ISO 42001 A.7.1: Data Governance for Responsible AI
Data governance is the foundation of trustworthy AI systems. Control A.7.1 requires organizations to implement governance practices that ensure data quality, transparency, and protection throughout AI pipelines—critical for organizations deploying machine learning at scale.
What this means
This control mandates establishing formal data governance practices that support responsible AI development and deployment. Organizations must address five core areas: data quality standards to ensure AI model accuracy; data lineage and provenance tracking to document data origins and transformations; access controls to restrict unauthorized use; protection of personal data in AI workflows to maintain privacy compliance; and systematic procedures to identify, document, and mitigate dataset biases that could lead to discriminatory outcomes.
How to comply
- 1.Define and document data quality standards for datasets used in AI systems, including validation rules and acceptance criteria
- 2.Implement data lineage tracking to maintain complete records of data sources, transformations, and usage across AI pipelines
- 3.Establish role-based access controls to limit AI dataset access to authorized personnel and systems
- 4.Create procedures for identifying personal data in AI training and deployment datasets, with documented safeguards for GDPR and local privacy laws
- 5.Conduct bias assessments on training datasets and document mitigation strategies for identified demographic or protected-class biases
- 6.Assign clear data ownership and governance roles with accountability for data governance policy compliance
- 7.Perform regular audits of AI datasets to ensure ongoing compliance with data governance policies
Evidence auditors look for
- Data governance policy document covering AI data requirements
- Data quality standards and acceptance criteria for AI datasets
- Data lineage diagrams or metadata records showing data flow through AI systems
- Access control logs demonstrating restricted access to AI training data
- Privacy impact assessments for AI systems using personal data
- Bias assessment reports with documented mitigation actions
- Dataset inventory identifying personal data and sensitivity levels
- Audit records of data governance compliance reviews
Frequently asked questions
When will FAQs be available?
The FAQ for this control is currently being prepared.
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