Data governance for AI training and inference
Data Integrity &
Privacy
AI systems are only as trustworthy as the data behind them. Data Integrity & Privacy governance ensures that the data powering your AI systems is accurate, lawfully obtained, properly consented, and traceable — from initial collection through training, fine-tuning, and production inference.
Why It Matters
Poor data quality produces unreliable, biased, or harmful model outputs. Unlawful use of personal data creates regulatory exposure under GDPR Article 22, the EU AI Act's data governance requirements, and US state privacy laws including CCPA and the growing patchwork of AI-specific state regulations. Beyond compliance, data provenance gaps make it nearly impossible to audit model behavior, respond to data subject requests, or demonstrate due diligence when an AI system produces a harmful decision.
Regulatory Context
GDPR Article 22 restricts solely automated decision-making with legal or similarly significant effects. The EU AI Act imposes data governance requirements on training datasets for high-risk AI systems. ISO 42001 Clause 8 addresses data management as a core AI management system requirement.
Key Dimensions
The assessment evaluates Data Integrity & Privacy across four questions, each targeting a distinct governance dimension:
Policies and controls designed specifically for data used in AI workflows — not just general data management — including provenance tracking, lineage documentation, and permitted use definitions.
Systematic evaluation of privacy risks before AI systems process personal data, with documented approvals from privacy and legal stakeholders and mandatory completion prior to deployment.
Mechanisms to validate, monitor, and remediate data quality issues in AI training and inference pipelines, including automated monitoring and defined remediation workflows.
Systematic management of data subject consent and the rights to access, erasure, and portability for personal data processed in AI systems — auditable and consistently applied.
What Optimizing Looks Like
An organization scoring in the Optimizing range (76–100) on Data Integrity & Privacy demonstrates:
- →Comprehensive AI data governance with lineage and provenance tracking for all datasets used in training and inference
- →Mandatory privacy impact assessments with documented approvals before any AI system processes personal data
- →Automated data quality monitoring with configured alerts and defined remediation workflows
- →Auditable consent management and data subject rights fulfillment for all personal data used in AI contexts
Assessment Questions
The following four questions make up the Data Integrity & Privacy domain in the assessment. Each is scored 0–3, for a maximum of 12 raw points (normalized to 0–100).
How does your organization govern data used to train or operate AI systems?
Are privacy impact assessments (PIAs) conducted before AI systems process personal data?
How is data quality ensured for AI training and inference?
Does your organization manage consent and data subject rights for AI-processed personal data?
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See how this domain fits into the full assessment → Assessment Methodology