Domain 2: AI Data Governance and Privacy

Assessment of data governance practices, privacy controls, and data management throughout the AI lifecycle

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Domain Overview

AI Data Governance and Privacy focuses on the management, protection, and governance of data used throughout the AI lifecycle. This domain addresses data quality, bias mitigation, privacy controls, and data lineage tracking to ensure AI systems are built on reliable, secure, and compliant data foundations.

Effective data governance is critical for AI systems as the quality, representativeness, and security of training and operational data directly impact AI system performance, fairness, and compliance. Organizations must implement robust data governance frameworks specific to AI to address unique challenges like bias detection, data provenance tracking, and specialized privacy considerations.

Assessment Areas

2.1 AI Data Governance Framework

Evaluation of the organization's data governance framework specific to AI training and operational data, including policies, procedures, and oversight mechanisms.

Key Control: ISO 42001 Section 7.5, NIST AI RMF (MAP function)

Organizations should have a documented data governance framework that addresses the unique requirements of AI data, including data quality, privacy, and security throughout the AI lifecycle.

2.2 Bias Assessment and Mitigation

Assessment of processes to identify, measure, and mitigate bias in AI training data to ensure fair and equitable AI system outputs.

Key Control: NIST AI RMF (MEASURE function), ISO 42001 Section 8.2

Organizations should implement formal processes for bias assessment in training data, including diverse data sampling, statistical analysis, and regular bias audits.

2.3 Data Lineage and Provenance

Evaluation of systems and processes for tracking data lineage and provenance throughout the AI lifecycle.

Key Control: CIS Control 3, NIST AI RMF (MAP function)

Organizations should establish data lineage and provenance tracking systems that document the origin, transformations, and usage of all AI-related data.

2.4 AI Data Protection Controls

Assessment of data protection controls specific to AI training datasets and model outputs, including access controls, encryption, and data minimization.

Key Control: CIS Control 3, ISO 42001 Section 8.3

Organizations should implement enhanced data protection controls for AI datasets, including encryption, access controls, and data minimization techniques.

2.5 Data Retention and Disposal

Evaluation of data retention and disposal policies and procedures specific to AI training data.

Key Control: CIS Control 3, NIST CSF 2.0 (PROTECT function)

Organizations should develop and implement a data retention and disposal policy specific to AI training data that complies with relevant regulations and minimizes risk.

2.6 Data Quality Assessment

Assessment of processes for regular data quality evaluation for AI systems, including completeness, accuracy, and relevance checks.

Key Control: ISO 42001 Section 9.1, NIST AI RMF (MEASURE function)

Organizations should establish formal data quality assessment processes for AI systems, including completeness, accuracy, and relevance checks.

Compliance Considerations

Regulatory Requirements

AI data governance must comply with various data protection regulations that may apply based on jurisdiction and data types:

  • General Data Protection Regulation (GDPR)
  • California Consumer Privacy Act (CCPA)
  • Health Insurance Portability and Accountability Act (HIPAA)
  • Sector-specific regulations (financial, healthcare, etc.)
  • Emerging AI-specific regulations

Industry Standards

Several industry standards provide guidance on AI data governance:

  • ISO 42001 (AI Management System)
  • NIST AI Risk Management Framework
  • CIS Controls (especially Control 3 - Data Protection)
  • IEEE 7000 series standards for ethical considerations

Quick Assessment

Answer these key questions to quickly evaluate your AI data governance maturity:

Quick Assessment Result

Your organization appears to be at a basic level of AI data governance maturity.

Next steps: Develop a formal AI data governance framework and implement basic bias assessment processes.

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Resources

Downloads

  • Data Domain Checklist
  • Full Assessment Package
  • Executive Summary

Related Domains

AI Governance and Risk Management AI Model Development and Security AI Transparency and Explainability