In recognising this, the ISO/IEC 42001 standard identifies Data Governance & Protection as a significant policy, ensuring that data used in AI systems is lawfully managed, secure, and forms the building block for any trustworthy AI throughout its entire data lifecycle. This content will examine why this matters in the context of AI.

Why Data Governance & Protection Matter in AI?

AI models learn and train based on the vast quantities of data available to them. As exemplified, should this data be mishandled, insecure, or biased, the output from these AI systems becomes highly misleading and potentially harmful. When organisations fail in their data control, they become prone to risks (legal, reputational, etc.), especially against strict privacy laws like GDPR.

With ISO/IEC 42001, organisations can avoid this pitfall. They are structured approaches to managing AI data responsibly, from acquisition to retirement.

Key Policy Requirements (Annex A.7, B.7)

• Data Quality & Provenance:

  All data used in AI systems should be accurate, complete, and verifiable. This includes maintaining detailed metadata on where data comes from, how it was collected, and whether it meets internal quality standards.

  • Require that every dataset be tagged with its origin and reviewed for data integrity.
  • Appoint data stewards to oversee validation and traceability across datasets.
• Privacy & Ethical Data Handling

  Personal or sensitive data used within AI systems must comply with applicable privacy regulations.

  This means:

  • Conducting PII reviews before any dataset is used.
  • Implementing anonymization or pseudonymization for sensitive attributes.
  • Tracking data consent and ensuring users have clarity on how their data is used.

  These steps are fundamental to achieving privacy-by-design, a key ethical principle in AI governance.

• Secure Data Management

  ISO 42001 mandates that all training and operational datasets be encrypted both at rest and in transit. Access should be tightly controlled and continuously monitored.

  • Use role-based access controls (RBAC) to restrict data handling to authorized personnel.
  • Establish data retention and deletion rules to minimize unnecessary storage and exposure.
• Documentation & Traceability

  Organisations must maintain comprehensive records of data sources, preparation methods, and transformations. This ensures transparency and facilitates regulatory compliance, particularly under the EU AI Act, by mandating high-risk AI systems to detailed technical documentation.

  A recommended approach is to create Training Data Statement documents that capture:

  • Data sources and acquisition processes
  • Cleaning, labelling, and preprocessing methods
  • Quality assessments and mitigation of bias or imbalance

Leveraging Emerging Tools for Data Governance

Modern AI governance requires modern tools. Platforms like Collibra, Apache Atlas, and Alation are leading the way by automating data cataloguing, lineage tracking, and quality monitoring.

• Collibra's AI Governance Suite proffers end-to-end lineage tracking, showing exactly how data flows from trusted sources into AI models.
• Apache Atlas and similar open-source frameworks embed automated data checks in their machine learning pipelines, ensuring each training job meets governance criteria before execution.

For documentation and compliance, platforms such as OneTrust can now automatically generate model cards, AI Bills of Materials (BoM), and data lineage reports, simplifying audit readiness under the EU AI Act and similar frameworks.

Regulatory Alignment: Preparing for the EU AI Act

The EU AI Act introduces one of the strictest regulations for AI globally. Articles 13-15 emphasise data documentation and transparency for high-risk AI systems. Organisations aligned with ISO/IEC 42001's Data Governance principles are well-positioned to comply. By maintaining detailed technical documentation and automated lineage tracking, they can demonstrate accountability, explainability, and regulatory conformity with minimal disruption.

Case in Point: The Cost of Poor Data Governance

Beyond compliance issues, mishandling data is a potential business risk. For instance, Paramount Pictures' AI-driven recommendation engine reportedly shared the data of subscribers without proper consent, leaving the company to face a $5 million class-action lawsuit.

This case illustrates that when data is not properly controlled, especially with AI systems, it could lead to severe consequences. Robust data governance policies, such as verifying consent before data use and maintaining strict retention limits, remain vital in preventing such incidents.

The Takeaway

The root of every AI relies on data. Without governance, they become liabilities rather than assets. The ISO/IEC 42001's Data Governance & Protection (Annex A.7, B.7) is a roadmap that ensures AI systems are trustworthy and built on secure data. Organisations that invest in these controls not only strengthen compliance but also build public trust and long-term resilience in their AI ecosystems.