to its datasets, owners, controls, and the evidence trail, so audits don’t become a scramble.

Required evidence and artifacts to retain

Use this as an audit evidence checklist:

Evidence type	What auditors look for	Examples to retain
Dataset inventory	Completeness and ownership	Inventory export, ownership assignments, data classification tags
Data quality standards	Defined criteria and thresholds	Data quality policy/standard, per-dataset rule sets
Quality execution proof	Tests ran and issues were handled	Automated test logs, dashboards, incident tickets, waiver approvals
Provenance/lineage	Traceable sources and transformations	Lineage graphs, ETL job logs, code repo references, run manifests
Versioning and change control	Controlled evolution	Dataset version registry, schema change approvals, model impact assessments
Access controls	Authorized access only	IAM policies, access requests/approvals, periodic access reviews
Third-party data controls	Contractual and operational oversight	Data-sharing agreements, DPAs, third-party due diligence summaries, processing logs
Production monitoring	Data issues detected and remediated	Drift reports, anomaly alerts, remediation records

Retention should be long enough to reconstruct what data influenced a deployed model and when. Avoid hardcoding a specific timeframe unless your internal policy requires it.

Common exam/audit questions and hangups

Expect these questions in ISO/IEC 42001-aligned audits ¹:

• “Show me the datasets used for this model’s last training run. Who approved them?”
• “How do you know the production data matches what you trained on?”
• “Where is lineage recorded from source to feature to model input?”
• “What happens when a source system changes its schema or meaning?”
• “How do you prevent unauthorized reuse of datasets across different purposes?”
• “Which controls are manual, which are automated, and how do you test effectiveness?”

Hangups that cause findings:

• Controls exist but aren’t mapped to specific models/datasets.
• Evidence is scattered across tools and cannot be reproduced.
• “One-time” quality checks with no ongoing monitoring.

Frequent implementation mistakes and how to avoid them

1. Mistake: Treating governance as a policy-only project.
   Fix: build pipeline gates and retain execution logs; policy becomes the wrapper, not the control.

2. Mistake: Inventorying systems, not datasets.
   Fix: inventory datasets (and derived datasets) tied to model runs; systems alone don’t prove training inputs.

3. Mistake: Ignoring derived artifacts (features, embeddings, labels).
   Fix: treat derived datasets as first-class governed assets with owners, versioning, and retention rules.

4. Mistake: No defined waiver process.
   Fix: allow exceptions but require documented risk acceptance, time bounds, and remediation tracking.

5. Mistake: Forgetting third-party data constraints.
   Fix: encode contractual use limits into the dataset inventory and access controls; review before reuse.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement. Practically, weak data governance increases the chance of unreliable outputs, model drift, and untraceable incidents. In an audit, the risk shows up as inability to demonstrate control operation and accountability over data that materially influences model behavior ¹.

Practical 30/60/90-day execution plan

First 30 days: establish control baseline and scope

• Identify in-scope models (production and near-production).
• Create a dataset inventory template and populate it for the highest-risk models.
• Assign owners (business + technical) for each dataset and model pipeline.
• Define minimum data quality checks for training and production inputs.
• Decide where evidence will live (single evidence register vs. scattered links).

Days 31–60: implement repeatable gates and lineage proof

• Add automated data quality tests to ingestion/training pipelines for in-scope models.
• Implement dataset versioning and require dataset IDs/versions in training job configs.
• Stand up lineage capture (tooling outputs or structured logs/run manifests).
• Create a change-control workflow for schema/label definition changes with model impact assessment.

Days 61–90: operationalize monitoring, access reviews, and audit readiness

• Add production monitoring tied to the same dataset identifiers (drift/anomaly checks).
• Run an access review for sensitive datasets used in AI pipelines; remediate over-broad access.
• Conduct a tabletop “reconstruct the last model release” exercise using retained artifacts.
• Build an audit evidence pack per model: inventory excerpt, last training manifest, quality logs, lineage proof, approvals, and monitoring outputs.

Frequently Asked Questions

Does this requirement apply if we only operate a third-party model (we didn’t train it)?

Yes if you control the data inputs, enrichment, or feedback loops in operation. You still need governance over the data that influences model outputs and any retraining or tuning you perform ¹.

What’s the minimum viable dataset inventory for audit purposes?

Track dataset ID/name, owner, purpose, source, classification/restrictions, versioning approach, and which models use it. Add lineage pointers and quality checks for the datasets tied to production models first.

How do we prove provenance if our tooling is immature?

Use structured run manifests that capture dataset IDs/versions, source references, transformation job identifiers, and code commit hashes, then store them in an immutable log location. Auditors want reproducibility and traceability more than a specific tool.

Do we need data governance controls for synthetic data?

Yes. Record why synthetic data is used, how it was generated, what source data informed it (if any), and what quality checks validate it for the model’s purpose. Treat it as a dataset with provenance and lifecycle controls.

How do we handle fast-changing data sources without freezing development?

Define change tiers. Low-risk changes can auto-approve with testing; high-risk changes (schema/meaning shifts, new third-party sources) require a model impact assessment and explicit approval before training or deployment.

What evidence is most persuasive in an ISO/IEC 42001 audit?

Artifacts that show controls ran: automated test logs, lineage outputs, signed approvals, and monitoring alerts with remediation tickets. Written policies help, but auditors test operating effectiveness ¹.

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Footnotes

1. ISO/IEC 42001 overview