AI Data Governance Standard

Document Type: Standard
Standard ID: STD-AI-003
Standard Title: AI Data Governance Standard
Version: 1.0
Effective Date: 2025-08-01
Next Review Date: 2026-08-01
Review Frequency: Annually or upon regulatory change
Parent Policy: POL-AI-001 - Artificial Intelligence Policy
Owner: Chief Data Officer
Approved By: AI Governance Committee Chair
Status: Draft
Classification: Internal Use Only

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TABLE OF CONTENTS

1. Document History
2. Objective
3. Scope and Applicability
4. Control Standard
5. Supporting Procedures
6. Compliance
7. Roles and Responsibilities
8. Exceptions
9. Enforcement
10. Key Performance Indicators (KPIs)
11. Training Requirements
12. Definitions
13. Link with AI Act and ISO42001

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DOCUMENT HISTORY

Version	Date	Author	Changes	Approval Date	Approved By
0.1	2025-06-20	Emily White, Chief Data Officer	Initial draft	-	-
0.2	2025-07-05	Emily White, Chief Data Officer	Added bias mitigation details	-	-
0.3	2025-07-22	Emily White, Chief Data Officer	Incorporated DPO feedback	-	-
1.0	2025-08-01	Emily White, Chief Data Officer	Final version approved - GRC restructured	2025-07-25	Jane Doe, AI Governance Committee Chair

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OBJECTIVE

This standard defines requirements for AI training, validation, and testing datasets to ensure they meet quality, relevance, representativeness, and bias mitigation standards in compliance with EU AI Act Article 10.

Primary Goals:

• Ensure all AI datasets meet quality standards appropriate for intended purpose
• Ensure datasets are relevant, representative, and appropriate for intended purpose
• Detect and mitigate biases in datasets to prevent discriminatory outcomes
• Establish comprehensive data governance with full lineage tracking
• Ensure AI data governance complies with GDPR and privacy requirements

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SCOPE AND APPLICABILITY

2.1 Mandatory Applicability

This standard is mandatory for:

• All high-risk AI systems (EU AI Act Article 10)
• All data used for AI system development, training, validation, testing, and operation

2.2 Recommended Applicability

This standard is recommended for:

• All AI systems using training data
• Limited-risk and minimal-risk AI systems (voluntary best practices)

2.3 Data Types Covered

• Training datasets
• Validation datasets
• Testing datasets
• Input data (operational)
• Synthetic data
• Third-party datasets
• Open-source datasets

2.4 Out of Scope

• General enterprise data governance (covered by enterprise data governance framework)
• Non-AI system data (covered by other data governance standards)
• Data outside EU AI Act scope

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CONTROL STANDARD

Control DATA-001: Data Quality Requirements Definition

Control ID: DATA-001
Control Name: Data Quality Requirements Definition
Control Type: Preventive
Control Frequency: Per AI system, annually
Risk Level: High

Control Objective

Define specific data quality requirements for each AI system based on intended purpose and risk level to ensure datasets meet appropriate quality standards before use in AI training, validation, and testing, in compliance with EU AI Act Article 10(2).

Control Requirements

CR-001.1: Data Quality Requirements Documentation

Define and document data quality requirements for each AI system with specific thresholds based on risk level.

Data Quality Dimensions:

Dimension	Definition	High-Risk AI Requirement	Limited/Minimal-Risk AI Requirement
Accuracy	Correctness of data values	≥95%	≥90%
Completeness	Presence of all required data	≥98%	≥95%
Consistency	Uniformity across datasets	100% for critical fields	≥95% for critical fields
Timeliness	Data currency and freshness	Defined per use case	Defined per use case
Validity	Conformance to defined formats	100%	≥98%
Uniqueness	No unintended duplicates	≥99%	≥95%

Mandatory Actions:

• Define quality requirements per AI system
• Document quality thresholds in Data Quality Requirements Document
• Obtain AI System Owner approval
• Review and update annually
• Align with intended purpose and risk level

Evidence Required:

• Data Quality Requirements Document (DOC-AI-DATA-001)
• Quality thresholds by AI system
• Approval records
• Annual review records

Audit Verification:

• Verify quality requirements defined for all AI systems
• Confirm thresholds appropriate for risk level
• Check approval obtained
• Validate annual review completed

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Control DATA-002: Data Quality Assessment

Control ID: DATA-002
Control Name: Data Quality Assessment and Validation
Control Type: Preventive
Control Frequency: Before training, after significant data updates
Risk Level: High

Control Objective

Assess data quality against defined requirements before use in AI training to ensure datasets meet quality thresholds and prevent quality issues from affecting AI system performance and compliance.

Control Requirements

CR-002.1: Pre-Training Quality Assessment

Conduct comprehensive data quality assessment using multiple methods before datasets are used for AI training.

Assessment Methods:

• Automated data profiling
• Statistical analysis
• Manual sampling and review
• Data quality tools

Mandatory Actions:

• Profile datasets to assess all quality dimensions
• Calculate quality metrics for each dimension
• Compare against defined thresholds
• Document assessment results
• Remediate quality issues before use
• Obtain quality approval before training begins

Quality Assessment Checklist:

• Accuracy assessed (correctness of values)
• Completeness assessed (presence of required data)
• Consistency assessed (uniformity across datasets)
• Timeliness assessed (data currency)
• Validity assessed (format conformance)
• Uniqueness assessed (duplicate detection)
• All metrics meet thresholds
• Quality issues identified and remediated
• Quality approval obtained

Evidence Required:

• Data Quality Assessment Report (RPT-AI-DATA-001)
• Quality metrics dashboard
• Remediation records
• Quality approval records

Audit Verification:

• Verify quality assessment conducted before training
• Confirm all quality dimensions assessed
• Check metrics meet defined thresholds
• Validate quality issues remediated before use
• Verify quality approval obtained

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Control DATA-003: Data Quality Monitoring

Control ID: DATA-003
Control Name: Continuous Data Quality Monitoring
Control Type: Detective
Control Frequency: Continuous
Risk Level: Medium

Control Objective

Continuously monitor data quality during AI system operation to detect quality degradation, identify quality issues early, and enable timely remediation to maintain AI system performance and compliance.

Control Requirements

CR-003.1: Operational Quality Monitoring

Implement automated quality monitoring with alerting and periodic reporting.

