Academy learning group 2 · Academy module (not an ISO clause) M04 · Chapter 4 of 8
Govern data provenance quality and handling
About 6 minutes · ISO/IEC 42001:2023
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Picture the topic
Remember this: Suitable data depends on the intended use and the people it represents.
Explore the anchors
Establish permitted sources purpose and provenance.
Concept matching activity
Match this task or description to its concept:
Check transformations labels and relevant quality.
Choose a concept before checking the explanation.
A quick way to study this topic
- Hide the explanation and redraw these three anchors from memory.
- Explain the difference between the anchors in your own words.
- Apply the distinction to the worked example below. Check the full lesson for assumptions and exceptions.
- Recall it again tomorrow, then after a few days. A memory hint is a retrieval cue, not a replacement for understanding.
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What this chapter explains
- Concepts and distinctions
- Apply the process and collect evidence
- Evidence acquisition, test plans and sampling for AI
Concepts and distinctions
AI data governance connects source legitimacy, quality, handling and intended use. A large dataset is not automatically representative, accurate or appropriate. Understand how records were collected, labeled, transformed and selected, including which populations or conditions may be underrepresented. Data quality should be assessed against the task and consequences rather than a universal cleanliness score.
Privacy, confidentiality, retention and access duties remain relevant throughout the lifecycle. Copying operational records into training can create a new use that needs assessment. Removing direct identifiers does not automatically resolve identifiability or permission questions. Dataset versions and transformation records help support reproducibility, investigation and accountable change.
Apply the process and collect evidence
Define ownership, acceptance checks and approved uses for each important dataset. Review material source or transformation changes and retain evidence proportionate to the activity. Test relevant data slices and document limitations. Apply controlled retention and disposal while accounting for dependencies, backups and other requirements; do not retain everything forever merely because retraining may be convenient.
Evidence acquisition, test plans and sampling for AI
A test plan identifies the criterion, population, selection method, procedure, expected result, evidence source and limitations. To test training-data change approval, define the relevant releases, select changes from more than one period, compare approvals with version history and inspect who could bypass the process. A model’s high test accuracy is not evidence that approval controls operated. Preserve dataset and model version references so a reviewer knows which system was assessed.
Combine document review, interviews, observation and reproducible tests. Evaluate dataset provenance, access, label quality, retention and known limitations against the organization’s requirements and applicable criteria. Audit tools can organize or analyze evidence but need access controls, validation and secure handling. Uploading confidential data to an unapproved AI tool creates a new exposure. Test simulations support an audit conclusion only within their stated assumptions; they do not establish every production outcome.
| Concept | What to remember |
|---|---|
| Acquire | Establish permitted sources purpose and provenance |
| Prepare | Check transformations labels and relevant quality |
| Maintain | Control access changes retention and disposal |
Worked example
A team trains a service model on five years of support records without checking changed products, missing user groups or whether the reuse was authorized. Assess purpose and permitted reuse, source quality, representation and time-related changes before relying on the data. Apply handling controls and record limitations. Dataset size and historical availability do not establish suitability for the new intended use.
Check your understanding
What is the best basis for evaluating whether a dataset is suitable?
Show explanation
The intended use source legitimacy relevant quality and representation. Suitability depends on purpose and evidence rather than raw dataset size.
Source and study boundary
Official standard overview. The reference identifies the topic being taught. Explanations and examples are original academy material; a topic link does not establish independent approval or sufficient exam mastery.