Data for development and enhancement of AI systems

ISO/IEC 42001 Annex A.7.2 requires you to actively manage all data used to develop and enhance AI systems, including sourcing, permissions, quality, provenance, security, retention, and ongoing change control. To operationalize it, put a single accountable data governance workflow around training, tuning, evaluation, and feedback data, and keep audit-ready evidence that the workflow runs. ¹

Key takeaways:

• Treat “development and enhancement data” as in-scope production risk, with owners, approvals, and traceability. ¹
• Build a controlled lifecycle: intake → classify → verify rights → assess quality/bias → secure → version → monitor → retire. ¹
• Keep artifacts that prove governance happened: dataset register, lineage, approvals, test results, and change logs. ¹

“Data for development and enhancement of AI systems” is where most AI risk becomes operational: model behavior follows the data you feed it, and regulators and auditors usually ask for evidence that your organization understood what data was used, why it was allowed, and how it was controlled. Annex A.7.2 is short, but it implies a full set of data governance mechanics for AI-specific datasets, including third-party data, user feedback, and internally generated logs. ¹

For a CCO, GRC lead, or compliance officer, the fastest path is to convert this requirement into (1) a defined scope of “AI development/enhancement data,” (2) an owned process with gates and clear decision rights, and (3) a consistent evidence package you can hand to internal audit, external assessors, or customers without scrambling. Your goal is not to write a “data policy” that no one follows; your goal is to make sure every dataset that touches model development has a documented source, documented permissions, documented quality checks, and documented change control. ¹

Regulatory text

Annex A, Control A.7.2: “The organization shall manage data used for development and enhancement of AI systems.” ¹

Operator meaning: you must put governance and controls around the data lifecycle for AI development and improvement. That includes training and fine-tuning corpora, evaluation datasets, human labeling outputs, prompt/response logs used for improvement, reinforcement learning feedback, synthetic data, and any third-party datasets used to build or update model behavior. “Manage” implies you can identify the data, control who can use it, confirm rights/permissions, measure quality, secure it, and retire it intentionally. ¹

Plain-English interpretation (what the requirement is really asking)

If you cannot answer these questions quickly and consistently, you likely do not “manage” AI development and enhancement data:

• What data did you use? (dataset identity, version, and contents at a high level)
• Where did it come from? (provenance, lineage, collection method)
• Are you allowed to use it? (license/contract terms, internal approvals)
• Was it fit for purpose? (quality checks, representativeness, known limitations)
• How did you protect it? (security controls, access, segregation)
• What changed over time? (dataset drift, refreshed data, deletion/retention actions)
• Who approved its use? (named accountable owners and sign-offs) ¹

Who it applies to (entity and operational context)

This requirement applies to any organization developing, customizing, or improving an AI system, whether you are:

• An AI provider building models or AI features for others
• An AI user fine-tuning, configuring, or continuously improving AI in your environment
• Any organization that runs AI product development, MLOps, analytics, or experimentation programs ¹

Operationally, it applies wherever AI improvement happens:

• Product/model teams curating training data and labels
• Data engineering teams building pipelines and feature stores
• Security teams governing access to sensitive datasets
• Legal/procurement managing third-party dataset contracts
• Support and operations teams feeding user feedback back into improvement loops ¹

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

1) Define the scope of “development and enhancement data”

Create a written scope statement that includes, at minimum:

• Training/fine-tuning data
• Evaluation/benchmark data
• Human labeling data and guidelines
• Prompt/response logs used to improve systems
• Telemetry and incident data used for model updates
• Synthetic data generation inputs/outputs (where used)
• Third-party datasets and externally sourced corpora ¹

Practical tip: treat “data that can change model behavior” as in-scope, even if it looks like ordinary product telemetry.

2) Establish accountable ownership and decision rights

Assign:

• Dataset owner (business accountability for appropriateness and use)
• Data steward (operational quality, metadata, lifecycle actions)
• Security owner (access model and controls)
• Approver set for high-risk datasets (privacy, legal, security, and AI governance sign-off as needed) ¹

If you already run a data governance council, add AI datasets to that forum rather than creating a parallel structure.

3) Build a dataset register that is audit-ready

Maintain a central register for each dataset used in development/enhancement, capturing:

• Name/ID, system(s) supported, environment location
• Source and collection method, including third parties
• Allowed uses and restrictions (license/contract summary)
• Data classification (sensitivity, confidentiality tier)
• Data minimization rationale (why each category is needed)
• Quality checks performed and results summary
• Known limitations and intended use boundaries
• Retention/deletion rules and operational owner
• Versioning scheme and change history ¹

This register becomes your “one answer” during audits.

4) Gate dataset intake with a repeatable workflow

Create an intake checklist and approval workflow. Minimum gates:

• Provenance and rights check: confirm origin, permissions, contractual limits, and internal authorization to use the data for AI development.
• Privacy and sensitivity review: confirm whether the dataset contains personal data or sensitive categories; document constraints and required safeguards.
• Security controls confirmation: access controls, encryption standards, segregation between dev/test/prod as applicable.
• Quality and fitness checks: completeness, duplication, labeling consistency, outliers, and mismatch with intended population.
• Bias/representation review (where relevant): document known skews and mitigations appropriate to the system’s purpose. ¹

Make the gate proportional. Low-risk public-domain data might take a lighter path; internal customer data used for training should take the strict path.

5) Implement lineage and version control for reproducibility

For every model release or significant enhancement:

• Link the model artifact to specific dataset versions.
• Store immutable references (hashes, snapshot IDs, or controlled storage versions).
• Track transformations: filtering, normalization, augmentation, labeling changes.
• Document why a dataset changed and who approved it. ¹

If an auditor asks “why did model behavior change,” this traceability is your primary evidence.