Mandatory Actions:

• Implement automated quality checks
• Monitor quality metrics in real-time
• Alert on quality threshold breaches
• Investigate quality issues promptly
• Remediate quality issues
• Report quality trends monthly

Quality Monitoring Metrics:

Metric	Threshold	Alert Level	Frequency
Accuracy	< 95% (high-risk) / < 90% (other)	Warning	Daily
Completeness	< 98% (high-risk) / < 95% (other)	Warning	Daily
Consistency	< 100% (critical fields)	Critical	Real-time
Validity	< 100%	Critical	Real-time
Uniqueness	< 99% (high-risk) / < 95% (other)	Warning	Daily

Evidence Required:

• Quality monitoring dashboard
• Alert logs
• Remediation records
• Monthly quality reports (RPT-AI-DATA-QM-XXX)

Audit Verification:

• Verify automated quality monitoring implemented
• Confirm alerting configured for threshold breaches
• Check quality issues investigated and remediated
• Validate monthly quality reports generated

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Control DATA-004: Data Relevance Assessment

Control ID: DATA-004
Control Name: Data Relevance and Purpose Alignment Assessment
Control Type: Preventive
Control Frequency: Per AI system, annually
Risk Level: High

Control Objective

Assess and document that datasets are relevant to intended purpose and geographical/behavioral/functional setting to ensure AI systems are trained on appropriate data that reflects their actual deployment context, in compliance with EU AI Act Article 10(3) and 10(4).

Control Requirements

CR-004.1: Comprehensive Relevance Assessment

Conduct relevance assessment across all relevant dimensions.

Relevance Criteria:

Criterion	Assessment Questions	Evidence Required
Purpose Alignment	Does data reflect intended use case?	Use case to data mapping
Geographical Relevance	Is data from relevant locations?	Geographic distribution analysis
Temporal Relevance	Is data from relevant time period?	Temporal distribution analysis
Behavioral Relevance	Does data reflect actual user behaviors?	Behavioral pattern analysis
Functional Relevance	Does data cover all system functions?	Function coverage analysis

Mandatory Actions:

• Define intended purpose clearly
• Identify target population/scenarios
• Assess dataset coverage of target
• Document relevance justification
• Obtain stakeholder approval
• Review relevance annually

Evidence Required:

• Data Relevance Assessment (ASSESS-AI-DATA-001)
• Purpose-to-data mapping
• Stakeholder approval records
• Annual review records

Audit Verification:

• Verify relevance assessment conducted for all AI systems
• Confirm all relevance criteria assessed
• Check relevance justification documented
• Validate stakeholder approval obtained

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Control DATA-005: Representativeness Analysis

Control ID: DATA-005
Control Name: Dataset Representativeness Assessment
Control Type: Preventive
Control Frequency: Before training, annually
Risk Level: High

Control Objective

Ensure datasets are sufficiently representative of all persons/situations AI system will encounter to prevent bias and ensure fair treatment across all user groups and scenarios, in compliance with EU AI Act Article 10(3).

Control Requirements

CR-005.1: Comprehensive Representativeness Analysis

Analyze dataset representativeness across all relevant subpopulations and protected characteristics.

Representativeness Metrics:

Metric	Definition	Target
Demographic Parity	Equal representation across protected characteristics	Ratio 0.8-1.2
Coverage	% of target population represented	≥90%
Balance	Distribution similarity to real-world	Within 10%
Diversity	Variety of scenarios/cases included	Comprehensive

Protected Characteristics to Assess:

• Gender
• Age groups
• Ethnicity/race (where legally permissible)
• Disability status
• Geographic location
• Socioeconomic status

Mandatory Actions:

• Identify all relevant subpopulations
• Analyze dataset distribution across subpopulations
• Calculate representativeness metrics
• Compare to target population distribution
• Address underrepresentation
• Document representativeness analysis

Evidence Required:

• Representativeness Analysis Report (RPT-AI-DATA-002)
• Distribution comparisons
• Gap analysis
• Mitigation plans (if underrepresentation identified)

Audit Verification:

• Verify representativeness analysis conducted before training
• Confirm all protected characteristics assessed
• Check representativeness metrics calculated
• Validate underrepresentation addressed
• Verify mitigation plans created if needed

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Control DATA-006: Dataset Appropriateness Evaluation

Control ID: DATA-006
Control Name: Dataset Selection and Appropriateness Assessment
Control Type: Preventive
Control Frequency: Per AI system, when changing datasets
Risk Level: Medium

Control Objective

Ensure datasets are appropriate considering state of the art and available alternatives to ensure optimal dataset selection and justify dataset choices for AI system development.

Control Requirements

CR-006.1: Dataset Appropriateness Justification

Evaluate and justify dataset selection against available alternatives and state of the art.

Appropriateness Factors:

Factor	Evaluation Criteria	Documentation Required
Quality vs. Alternatives	How does quality compare to other datasets?	Quality comparison analysis
Representativeness vs. Alternatives	How does representativeness compare?	Representativeness comparison
Cost vs. Value	Is cost justified by value?	Cost-benefit analysis
Privacy Implications	What are privacy risks?	Privacy impact assessment
Bias Risks	What are bias risks compared to alternatives?	Bias risk comparison
State of the Art	Does dataset align with industry best practices?	State of the art review

Mandatory Actions:

• Research available datasets
• Compare dataset options
• Justify dataset selection
• Document why chosen dataset is appropriate
• Consider synthetic data alternatives
• Obtain approval for dataset selection

Evidence Required:

• Dataset Selection Justification (DOC-AI-DATA-002)
• Alternative analysis
• State of the art review
• Approval records

Audit Verification:

• Verify dataset appropriateness evaluated
• Confirm alternatives considered
• Check justification documented
• Validate approval obtained

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Control DATA-007: Bias Examination

Control ID: DATA-007
Control Name: Bias Detection and Assessment
Control Type: Preventive
Control Frequency: Before training, after dataset updates
Risk Level: High

Control Objective

Examine training, validation, and testing datasets for possible biases to identify discriminatory patterns before they are learned by AI models, preventing bias propagation to AI system outputs, in compliance with EU AI Act Article 10(2)(f) and (g).

Control Requirements

CR-007.1: Comprehensive Bias Assessment

Conduct bias assessment across all bias types and protected characteristics.

Bias Types to Assess:

Bias Type	Description	Detection Method
Historical Bias	Data reflects past discrimination	Compare to equitable baseline
Representation Bias	Underrepresentation of groups	Distribution analysis
Measurement Bias	Biased labels or features	Label quality review
Aggregation Bias	Inappropriate grouping	Subgroup analysis
Evaluation Bias	Biased test data	Test set representativeness

Mandatory Actions:

• Conduct bias assessment before training
• Use statistical methods to detect bias
• Use bias detection tools (e.g., IBM AI Fairness 360, Aequitas)
• Document bias findings
• Assess bias impact on fairness
• Assess all protected characteristics

Evidence Required:

• Bias Assessment Report (RPT-AI-DATA-003)
• Bias metrics by protected characteristic
• Bias detection tool outputs
• Impact assessment

Audit Verification:

• Verify bias assessment conducted before training
• Confirm all bias types assessed
• Check bias detection tools used
• Validate bias findings documented
• Verify protected characteristics assessed

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Control DATA-008: Bias Mitigation

Control ID: DATA-008
Control Name: Bias Mitigation Implementation
Control Type: Corrective
Control Frequency: After bias detection
Risk Level: High

Control Objective

Implement appropriate measures to mitigate detected biases in datasets to reduce discriminatory outcomes and improve fairness across all protected characteristics.

