# A.7.4 Quality of data for AI systems

Define and document data quality requirements and check that data used in building and running the AI system satisfy them. In practice the record is data quality requirements for training, validation, test and production data. For quality of data for ai systems the evidence starts with the provenance record.

- Framework: ISO/IEC 42001:2023
- Cited to: ISO/IEC 42001:2023, Annex A control A.7.4
- Source: https://www.iso.org/standard/81230.html
- Last updated: 2026-10-20

## What it asks

Define and document data quality requirements and check that data used in building and running the AI system satisfy them.

## Where generative AI changes the answer

Quality checks have to cover duplication, staleness and the presence of personal data, because each one changes how the model behaves.

## Who it binds

The data owner for the AI system and the team that builds or buys it.

## Evidence an auditor samples

- Data quality requirements for training, validation, test and production data
- Data quality measurement results
- Bias analysis and adjustments made for performance and fairness

## The test you can run

Ask for the provenance of one document in the retrieval corpus, and for the redaction decision on it.

## Common failures

- Quality requirements defined for training data only
- Bias impact on fairness not assessed

## Before the draft publishes

Trace one output back to the data behind it. If you cannot, provenance is the first control to build.

## What this page does not say

It does not state the text of ISO/IEC DTS 25568, because ISO has not published the draft's clause text. It does not certify anything.

Not legal advice; does not certify.