Ensure Accuracy of AI Model Training Data
Techniques verify that AI training data is reliable and accurate.
Plain language
Ensuring the data used to train AI is accurate matters because if the training data is faulty, the AI could make poor decisions leading to bad outcomes like incorrect forecasts or flawed insights that affect your business success.
Framework
ASD Information Security Manual (ISM)
Control effect
Preventative
Classifications
NC, OS, P, S, TS
ISM last updated
Dec 2025
Control Stack last updated
18 June 2026
E8 maturity levels
N/A
Guideline
Guidelines for software developmentOfficial control statement
Data validation and verification techniques are used to ensure the reliability and accuracy of training data used by AI models.
Why it matters
Inaccurate AI training data can lead to poor business decisions, costing time, money, and reputation for errors that could have been avoided.
Operational notes
Regularly verify and update training data sources to maintain AI model accuracy and usefulness.
Implementation tips
- Data managers should verify the source of the training data. They can do this by checking the data origin, ensuring it's from a trusted provider, and confirming that the data hasn't been tampered with since collection.
- The IT team should implement regular data audits. This involves setting regular intervals to check the data for errors and inconsistencies, which helps ensure the data is clean and usable.
- Business analysts should define clear data accuracy criteria. They can do this by stating what constitutes 'accurate data' for their specific needs, like checking for complete records and valid entries.
- The staff responsible for data entry should ensure the data quality. This can be done by validating the entered data against known standards at the point of entry to catch errors immediately.
- System owners should facilitate training programs for staff who handle data. These programs should cover their role in maintaining data accuracy and how to detect unreliable data early.
Audit / evidence tips
- Askdata sourcing records: Request the documentation that outlines where the training data was sourced fromLook atevidence of trusted sources and confirmation of data integrityGoodshows records of trusted, verified origins with timestamps
- Look atreports that detail regular checks on data accuracy and integrity. Verify reports include identified and corrected errorsGoodreport lists specific inaccuracies found and actions taken to correct them
- Askdata validation protocols: Obtain documentation on procedures used to ensure data accuracy. Check these protocols specify step-by-step verification processesGoodprotocol includes clear validation processes and staff responsibilities
- Look atrecords of training programs focused on data accuracy. Ensure these sessions are regular and include all relevant personnelGoodincludes training dates, attendees, and content covered
- Askerror logging and resolution records: Review records of data errors and how they were resolved. Ensure these logs detail the type of error and corrective action takenGoodrecord shows timely error detection and resolution
Cross-framework mappings
How ISM-2088 relates to controls across ISO/IEC 27001, ISO/IEC 42001, Essential Eight, and ASD ISM.
ISO 27001
| Control | Notes | Details |
|---|---|---|
handshakeSupports(3)expand_less | ||
| Annex A 5.19 | ISM-2088 requires organisations to validate and verify AI training data to ensure it is reliable and accurate for model training | |
| Annex A 5.20 | ISM-2088 requires techniques that verify AI training data is accurate and reliable prior to use | |
| Annex A 5.21 | ISM-2088 requires data validation and verification to maintain the integrity of AI training data | |
ISO 42001
| Control | Notes | Details |
|---|---|---|
sync_altPartially overlaps(1)expand_less | ||
| Annex A 6.2.7 | Annex A 6.2.7 requires the organisation to identify what AI system technical documentation is needed and provide it to relevant categorie... | |
These mappings show relationships between controls across frameworks. They do not imply full equivalence or certification.
Related ASD ISM controls in Software development
See all Guidelines for software development controls, or browse the full ASD ISM library.