Control Requirements

CR-008.1: Bias Mitigation Strategy Selection and Implementation

Select and implement appropriate bias mitigation strategies based on bias type and context.

Mitigation Strategies:

Strategy	When to Use	Implementation	Effectiveness Validation
Data Rebalancing	Representation bias	Oversample underrepresented groups	Post-mitigation distribution analysis
Data Augmentation	Insufficient diversity	Generate synthetic samples	Diversity metrics
Feature Engineering	Biased features	Remove or transform biased features	Feature importance analysis
Reweighting	Imbalanced classes	Assign weights to balance influence	Weighted performance metrics
Relabeling	Label bias	Review and correct biased labels	Label quality review
Fairness Constraints	Model-level bias	Apply fairness constraints during training	Fairness metrics validation

Mandatory Actions:

• Select appropriate mitigation strategies
• Implement mitigation measures
• Validate mitigation effectiveness
• Document mitigation actions
• Monitor for residual bias
• Obtain approval for mitigation approach

Evidence Required:

• Bias Mitigation Plan (PLAN-AI-DATA-001)
• Mitigation implementation records
• Post-mitigation bias assessment
• Effectiveness validation records
• Approval records

Audit Verification:

• Verify mitigation strategies selected for all identified biases
• Confirm mitigation measures implemented
• Check mitigation effectiveness validated
• Validate mitigation actions documented
• Verify residual bias monitored

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Control DATA-009: Fairness Validation

Control ID: DATA-009
Control Name: Fairness Metrics Validation
Control Type: Detective
Control Frequency: After mitigation, before deployment
Risk Level: High

Control Objective

Validate that bias mitigation achieves fairness objectives by calculating and verifying fairness metrics meet defined thresholds, ensuring AI systems treat all groups fairly across protected characteristics.

Control Requirements

CR-009.1: Fairness Metrics Calculation and Validation

Calculate fairness metrics and validate they meet defined thresholds.

Fairness Metrics:

Metric	Definition	Target	Measurement
Demographic Parity	Equal positive prediction rate across groups	Ratio 0.8-1.2	(Positive rate group A) / (Positive rate group B)
Equal Opportunity	Equal true positive rate across groups	Ratio 0.8-1.2	(TPR group A) / (TPR group B)
Equalized Odds	Equal TPR and FPR across groups	Ratio 0.8-1.2	(TPR/FPR group A) / (TPR/FPR group B)
Predictive Parity	Equal precision across groups	Ratio 0.8-1.2	(Precision group A) / (Precision group B)
Calibration	Equal calibration across groups	Within 5%	Calibration difference between groups

Mandatory Actions:

• Calculate fairness metrics for all protected characteristics
• Compare to fairness thresholds
• Assess trade-offs (accuracy vs. fairness)
• Document fairness validation
• Obtain approval for fairness-accuracy balance
• Block deployment if fairness thresholds not met

Evidence Required:

• Fairness Validation Report (RPT-AI-DATA-004)
• Fairness metrics dashboard
• Trade-off analysis
• Approval records

Audit Verification:

• Verify fairness metrics calculated for all protected characteristics
• Confirm metrics meet defined thresholds
• Check trade-off analysis documented
• Validate approval obtained
• Verify deployment blocked if thresholds not met

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Control DATA-010: Data Lineage Documentation

Control ID: DATA-010
Control Name: Data Lineage Tracking and Documentation
Control Type: Detective
Control Frequency: Continuous updates
Risk Level: Medium

Control Objective

Document complete data lineage from source to AI model to enable traceability, support audits, facilitate troubleshooting, and demonstrate data governance compliance.

Control Requirements

CR-010.1: Complete Lineage Documentation

Document all lineage elements for every dataset used in AI systems.

Lineage Elements:

Element	Description	Required Info
Data Source	Original data origin	Source system, provider, date, contact
Collection Method	How data was collected	Method, tools, responsible party, date
Transformations	All data processing steps	Transformation type, date, person, tools
Quality Checks	Quality assessments performed	Check type, results, date, person
Versions	Dataset versions	Version number, changes, date, changelog
Usage	Which AI systems use data	AI system ID, purpose, date, status

Mandatory Actions:

• Document data lineage for all datasets
• Maintain lineage in data catalog
• Update lineage with each transformation
• Enable lineage traceability (source to model)
• Provide lineage reports on demand
• Maintain lineage for 10 years

Evidence Required:

• Data Lineage Documentation (DOC-AI-DATA-003)
• Data catalog with lineage
• Lineage diagrams
• Transformation logs
• Lineage reports

Audit Verification:

• Verify lineage documented for all datasets
• Confirm all lineage elements captured
• Check lineage updated with transformations
• Validate traceability enabled
• Verify 10-year retention

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Control DATA-011: Data Provenance Verification

Control ID: DATA-011
Control Name: Data Provenance and Legal Compliance
Control Type: Preventive
Control Frequency: Per dataset acquisition
Risk Level: High

Control Objective

Establish and verify data provenance for all AI datasets to ensure legal compliance, protect intellectual property, and enable regulatory compliance, in compliance with GDPR and data protection requirements.

Control Requirements

CR-011.1: Comprehensive Provenance Verification

Verify and document all provenance requirements for each dataset.

Provenance Requirements:

Requirement	Verification Method	Documentation Required
Source Verification	Confirm data source authenticity	Source verification records
Licensing	Document data usage rights	License agreements, terms
Consent	Verify consent for data use (if personal data)	Consent records, consent management logs
Legal Basis	Document legal basis for processing (GDPR)	Legal basis documentation
Third-Party Agreements	Maintain data sharing agreements	Data sharing agreements, contracts

Mandatory Actions:

• Verify data source for all datasets
• Document licensing terms
• Obtain and document consent (where required)
• Establish legal basis for processing
• Maintain third-party agreements
• Conduct provenance audits annually

Evidence Required:

• Data Provenance Records (REC-AI-DATA-001)
• Licenses and agreements
• Consent records
• Legal basis documentation
• Third-party agreement records
• Annual provenance audit reports

Audit Verification:

• Verify provenance verified for all datasets
• Confirm licensing terms documented
• Check consent obtained where required
• Validate legal basis established
• Verify third-party agreements maintained
• Check annual provenance audits completed

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Control DATA-012: Data Catalog Management

Control ID: DATA-012
Control Name: Data Catalog Maintenance and Discovery
Control Type: Detective
Control Frequency: Continuous
Risk Level: Medium

Control Objective

Maintain comprehensive data catalog for all AI datasets to enable data discovery, support data governance, facilitate compliance, and enable efficient data management.

