Data Governance in the Age of AI
AI Data Governance for Risk, Privacy, and Accountability
Hours
Lectures
Content
About This Course
AI systems depend on data throughout their lifecycle, from acquisition and preparation to development, deployment, monitoring, and retirement. Data Governance in the Age of AI teaches professionals how to manage data quality, privacy, security, provenance, and accountability throughout the AI lifecycle. The course covers AI and data governance, regulatory requirements, data acquisition, third-party risks, bias, privacy engineering, generative AI, RAG, vector stores, agentic AI, human oversight, monitoring, retention, and machine unlearning. Learners gain practical knowledge to establish governance controls, protect sensitive data, document AI-related decisions, manage lifecycle risks, and support responsible AI practices as technologies and regulatory expectations continue to evolve.
What You'll Learn
- Understand data governance, AI governance, and information governance.
- Establish data ownership, accountability, metadata, lineage, and lifecycle controls.
- Assess data quality, provenance, privacy, bias, and fitness for purpose.
- Evaluate regulatory, licensing, intellectual property, and third-party data risks.
- Apply privacy and security controls across AI data environments.
- Identify risks in RAG, vector stores, generative AI, and agentic AI.
- Support transparency, human oversight, monitoring, retention, and data deletion.
- Connect governance practices with standards, metrics, auditability, and vendor assurance.
Requirements
- No prior AI or data governance experience required.
- Basic familiarity with data, technology, or compliance is helpful.
- Suitable for compliance, privacy, risk, cybersecurity, AI, and audit professionals.
- Access to a computer, tablet, or smartphone.
- Commitment to responsible data management.
This Course Includes
- 6 hours of self-paced Data Governance in the Age of AI training.
- Practical scenarios covering data governance and AI lifecycle risks.
- Downloadable governance concepts, control considerations, and documentation guidance.
- Knowledge checks to reinforce key concepts.
- Mobile and desktop access.
- Professional Certificate of Completion.
Who Is This Course For?
This course is ideal for data governance professionals, compliance officers, privacy professionals, risk managers, AI governance teams, information security professionals, legal and regulatory teams, data managers, technology leaders, internal auditors, and professionals responsible for AI-related data practices.
It is also suitable for organizations developing or deploying generative AI, machine learning, RAG applications, AI agents, and other data-driven technologies that require stronger controls for quality, privacy, security, provenance, accountability, and lifecycle management.
Certification
Compliance and Regulatory Alignment
This course covers data governance practices aligned with AI risk management, privacy, security, provenance, documentation, and accountability. It introduces regulatory considerations across federal, state, local, sectoral, and international environments, along with NIST AI RMF concepts and data governance requirements relevant to certain high-risk AI systems.
Why Compliance Training Matters
Effective data governance helps organizations protect sensitive information, maintain data quality, document AI decisions, and manage lifecycle risks. Training also helps professionals identify risks involving data acquisition, privacy, bias, security, AI systems, and third-party data while supporting stronger accountability and responsible AI practices.
Career Benefits
Data Governance skills are valuable across compliance, privacy, cybersecurity, risk, audit, technology, and AI governance roles. This course strengthens practical knowledge of AI data controls, privacy, security, documentation, and lifecycle management, helping professionals support responsible AI programs, improve governance processes, and respond to evolving regulatory expectations.
Course Curriculum
24 •6 hours
Module 1: Governing Data in AI-Driven Organizations
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Data Governance, AI Governance, and Information Governance
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Governance Objects Across the AI Lifecycle
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Operating Models, Decision Rights, and Accountability
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Inventories, Metadata, Lineage, and Lifecycle Control
Module 2: Regulatory Architecture, Rights, and Provenance
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Federal, State, Local, and Sectoral Regulatory Architecture
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Data Acquisition, Scraping, Licensing, and Intellectual Property
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Third-Party, Cross-Border, and National-Security Data Controls
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Provenance, Transparency, and Content Authenticity
Module 3: Data Fitness, Privacy, and Responsible Use
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AI Data Quality and Fitness for Purpose
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Representation, Bias, and Fairness Across the Lifecycle
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Sensitive, Derived, and Inferred Data
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Privacy Engineering and Privacy-Enhancing Technologies
Module 4: Secure Data Architecture for Generative and Agentic AI
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Secure AI Data Pipelines and Supply Chains
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Generative AI, RAG, Embeddings, and Vector Stores
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Agent Identity, Memory, Tools, and Delegated Authority
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Poisoning, Extraction, Prompt Injection, and Incident Resilience
Module 5: Deployment, Oversight, and Lifecycle Accountability
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High-Impact Decisions and Civil Rights
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Transparency, Explainability, and Human Oversight
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Monitoring, Drift, Retraining, and Change Governance
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Retention, Deletion, Machine Unlearning, and Retirement
Module 6: Assurance, Standards, and Adaptive Governance
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Framework Integration and Control Architecture
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Documentation, Auditability, and Governance Evidence
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Governance Maturity, Metrics, and Vendor Assurance
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Emerging Standards, Regulatory Change, and Adaptive Governance
Frequently Asked Questions
Data Governance in the Age of AI involves the policies, processes, roles, and controls organizations use to manage data throughout the AI lifecycle. It covers data quality, provenance, privacy, security, access, accountability, responsible use, and lifecycle management.
Data governance helps organizations ensure that AI systems use appropriate, reliable, secure, and properly managed data. Strong controls can help address risks involving poor data quality, privacy, bias, unauthorized access, provenance, and regulatory requirements.
AI data governance extends traditional data governance to address AI-specific concerns such as training-data provenance, model inputs and outputs, bias, automated decision-making, AI-generated content, RAG data, embeddings, vector stores, and agentic AI systems.
Yes. Data governance can support the identification of representation gaps, data-quality problems, inappropriate assumptions, and potential sources of bias throughout the AI lifecycle. Documentation, data lineage, quality assessments, and monitoring can also strengthen accountability for identifying and addressing data-related bias.
Yes. The course examines regulatory architecture, data acquisition, intellectual property, third-party and cross-border data controls, sensitive data, privacy engineering, provenance, and responsible data use. It also introduces governance concepts aligned with recognized AI risk-management frameworks.
Yes. After successfully completing the course, you will receive a professional Certificate of Completion recognizing your knowledge of AI data governance, data quality, privacy, provenance, security, lifecycle accountability, and responsible data-management practices.