Ensuring AI Training Data Integrity
Ensure AI models are trained with accurate and reliable data through validation techniques.
Plain language
This control is about making sure the data used to teach artificial intelligence (AI) models is spot-on and trustworthy. If the data is wrong or misleading, the AI could make bad decisions that might affect everything from business operations to customer safety.
Framework
ASD Information Security Manual (ISM)
Control effect
Preventative
Classifications
NC, OS, P, S, TS
ISM last updated
Nov 2025
Control Stack last updated
19 Mar 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 artificial intelligence models.
Why it matters
Training on unvalidated or incorrect data can produce unreliable outputs, flawed decisions and increased security risk from data poisoning.
Operational notes
Validate and verify training datasets (schema, range and provenance checks) to detect errors or tampering before model training and refreshes.
Implementation tips
- Data Analysts should regularly check the data sets used for AI training to ensure their accuracy. They can do this by cross-referencing the data with verified sources or conducting periodic reviews to spot anomalies or errors.
- The IT team should set up automated tools to validate incoming data. These tools can check data as it arrives for any inconsistencies or errors based on predefined rules and flag any suspicious data for further review.
- Managers should organise training sessions for employees involved in data collection. During these sessions, employees should learn about the importance of data integrity and practical techniques for ensuring data accuracy.
- Quality Assurance teams should develop and follow a clear protocol for data verification. This involves creating a checklist of criteria that data must meet, such as completeness and correctness, before it is used in AI training.
- System owners are responsible for ensuring data backup and recovery plans are in place for AI training databases. This involves setting up regular backups and testing the recovery process to ensure data remains intact and unchanged in case of data loss.
Audit / evidence tips
- AskRecords of data validation processes: Request documents detailing the methods and frequency of data checks GoodIncludes regular intervals and clear roles assigned
- AskTo see automated validation reports: Request examples of reports from data validation tools GoodResult shows clear logs of flagging and corrective actions taken
- AskTraining session records: Request attendance lists and training materials for data accuracy workshops GoodIncludes comprehensive coverage of data handling techniques
- AskThe data verification protocol: Request the checklist or framework used to judge data quality GoodShows thorough criteria that align with AI training needs
- AskTo review backup and recovery test results: Request logs or reports of backup and recovery tests GoodOutcome displays successful restoration of data without loss or corruption
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 |
|---|---|---|
| handshake Supports (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_alt Partially 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.