Control Requirements

CR-012.1: Data Catalog Completeness and Maintenance

Maintain complete, current, and searchable data catalog for all AI datasets.

Catalog Contents:

Field	Description	Mandatory
Dataset name and ID	Unique identifier and name	YES
Description and purpose	What data is and why it exists	YES
Data schema	Structure and format	YES
Data quality metrics	Current quality scores	YES
Lineage information	Complete lineage	YES
Provenance records	Source, licensing, consent	YES
Access controls	Who can access and how	YES
Usage tracking	Which AI systems use it	YES
Retention period	How long data is kept	YES
Related AI systems	Links to AI systems	YES

Mandatory Actions:

• Create catalog entries for all datasets
• Update catalog continuously
• Enable search and discovery
• Provide metadata management
• Integrate with data governance tools
• Maintain catalog accuracy

Evidence Required:

• Data Catalog (CATALOG-AI-DATA-001)
• Catalog completeness metrics
• Usage reports
• Catalog update logs

Audit Verification:

• Verify catalog entries for all datasets
• Confirm all mandatory fields populated
• Check catalog updated continuously
• Validate search and discovery enabled
• Verify catalog accuracy

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Control DATA-013: Privacy Impact Assessment

Control ID: DATA-013
Control Name: Data Protection Impact Assessment (DPIA)
Control Type: Preventive
Control Frequency: Per high-risk AI system, after substantial modifications
Risk Level: High

Control Objective

Conduct Data Protection Impact Assessment (DPIA) for high-risk AI systems processing personal data to identify and mitigate privacy risks, ensure GDPR compliance, and protect data subject rights.

Control Requirements

CR-013.1: DPIA Execution and Documentation

Conduct comprehensive DPIA per GDPR Article 35 for all high-risk AI systems processing personal data.

DPIA Triggers:

• Systematic and extensive profiling
• Large-scale processing of special category data
• Systematic monitoring of publicly accessible areas
• High-risk AI system processing personal data

DPIA Contents (per GDPR Article 35):

• Systematic description of processing operations
• Assessment of necessity and proportionality
• Assessment of risks to rights and freedoms
• Measures to address risks
• Safeguards, security measures, and mechanisms

Mandatory Actions:

• Determine if DPIA required
• Conduct DPIA per GDPR Article 35
• Identify privacy risks
• Implement privacy controls
• Obtain DPO approval
• Consult supervisory authority if high risk
• Review DPIA after substantial modifications

Evidence Required:

• Data Protection Impact Assessment (DPIA-AI-XXX)
• Privacy risk assessment
• DPO approval records
• Supervisory authority consultation records (if applicable)
• Privacy control implementation records

Audit Verification:

• Verify DPIA conducted for all high-risk AI processing personal data
• Confirm DPIA contents complete per GDPR Article 35
• Check DPO approval obtained
• Validate privacy controls implemented
• Verify supervisory authority consulted if high risk

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Control DATA-014: Data Minimization

Control ID: DATA-014
Control Name: Data Minimization and Purpose Limitation
Control Type: Preventive
Control Frequency: Per AI system, annually
Risk Level: Medium

Control Objective

Collect and process only data necessary for AI system purpose to comply with GDPR Article 5(1)(c) data minimization principle and reduce privacy risks.

Control Requirements

CR-014.1: Data Minimization Assessment and Implementation

Assess and implement data minimization for all AI systems processing personal data.

Mandatory Actions:

• Define minimum data requirements
• Justify each data element
• Remove unnecessary data
• Implement data minimization in pipelines
• Review data scope regularly (annually)
• Document minimization decisions

Data Minimization Assessment:

Data Element	Purpose	Necessity Justification	Can Be Removed?	Action
[Example]	[Purpose]	[Justification]	Yes/No	Remove/Keep

Evidence Required:

• Data Minimization Assessment (ASSESS-AI-DATA-002)
• Justification for each data element
• Data reduction records
• Annual review records

Audit Verification:

• Verify data minimization assessed for all AI systems
• Confirm each data element justified
• Check unnecessary data removed
• Validate data minimization implemented in pipelines
• Verify annual review completed

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Control DATA-015: Data Anonymization and Pseudonymization

Control ID: DATA-015
Control Name: Privacy Protection Techniques
Control Type: Preventive
Control Frequency: Before training, for personal data
Risk Level: High

Control Objective

Apply appropriate anonymization or pseudonymization techniques to protect privacy while enabling AI system development, balancing privacy protection with data utility.

Control Requirements

CR-015.1: Privacy Protection Technique Selection and Implementation

Select and implement appropriate privacy protection techniques based on use case and privacy requirements.

Techniques:

Technique	Use Case	Reversibility	Privacy Level	Data Utility
Anonymization	Remove all identifiers permanently	Irreversible	Highest	Medium
Pseudonymization	Replace identifiers with pseudonyms	Reversible with key	High	High
Aggregation	Group data to prevent identification	Irreversible	High	Low-Medium
Masking	Hide sensitive data elements	Varies	Medium-High	High
Synthetic Data	Generate artificial data	N/A	Highest	Medium-High

Mandatory Actions:

• Assess need for anonymization/pseudonymization
• Select appropriate technique
• Implement technique
• Validate effectiveness
• Document approach
• Assess re-identification risk

Re-identification Risk Assessment:

• Assess risk of re-identifying individuals
• Consider available auxiliary information
• Evaluate attack scenarios
• Document risk level
• Implement additional protections if needed

Evidence Required:

• Anonymization/Pseudonymization Plan (PLAN-AI-DATA-002)
• Implementation records
• Effectiveness validation
• Re-identification risk assessment
• Privacy protection verification

Audit Verification:

• Verify privacy protection technique selected
• Confirm technique implemented
• Check effectiveness validated
• Validate re-identification risk assessed
• Verify approach documented

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SUPPORTING PROCEDURES

This standard is implemented through the following detailed procedures:

Procedure PROC-AI-DATA-001: Data Quality Assessment Procedure

Purpose: Define step-by-step process for assessing and ensuring data quality
Owner: Chief Data Officer
Implements: Controls DATA-001, DATA-002, DATA-003

Procedure Steps:

1. Define data quality requirements - Control DATA-001
2. Profile datasets
3. Calculate quality metrics - Control DATA-002
4. Compare to thresholds
5. Document assessment results
6. Remediate quality issues
7. Obtain quality approval
8. Set up continuous monitoring - Control DATA-003

Outputs:

• Data Quality Requirements Document
• Data Quality Assessment Report
• Quality approval records
• Monitoring configuration

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Procedure PROC-AI-DATA-002: Bias Detection and Mitigation Procedure

Purpose: Define process for detecting and mitigating data bias
Owner: Chief Data Officer
Implements: Controls DATA-007, DATA-008, DATA-009

Procedure Steps:

1. Conduct bias examination - Control DATA-007
2. Document bias findings
3. Select mitigation strategies - Control DATA-008
4. Implement mitigation measures
5. Validate mitigation effectiveness
6. Calculate fairness metrics - Control DATA-009
7. Verify fairness thresholds met
8. Obtain fairness approval

Outputs:

• Bias Assessment Report
• Bias Mitigation Plan
• Fairness Validation Report
• Approval records

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Procedure PROC-AI-DATA-003: Data Lineage Documentation Procedure

Purpose: Define process for documenting data lineage
Owner: Chief Data Officer
Implements: Controls DATA-010, DATA-011, DATA-012

Procedure Steps:

1. Document data source - Control DATA-011
2. Document collection method
3. Track all transformations - Control DATA-010
4. Document quality checks
5. Maintain version history
6. Update data catalog - Control DATA-012
7. Enable lineage traceability
8. Generate lineage reports

Outputs:

• Data Lineage Documentation
• Data Catalog entries
• Lineage reports

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Procedure PROC-AI-DATA-004: Training Data Preparation Procedure

Purpose: Define process for preparing datasets for AI training
Owner: Chief Data Officer
Implements: Controls DATA-004, DATA-005, DATA-006, DATA-013, DATA-014, DATA-015

Procedure Steps:

1. Assess data relevance - Control DATA-004
2. Analyze representativeness - Control DATA-005
3. Evaluate dataset appropriateness - Control DATA-006
4. Conduct privacy impact assessment - Control DATA-013
5. Implement data minimization - Control DATA-014
6. Apply privacy protection techniques - Control DATA-015
7. Obtain all required approvals
8. Prepare dataset for training

Outputs:

• Data Relevance Assessment
• Representativeness Analysis
• Dataset Appropriateness Justification
• DPIA
• Privacy protection verification
• Training dataset ready

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COMPLIANCE

5.1 Compliance Monitoring

Monitoring Approach: Continuous automated monitoring supplemented by monthly manual reviews and quarterly comprehensive audits.

Compliance Metrics:

Metric	Target	Measurement Method	Frequency	Owner
Data Quality Requirements Coverage	100%	% of AI systems with documented quality requirements	Monthly	Chief Data Officer
Data Quality Threshold Compliance	≥95%	% of datasets meeting quality thresholds	Monthly	Chief Data Officer
Bias Assessment Coverage	100%	% of high-risk AI with bias assessment	Quarterly	Chief Data Officer
Fairness Metric Compliance	100%	% of AI systems meeting fairness thresholds	Quarterly	Chief Data Officer
Lineage Completeness	100%	% of datasets with complete lineage	Monthly	Chief Data Officer
DPIA Completion	100%	% of required DPIAs completed	Quarterly	Data Protection Officer
Data Minimization Compliance	100%	% of AI systems with minimization assessment	Quarterly	Data Protection Officer
Privacy Protection Implementation	100%	% of personal data with privacy protection	Monthly	Data Protection Officer

Monitoring Tools:

• Data Quality Dashboard
• Bias Detection Dashboard
• Data Catalog
• Privacy Compliance Dashboard
• Monthly compliance reports
• Quarterly AI Governance Committee reviews

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5.2 Internal Audit Requirements

Audit Frequency: Annually (minimum)

Audit Scope:

• Data quality requirements completeness
• Data quality assessment quality
• Bias assessment completeness and quality
• Fairness validation accuracy
• Data lineage completeness
• Data provenance verification
• Data catalog completeness
• DPIA completeness and quality
• Data minimization implementation
• Privacy protection effectiveness
• Controls effectiveness (DATA-001 through DATA-015)

Audit Activities:

• Review 100% of data quality requirements
• Sample 20% of datasets for detailed quality review
• Review 100% of bias assessments
• Test fairness metric calculations
• Verify lineage completeness
• Check provenance records
• Review data catalog completeness
• Test privacy protection effectiveness
• Interview key personnel

Audit Outputs:

• Annual Data Governance Audit Report
• Findings and recommendations
• Corrective action plans for deficiencies

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5.3 External Audit / Regulatory Inspection

Preparation:

• Maintain audit-ready data governance documentation at all times
• Designate Chief Data Officer and Data Protection Officer as regulatory liaisons
• Prepare standard response procedures for authority requests

Provide to Auditors/Regulators:

• Data Quality Requirements Documents
• Data Quality Assessment Reports
• Bias Assessment Reports
• Fairness Validation Reports
• Data Lineage Documentation
• Data Provenance Records
• Data Catalog
• DPIAs
• Data minimization assessments
• Privacy protection documentation
• Data governance procedures
• Internal audit reports
• Evidence of controls execution

Authority Request Response:

• Acknowledge request within 1 business day
• Provide requested documentation within 5 business days
• Coordinate through Legal, Chief Data Officer, and Data Protection Officer
• Document all interactions with authorities

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ROLES AND RESPONSIBILITIES

6.1 RACI Matrix

Activity	Chief Data Officer	Data Scientist	Data Engineer	Data Protection Officer	AI System Owner	AI Risk Manager	Legal
Data Quality Requirements	R/A	C	C	I	A	I	I
Data Quality Assessment	R	R	C	I	A	I	I
Data Quality Monitoring	R	C	R	I	A	I	I
Data Relevance Assessment	R	R	I	I	A	C	I
Representativeness Analysis	R	R	I	I	A	R	I
Dataset Appropriateness	R	R	I	C	A	I	C
Bias Examination	R	R	I	I	A	R	I
Bias Mitigation	R	R	C	I	A	C	I
Fairness Validation	R	R	I	I	A	R/A	I
Data Lineage Documentation	R	C	R	I	A	I	I
Data Provenance	R	I	I	R	A	I	R
Data Catalog Management	R/A	C	C	I	I	I	I
DPIA	C	I	I	R/A	A	C	C
Data Minimization	R	C	C	R	A	I	C
Privacy Protection	R	C	C	R/A	A	I	C

RACI Legend:

• R = Responsible (does the work)
• A = Accountable (ultimately answerable)
• C = Consulted (provides input)
• I = Informed (kept up-to-date)

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6.2 Role Descriptions

Chief Data Officer

• Primary Responsibility: Oversees data governance framework, ensures compliance
• Key Activities:
  • Establishes data governance framework
  • Maintains data catalog
  • Coordinates data quality management
  • Oversees bias detection and mitigation
  • Reports data governance metrics
• Required Competencies: Data governance expertise, EU AI Act Article 10, GDPR, data quality management

Data Scientist

• Primary Responsibility: Assesses data quality, bias, representativeness; prepares datasets
• Key Activities:
  • Conducts data quality assessments
  • Performs bias examinations
  • Analyzes representativeness
  • Implements bias mitigation
  • Calculates fairness metrics
• Required Competencies: Data science, statistics, bias detection, fairness metrics