6) Secure data through the full lifecycle

Controls should match classification:

• Role-based access with least privilege for training data
• Segregated storage for restricted datasets
• Controlled export (prevent ad hoc copies into notebooks or personal storage)
• Secure deletion and retention enforcement tied to the dataset register ¹

One common failure mode is having strong production controls but weak data-science workstation and notebook controls.

7) Monitor ongoing use and “enhancement loops”

Enhancement data often arrives continuously (feedback, logs, new labels). Put controls around:

• What telemetry is collected and when it becomes training input
• Filtering/redaction rules before data enters an enhancement pipeline
• Periodic dataset review to confirm rights and appropriateness still hold
• Triggers for re-review when a dataset changes materially or a new use case appears ¹

8) Operationalize with tooling (where it helps)

A GRC system can host the controls and evidence, while data catalog and MLOps tools hold technical metadata. If you need one place to coordinate approvals, assign owners, collect artifacts, and demonstrate that gates were completed, Daydream can act as the control plane that ties dataset tickets, approvals, and evidence to each AI system and release without building custom workflows.

Required evidence and artifacts to retain

Keep artifacts that show “management” happened, not just intent:

• Dataset register entries (current and historical)
• Data intake/approval records (tickets, sign-offs, risk acceptance where needed)
• Data provenance/lineage documentation (including third-party source records)
• License/contract summaries and permissions mapping for third-party data
• Data classification results and handling requirements
• Data quality test outputs and issue remediation records
• Version history and change logs for datasets used in releases
• Access control reviews for sensitive AI datasets
• Retention and deletion execution evidence (logs or attestations) ¹

Common exam/audit questions and hangups

Expect questions like:

• “Show me the datasets used for the last model release and who approved them.”
• “How do you prevent restricted data from being used in training?”
• “How do you prove you can reproduce the model training conditions?”
• “Where do you document dataset limitations and intended use constraints?”
• “What happens when you refresh data or incorporate user feedback?” ¹

Hangup: teams often have partial evidence spread across Jira, a data catalog, a notebook, and Slack. Auditors want a cohesive story with traceable artifacts.

Frequent implementation mistakes (and how to avoid them)

• Mistake: Only governing “training data,” ignoring evaluation and feedback loops. Fix: include evaluation sets and enhancement telemetry in the dataset register and gates. ¹
• Mistake: Treating dataset approval as a one-time event. Fix: require re-approval on material change and tie dataset versions to model releases. ¹
• Mistake: Missing third-party restrictions. Fix: add a contract/rights field that is required before any dataset can be marked “approved for AI development.” ¹
• Mistake: “We have a policy” with no operating mechanism. Fix: implement an intake workflow with named owners and evidence outputs that are reviewed periodically. ¹
• Mistake: No reproducibility. Fix: enforce dataset snapshotting and lineage capture as part of release criteria. ¹

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement. Practically, weak management of AI development and enhancement data increases the likelihood of privacy violations, IP/licensing disputes, security incidents, and inability to explain or reproduce model behavior during incidents and audits. These are governance failures auditors can validate quickly because the evidence either exists or it does not. ¹

Practical 30/60/90-day execution plan

First 30 days (stabilize and inventory)

• Define “development and enhancement data” scope for your AI systems. ¹
• Stand up a dataset register template and require it for new work.
• Identify top-risk datasets already in use and assign owners.
• Implement a lightweight intake gate: provenance, rights, classification, and basic quality checks.

Next 60 days (standardize controls and evidence)

• Formalize approvals and decision rights; document who can accept risk exceptions. ¹
• Add versioning/lineage requirements to model release checklists.
• Centralize evidence collection (tickets, approvals, test outputs) so audits do not require archaeology.
• Ensure third-party dataset contracts and restrictions map cleanly to allowed AI uses.

Next 90 days (operational maturity)

• Extend controls to continuous enhancement loops (logs, feedback, new labeling). ¹
• Implement periodic access reviews for sensitive AI datasets.
• Run an internal “mock audit” on a recent model release: prove dataset lineage, approvals, and reproducibility.
• Automate where possible (catalog integration, approval workflows, evidence retention) and track exceptions to closure.

Frequently Asked Questions

Does Annex A.7.2 apply if we only fine-tune a third-party model?

Yes if you use data to change model behavior or performance, you are managing development/enhancement data in practice. Treat fine-tuning data, eval sets, and feedback used for updates as in scope. ¹

What counts as “enhancement data” besides training data?

Anything used to improve the system: prompt/response logs, user feedback, human review outcomes, incident examples, and curated eval datasets. If it informs updates, manage it through the same lifecycle controls. ¹

Do we need a separate AI data governance policy?

You need an operating process that covers AI development/enhancement datasets end-to-end. You can implement it as an addendum to existing data governance if it clearly covers AI-specific datasets, approvals, versioning, and evidence. ¹

How do we handle third-party data licensing constraints?

Record the license or contract restrictions in the dataset register, require a rights check at intake, and block unapproved uses through your workflow and access controls. Keep the contract summary and approval record attached to the dataset entry. ¹

What evidence do auditors typically want first?

Start with the dataset register, then show one recent model release with linked dataset versions, approvals, quality checks, and access controls. Auditors look for traceability and repeatability more than perfect documentation. ¹

We move fast; how do we keep this from slowing the team down?

Make the intake gate risk-based and productized: templates, required fields, and pre-approved low-risk sources. Tools like Daydream can keep approvals and evidence in one workflow so engineers are not rebuilding compliance from scratch each release.

Footnotes

1. ISO/IEC 42001:2023 Artificial intelligence — Management system