AI Data Governance, Privacy, and Integrity Course

Overview

AI Data Governance, Privacy, and Integrity Course is a ten-day practitioner course for governance, privacy, risk, data stewardship, and AI ownership teams, who leave with an AI Data Governance and Privacy Control Pack. Participants map ownership, lineage, quality, purpose, access, retention, third-party data, changes, incidents, and monitoring across the data lifecycle. The course connects data controls with accountability and human review. Agile Leaders Training Center provides training in AI data governance, privacy, and integrity.

Who Should Attend

• Data governance teams responsible for policies, ownership, and stewardship
• Privacy teams responsible for approved purposes and personal-data controls
• Risk and compliance teams responsible for evidence and assurance
• AI product teams responsible for data inputs, outputs, and changes
• Data stewards responsible for quality, lineage, access, and disposition

The course assumes participants oversee business data or AI use and leaves out legal advice, cybersecurity engineering, model development, and broad enterprise AI strategy.

Departments and Industries

The course supports governed AI data use across assurance, technology, and operational functions.

• Data governance, privacy, risk, compliance, and internal audit
• Information technology, analytics, AI product, and records management
• Finance, procurement, human resources, and shared services
• Healthcare, insurance, banking, and professional services
• Manufacturing, logistics, retail, and telecommunications

Learning Objectives

By the end of this course, participants will be able to:

• Build ownership and stewardship controls for AI data
• Analyze lineage, provenance, quality, and integrity evidence
• Apply purpose, minimization, privacy, and access controls
• Evaluate retention, deletion, and third-party data decisions
• Diagnose data changes, incidents, exceptions, and control gaps
• Build an AI data governance and privacy control pack

Course Agenda

Day 1: Governance Scope and Accountability

• AI Data Lifecycle and Processing Map
• Data Owner and Steward Responsibility Matrix
• Decision Authority and Human Oversight Record
• Policy-to-Control Traceability Grid
• Governance Scope and Boundary Statement

Day 2: Inventory and Classification

• AI Data Asset Inventory Template
• Input, Reference, Output, and Feedback Classification
• Sensitive Data Identification Checklist
• Business Purpose and Approved Use Register
• System and Data Dependency Map

Day 3: Lineage and Provenance

• Source-to-System Data Lineage Diagram
• Dataset Origin and Provenance Record
• Transformation and Derivation Log
• Source Authority and Reliability Rating
• Traceability Gap Register

Day 4: Quality and Integrity

• Data Quality Dimension Scorecard
• Accuracy, Completeness, and Consistency Rules
• Integrity Check and Reconciliation Method
• Duplicate, Conflict, and Outlier Review
• Quality Exception and Remediation Record

Day 5: Purpose and Data Minimization

• Purpose Necessity and Proportionality Test
• Minimum Data Requirement Matrix
• Collection and Reuse Boundary Decision
• Data Field Reduction Exercise
• Purpose Change Approval Record

Day 6: Privacy Impact and Sensitive Data

• AI Data Privacy Impact Screening
• Individual and Group Impact Map
• Sensitive Data Handling Decision Tree
• Privacy Risk and Mitigation Register
• Human Review and Escalation Gate

Day 7: Access, Retention, and Deletion

• Role-Based Data Access Matrix
• Access Request and Approval Workflow
• Retention Rule and Trigger Schedule
• Deletion and Disposition Evidence Log
• Access and Retention Exception Review

Day 8: Third-Party and Change Control

• Third-Party Data Due Diligence Checklist
• Data Sharing and Transfer Control Record
• Dataset Version and Change Log
• Change Impact and Revalidation Checklist
• Supplier Data Responsibility Schedule

Day 9: Incidents, Monitoring, and Assurance

• Data Incident and Integrity Event Taxonomy
• Control Monitoring Indicator Set
• Exception, Waiver, and Corrective Action Register
• Governance Evidence Review Calendar
• Control Gap and Assurance Report

Day 10: AI Data Control Practice

• Suggested Exercise: Map Ownership and Data Flow
• Suggested Exercise: Test Quality and Privacy Controls
• Suggested Exercise: Review Third-Party Data Changes
• Suggested Exercise: Record Incidents and Assurance Evidence
• Capstone Exercise: AI Data Governance and Privacy Control Pack

Practical Exercises

The course uses suggested activities to convert governance requirements into reviewable data controls.

• Suggested activity: map data assets, purposes, owners, stewards, systems, and lifecycle dependencies
• Suggested activity: document lineage, provenance, transformations, quality rules, integrity checks, and exceptions
• Suggested activity: assess minimization, privacy impacts, access, retention, deletion, and third-party responsibilities
• Suggested activity: monitor changes and incidents, assemble evidence, and prioritize corrective actions

FAQs

Who suits AI data governance, privacy, and integrity training?

AI data governance, privacy, and integrity training suits governance, privacy, risk, data stewardship, assurance, and AI ownership teams. It assumes responsibility for business data or AI use, not software engineering.

How does AI data governance differ from general AI governance?

AI data governance concentrates on data ownership, purpose, lineage, quality, privacy, access, retention, third parties, and integrity evidence. General AI governance also addresses broader system, model, organizational, and use-case decisions.

Why do AI systems need data lineage and provenance?

Lineage and provenance show where data originated, how it changed, which systems handled it, and which outputs depend on it, enabling review of quality, authority, accountability, and corrective action.

How does data minimization apply to AI data governance?

Data minimization requires teams to define the purpose, identify the least data necessary, challenge collection and reuse, document exceptions, and reassess the decision when the purpose or system changes.

How should AI data integrity incidents be managed?

Integrity incidents should be classified, contained, traced to affected data and outputs, assigned to an owner, corrected with evidence, and reviewed for control changes and follow-up monitoring.

Conclusion

Participants take back an AI Data Governance and Privacy Control Pack linking assets, purposes, ownership, lineage, quality, privacy, access, retention, third parties, changes, incidents, and assurance evidence. It makes control responsibilities and decisions visible. It supports repeatable governance reviews and documented corrective action across the AI data lifecycle.