Data Engineer

• Primary Responsibility: Implements data pipelines; maintains lineage
• Key Activities:
  • Implements data quality checks
  • Maintains data lineage
  • Updates data catalog
  • Implements privacy protection techniques
• Required Competencies: Data engineering, ETL, data pipeline management

Data Protection Officer (DPO)

• Primary Responsibility: Ensures GDPR compliance; conducts DPIAs
• Key Activities:
  • Conducts DPIAs
  • Verifies data provenance
  • Ensures data minimization
  • Validates privacy protection
  • Manages consent
• Required Competencies: GDPR expertise, privacy impact assessment, data protection

AI System Owner

• Primary Responsibility: Accountable for data governance of their AI system
• Key Activities:
  • Defines data requirements
  • Approves datasets
  • Approves data quality requirements
  • Participates in data assessments
• Required Competencies: AI system knowledge, data requirements definition

AI Risk Manager

• Primary Responsibility: Assesses bias risks; validates fairness
• Key Activities:
  • Assesses bias risks
  • Validates fairness metrics
  • Reviews representativeness
  • Integrates with risk management
• Required Competencies: Risk management, bias assessment, fairness validation

Legal

• Primary Responsibility: Advises on legal compliance; reviews data agreements
• Key Activities:
  • Reviews data licensing
  • Verifies legal basis
  • Reviews data sharing agreements
  • Advises on GDPR compliance
• Required Competencies: GDPR legal expertise, data licensing, contract law

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EXCEPTIONS

7.1 Exception Philosophy

Data governance is a critical regulatory compliance activity for high-risk AI systems. Exceptions are granted restrictively and only where compensating controls adequately mitigate risks.

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7.2 Allowed Exceptions

The following exceptions may be granted with proper justification and approval:

Exception Type	Justification Required	Maximum Duration	Approval Authority	Compensating Controls
Simplified Quality Assessment (Minimal-Risk AI)	AI system clearly minimal-risk; no significant quality concerns	Permanent	Chief Data Officer	Document simplified rationale; Annual re-confirmation
Extended Lineage Documentation Timeline	Technical constraints prevent immediate lineage documentation	30 days	Chief Data Officer	Interim documentation; Accelerated implementation plan
Deferred Bias Assessment (Non-Personal Data)	Dataset contains no personal data; no protected characteristics	Until personal data added	Chief Data Officer + AI Risk Manager	Document rationale; Re-assess if personal data added

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7.3 Prohibited Exceptions

The following exceptions cannot be granted under any circumstances:

❌ Skipping data quality assessment for high-risk AI - Mandatory per Article 10(2), no exceptions
❌ Skipping bias assessment for high-risk AI - Mandatory per Article 10(2)(f) and (g), no exceptions
❌ Using datasets below quality thresholds - Creates compliance gaps, performance risk
❌ Skipping DPIA for high-risk AI processing personal data - Mandatory per GDPR Article 35
❌ Operating without data lineage - Required for traceability and compliance

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7.4 Exception Request Process

Step 1: Submit Exception Request

• Complete Exception Request Form (FORM-AI-EXCEPTION-001)
• Include business justification
• Propose compensating controls
• Specify duration requested
• Attach risk assessment

Step 2: Risk Assessment

• Chief Data Officer assesses risk of granting exception
• Evaluates adequacy of compensating controls
• Documents residual risk

Step 3: Approval

• Route to appropriate approval authority based on exception type
• Chief Data Officer approval: Minor exceptions
• Chief Data Officer + AI Risk Manager: Significant exceptions
• AI Governance Committee: Critical exceptions

Step 4: Documentation and Monitoring

• Document exception in Exception Register
• Assign exception owner
• Set review date
• Monitor compensating controls
• Report exceptions quarterly to AI Governance Committee

Step 5: Exception Review and Closure

• Review exception at specified review date
• Assess if exception still needed
• Close exception when normal data governance completed
• Document lessons learned

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ENFORCEMENT

8.1 Non-Compliance Consequences

Violation	Severity	Consequence	Remediation Required
High-risk AI without data quality assessment	Critical	Immediate suspension until assessment completed	Complete assessment within 10 business days; Root cause analysis
High-risk AI without bias assessment	Critical	Immediate suspension until assessment completed	Complete bias assessment within 10 business days
Using datasets below quality thresholds	High	Halt training until quality issues resolved	Remediate quality issues; Re-assess; Re-approve
Missing DPIA for high-risk AI	Critical	Immediate suspension; GDPR violation	Complete DPIA within 15 business days; Implement privacy controls
Missing data lineage	Medium	Written warning; Escalation to management	Complete lineage documentation within 10 business days
Fairness thresholds not met	High	Block deployment until fairness achieved	Implement additional mitigation; Re-validate fairness
Missing data minimization assessment	Medium	Written warning	Complete assessment within 10 business days

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8.2 Escalation Procedures

Level 1: Chief Data Officer

• Minor procedural violations
• Documentation deficiencies
• Timeline delays < 5 days
• Action: Written warning, corrective action required

Level 2: Chief Data Officer + AI Risk Manager

• Repeated violations
• Missing bias assessments
• Quality threshold breaches
• Action: Formal review, corrective action plan, management notification

Level 3: AI Governance Committee

• High-risk AI without data quality/bias assessment
• Missing DPIA
• GDPR violations
• Action: Immediate AI system suspension, investigation, disciplinary action

Level 4: Executive Management + Legal + DPO

• Potential regulatory enforcement action
• Significant GDPR violations
• Reputational risk
• Action: Executive crisis management, legal strategy, regulatory engagement

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8.3 Immediate Escalation Triggers

Escalate immediately to AI Governance Committee + Legal + DPO if:

• ⚠️ High-risk AI system operating without data quality/bias assessment
• ⚠️ GDPR violation identified (missing DPIA, unauthorized processing)
• ⚠️ Data breach or privacy incident
• ⚠️ Regulatory inquiry or inspection related to data governance
• ⚠️ Significant fairness violations identified in production

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8.4 Disciplinary Actions

Individuals responsible for data governance violations may be subject to:

• Verbal or written warning
• Mandatory retraining
• Performance improvement plan
• Reassignment of responsibilities
• Suspension (with pay during investigation)
• Termination (for egregious violations, e.g., knowingly using biased data)

Factors Considered:

• Intent (knowing violation vs. honest mistake)
• Severity of violation
• Impact (actual or potential)
• Cooperation with remediation
• Prior violation history

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KEY PERFORMANCE INDICATORS (KPIs)

