Control A.7.6 – Data Preparation

In todays article by Kimova AI, we explore Annex A Control A.7.6 – Data Preparation, a vital control in ISO/IEC 42001 that focuses on how data is processed, structured, and refined before it is used in AI systems. From an ISMS auditor’s perspective, data preparation is one of the most influential stages in determining the reliability, fairness, and safety of AI outcomes.

What This Control Means

Control A.7.6 requires organizations to define, implement, and document processes for preparing data used in AI systems. This includes transforming raw data into a form suitable for training, testing, validation, and operational use while preserving integrity and minimizing risk.

Data preparation is where many AI risks—such as bias, data leakage, and loss of context—are either introduced or mitigated.

Why Data Preparation Matters in AI Governance

Poorly prepared data can undermine even the most advanced AI models. From an ISO 42001 standpoint, weak data preparation can result in:

biased or misleading AI outputs
inconsistent model behavior
reduced explainability and traceability
compliance failures during audits
difficulty reproducing or validating AI results

ISO 42001 emphasizes structured data preparation to ensure AI systems remain trustworthy, controlled, and auditable.

Key Expectations Under Control A.7.6

To comply with this control, organizations should demonstrate that:

Data Preparation Methods Are Defined

Processes for cleaning, normalization, labeling, anonymization, and feature engineering are formally established.

Risks Are Actively Managed

Data preparation steps address risks such as bias amplification, loss of representativeness, and unintended disclosure of sensitive data.

Separation of Data Sets Is Maintained

Training, testing, and validation datasets are clearly segregated to prevent data leakage and overfitting.

Changes Are Documented and Traceable

Any modifications to datasets during preparation are recorded and linked to dataset versions.

Human Oversight Is Applied

Critical data preparation activities—especially labeling and filtering—are reviewed and validated by competent personnel.

Implementation Guidance

Organizations can effectively implement Control A.7.6 by:

Creating standard operating procedures (SOPs) for data preparation
Applying automated and manual quality checks during preprocessing
Using version control and audit logs for prepared datasets
Documenting assumptions, exclusions, and transformations
Aligning data preparation with AI impact assessments and risk treatment plans

At Kimova AI, we see strong data preparation controls as essential for reducing downstream AI risks and ensuring compliance with ISO/IEC 42001 requirements.

Conclusion

Annex A Control A.7.6 reinforces a critical truth in AI governance: the quality, fairness, and reliability of AI systems depend heavily on how data is prepared.

By establishing controlled, transparent, and well-documented data preparation practices, organizations can significantly strengthen their AI management system and audit readiness.

In tomorrow’s article by Kimova.AI, we’ll explore Annex A Control A.8.1 – Information for Interested Parties of AI Systems, where we’ll explore how organizations can provide clear, accurate, and relevant information about AI systems to stakeholders, fostering transparency, trust, and informed decision-making.

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