9.1 Data Governance KPIs

KPI ID	KPI Name	Definition	Target	Measurement Method	Frequency	Owner	Reporting To
KPI-DATA-001	Data Quality Requirements Coverage	% of AI systems with documented quality requirements	100%	(# AI systems with requirements / # total AI systems) × 100	Monthly	Chief Data Officer	AI Governance Committee
KPI-DATA-002	Data Quality Threshold Compliance	% of datasets meeting quality thresholds	≥95%	(# datasets meeting thresholds / # total datasets) × 100	Monthly	Chief Data Officer	Management
KPI-DATA-003	Bias Assessment Coverage	% of high-risk AI with bias assessment	100%	(# high-risk AI with bias assessment / # high-risk AI) × 100	Quarterly	Chief Data Officer	AI Governance Committee
KPI-DATA-004	Fairness Metric Compliance	% of AI systems meeting fairness thresholds	100%	(# AI systems meeting thresholds / # total AI systems) × 100	Quarterly	Chief Data Officer	AI Governance Committee
KPI-DATA-005	Lineage Completeness	% of datasets with complete lineage	100%	(# datasets with complete lineage / # total datasets) × 100	Monthly	Chief Data Officer	Management
KPI-DATA-006	DPIA Completion	% of required DPIAs completed	100%	(# DPIAs completed / # required DPIAs) × 100	Quarterly	Data Protection Officer	AI Governance Committee
KPI-DATA-007	Data Minimization Compliance	% of AI systems with minimization assessment	100%	(# AI systems with assessment / # total AI systems) × 100	Quarterly	Data Protection Officer	AI Governance Committee
KPI-DATA-008	Privacy Protection Coverage	% of personal data with privacy protection	100%	(# datasets with protection / # datasets with personal data) × 100	Monthly	Data Protection Officer	Management
KPI-DATA-009	Data Catalog Completeness	% of datasets in catalog with all mandatory fields	100%	(# complete catalog entries / # total datasets) × 100	Monthly	Chief Data Officer	Management
KPI-DATA-010	Quality Issue Resolution Time	Average days to resolve quality issues	< 5 days	Σ (resolution date - issue date) / # issues	Monthly	Chief Data Officer	Management

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9.2 KPI Dashboards and Reporting

Real-Time Dashboard (Chief Data Officer access)

• Current data quality scores
• Bias assessment status
• Fairness metrics
• Lineage completeness
• DPIA status
• Privacy protection coverage

Monthly Management Report

• KPI-DATA-001, 002, 005, 008, 009, 010
• Trend analysis (vs. previous month)
• Issues and risks
• Planned actions

Quarterly AI Governance Committee Report

• All KPIs
• Bias assessment completion status
• Fairness compliance status
• DPIA completion status
• Internal audit findings (if conducted)
• Exception register review

Annual Executive Report

• Full-year KPI performance
• Data governance maturity assessment
• Strategic recommendations
• Regulatory outlook

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9.3 KPI Thresholds and Alerts

KPI	Green (Good)	Yellow (Warning)	Red (Critical)	Alert Action
Data Quality Requirements Coverage	100%	95-99%	< 95%	Red: Immediate escalation to AI Governance Committee Chair
Data Quality Threshold Compliance	≥95%	90-94%	< 90%	Red: Escalate to AI Governance Committee
Bias Assessment Coverage	100%	90-99%	< 90%	Red: Halt high-risk AI deployments until assessed
Fairness Metric Compliance	100%	90-99%	< 90%	Red: Block deployments until fairness achieved
DPIA Completion	100%	90-99%	< 90%	Red: Immediate escalation to DPO + Legal

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TRAINING REQUIREMENTS

10.1 Training Program Overview

All personnel involved in AI data governance must complete role-specific training to ensure competency in data quality management, bias detection, GDPR compliance, and data governance procedures.

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10.2 Role-Based Training Requirements

Role	Training Course	Duration	Content	Frequency	Assessment Required
Chief Data Officer	Data Governance Expert Training	20 hours	EU AI Act Article 10; Data quality management; Bias detection; GDPR; Data lineage; Data catalog management	Initial + annually	Yes - Written exam (≥90%) + Practical data governance exercise
Data Scientists	Data Quality and Bias Assessment	16 hours	Data quality dimensions; Bias types; Fairness metrics; Bias mitigation; Representativeness analysis	Initial + annually	Yes - Practical bias assessment exercise
Data Engineers	Data Lineage and Pipeline Management	12 hours	Data lineage documentation; Data catalog; Privacy protection techniques; Pipeline implementation	Initial + annually	Yes - Practical lineage documentation exercise
Data Protection Officer	GDPR and Privacy for AI Data	16 hours	GDPR Article 35 (DPIA); Data minimization; Privacy protection techniques; Consent management; Legal basis	Initial + annually	Yes - Written exam (≥90%)
AI System Owners	Data Governance Overview	6 hours	Data quality requirements; Bias awareness; Data relevance; Responsibilities	At onboarding + annually	Yes - Knowledge check (≥80%)
All AI Development Staff	Data Quality Awareness	2 hours	Data quality basics; Bias awareness; Data minimization	At onboarding + annually	Yes - Knowledge check (≥80%)

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10.3 Training Content by Topic

Data Quality Management

• Data quality dimensions (accuracy, completeness, consistency, etc.)
• Quality requirements definition
• Quality assessment methods
• Quality monitoring
• Quality remediation

Bias Detection and Mitigation

• Bias types (historical, representation, measurement, etc.)
• Protected characteristics
• Bias detection methods
• Bias mitigation strategies
• Fairness metrics

Data Relevance and Representativeness

• Relevance criteria
• Representativeness metrics
• Protected characteristics
• Underrepresentation identification
• Dataset appropriateness evaluation

Data Lineage and Provenance

• Lineage documentation
• Provenance verification
• Data catalog management
• Traceability

GDPR and Privacy

• DPIA requirements
• Data minimization
• Privacy protection techniques
• Consent management
• Legal basis

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10.4 Training Delivery Methods

Initial Training:

• Instructor-led classroom or virtual training
• Includes interactive exercises and case studies
• Hands-on practice with data governance tools
• Group discussions of complex scenarios

Annual Refresher:

• E-learning modules for core content review
• Live update sessions for regulatory changes
• Case study reviews of recent data governance activities
• Knowledge assessment

On-the-Job Training:

• Mentoring for new data governance staff
• Job shadowing during data assessments
• Supervised data governance activities for first 5 AI systems

Just-in-Time Training:

• Quick reference guides and job aids
• Video tutorials on specific topics
• Help desk support from experienced data governance staff

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10.5 Training Effectiveness Measurement

Assessment Methods:

• Written exams for knowledge retention
• Practical exercises for skill application
• On-the-job observations for competency validation
• Feedback surveys for training quality

Competency Validation:

• Chief Data Officers: Must correctly assess 5 sample datasets with 100% accuracy before independent assessment
• Data Scientists: Must demonstrate understanding of bias detection and fairness metrics
• All staff: Must pass knowledge assessments with minimum required scores

Training Metrics:

Metric	Target	Frequency
Training completion rate	100%	Quarterly
Assessment pass rate (first attempt)	≥ 90%	Per training
Training effectiveness score (survey)	≥ 4.0/5.0	Per training
Time to competency (Chief Data Officers)	< 45 days	Per person

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10.6 Training Records

Records Maintained:

• Training attendance records
• Assessment scores
• Competency validations
• Refresher training completion
• Individual training transcripts

Retention: 10 years (to align with EU AI Act documentation retention)

Access: HR, Chief Data Officer, Internal Audit, Competent Authorities (upon request)

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DEFINITIONS

Term	Definition	Source
Training Data	Data used to train AI model to learn patterns	This Standard
Validation Data	Data used to tune model hyperparameters during training	This Standard
Testing Data	Data used to evaluate final model performance	This Standard
Data Quality	Degree to which data meets requirements for intended use	ISO/IEC 5259 series
Representativeness	Extent to which data reflects real-world population/scenarios	EU AI Act Article 10(3)
Bias	Systematic error or unfairness in data or model outputs	EU AI Act Article 10(2)(f) and (g)
Data Lineage	Complete history of data from origin to current state	This Standard
Special Category Data	Personal data revealing racial/ethnic origin, political opinions, religious beliefs, health, sex life, etc.	GDPR Article 9
Data Provenance	Information about the origin, ownership, and history of data	This Standard
Data Minimization	Principle of collecting and processing only data necessary for purpose	GDPR Article 5(1)(c)
Anonymization	Process of removing all identifiers from data permanently	GDPR
Pseudonymization	Process of replacing identifiers with pseudonyms (reversible with key)	GDPR Article 4(5)
Fairness Metric	Quantitative measure of fairness across protected characteristics	This Standard
Demographic Parity	Equal positive prediction rate across groups	This Standard
Equal Opportunity	Equal true positive rate across groups	This Standard
DPIA	Data Protection Impact Assessment per GDPR Article 35	GDPR Article 35
Data Catalog	Centralized repository of metadata about all datasets	This Standard

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LINK WITH AI ACT AND ISO42001

12.1 EU AI Act Regulatory Mapping

This standard implements the following EU AI Act requirements:

EU AI Act Provision	Article	Requirement Summary	Implemented By (Controls)
Data and Data Governance	Article 10	Requirements for training, validation, testing data	All controls (DATA-001 through DATA-015)
Training, Validation, Testing Data Quality	Article 10(2)	Quality requirements for datasets	DATA-001, DATA-002, DATA-003
Data Relevance	Article 10(3)	Datasets relevant and representative	DATA-004, DATA-005, DATA-006
Data Examination for Bias	Article 10(2)(f) and (g)	Examine datasets for bias and apply measures to detect, prevent and mitigate biases	DATA-007, DATA-008, DATA-009
Contextual Relevance	Article 10(4)	Datasets must account for characteristics particular to the specific geographical, contextual, behavioural or functional setting	DATA-004
Special Category Data Processing	Article 10(5)	Requirements for processing special category data	DATA-013, DATA-014, DATA-015

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12.2 GDPR Alignment

This standard aligns with GDPR requirements:

GDPR Provision	Requirement	Implementation in This Standard
Article 5(1)(c): Data Minimization	Collect only necessary data	DATA-014
Article 9: Special Category Data	Requirements for processing special category data	DATA-013, DATA-015
Article 25: Data Protection by Design	Privacy protection techniques	DATA-015
Article 35: DPIA	Data Protection Impact Assessment	DATA-013
Article 30: Records of Processing	Documentation of data processing	DATA-010, DATA-012

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12.3 ISO/IEC 42001:2023 Alignment

This standard aligns with ISO/IEC 42001:2023 as follows:

ISO 42001 Clause	Requirement	Implementation in This Standard
Clause 6.1.2: AI system impact assessment	Assess data-related impacts	DATA-004, DATA-005, DATA-007
Clause 7.5: Documented information	Maintain data documentation	DATA-010, DATA-012
Clause 8.2: AI system risk assessment	Data-related risk assessment	DATA-007, DATA-008, DATA-009
Clause 9.1: Monitoring and measurement	Monitor data quality	DATA-003

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12.4 ISO/IEC 5259 Series Alignment

This standard aligns with ISO/IEC 5259 series (Data Quality for AI) as follows:

ISO 5259 Standard	Requirement	Implementation in This Standard
ISO/IEC 5259-1: Overview	Data quality framework	DATA-001, DATA-002
ISO/IEC 5259-2: Data quality measures	Quality metrics and measurement	DATA-002, DATA-003
ISO/IEC 5259-3: Data quality management	Quality management process	DATA-001, DATA-002, DATA-003

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12.5 Relationship to Other Standards

This data governance standard integrates with other AI Act standards:

Related Standard	Integration Point	Rationale
STD-AI-002: Risk Management	Bias risk assessment (RM-006) uses data governance outputs	Data bias analysis feeds into risk assessment
STD-AI-004: Technical Documentation	Data requirements documented in technical documentation	Data governance outputs feed into Annex IV documentation
STD-AI-006: Transparency	Data limitations communicated to users	Data quality and bias limitations in transparency notices

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12.6 References and Related Documents

EU AI Act (Regulation (EU) 2024/1689):

• Article 10: Data and Data Governance
• Article 10(2): Training, Validation, Testing Data
• Article 10(3): Data Relevance
• Article 10(2)(f) and (g): Data Examination for Bias
• Article 10(4): Contextual Relevance of Datasets
• Article 10(5): Exceptional Processing of Special Category Data for Bias Correction

GDPR (Regulation (EU) 2016/679):

• Article 5(1)(c): Data Minimization
• Article 9: Special Category Data
• Article 25: Data Protection by Design
• Article 35: Data Protection Impact Assessment

ISO/IEC Standards:

• ISO/IEC 42001:2023: Information technology — Artificial intelligence — Management system
• ISO/IEC 5259 series: Information technology — Data quality for AI and machine learning
• ISO/IEC 23894:2023: Information technology — Artificial intelligence — Guidance on risk management

Internal Documents:

• POL-AI-001: Artificial Intelligence Policy (parent policy)
• STD-AI-002: AI Risk Management Standard
• PROC-AI-DATA-001 through PROC-AI-DATA-004: Data governance procedures

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APPROVAL AND AUTHORIZATION

Role	Name	Title	Signature	Date
Prepared By	Emily White	Chief Data Officer	_________________	________
Reviewed By	Michael Brown	Chief Legal Officer	_________________	________
Reviewed By	Lisa Anderson	Data Protection Officer	_________________	________
Reviewed By	Jane Doe	Chief Strategy & Risk Officer	_________________	________
Approved By	Jane Doe	AI Governance Committee Chair	_________________	________

Effective Date: 2025-08-01
Next Review Date: 2026-08-01
Review Frequency: Annually or upon regulatory change

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END OF STANDARD STD-AI-003

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This standard is a living document. Feedback and improvement suggestions should be directed to the Chief Data Officer.