Governance, Risk & Compliance
Data Governance, Privacy & Integrity in Artificial Intelligence (AI)
This five-day programme prepares professionals to govern the data that AI systems depend on, covering data governance foundations for AI, training data sourcing, consent and licensing, data quality, provenance and lineage, privacy and integrity controls in AI pipelines, and monitoring, assurance and governance reporting. Participants develop skills in AI data governance design, training data assessment, privacy and integrity control, and AI data assurance reporting.
Introduction
Data Governance, Privacy & Integrity in Artificial Intelligence (AI) Training Course prepares professionals to govern the data that AI systems depend on. Participants work through data governance frameworks for AI, training data sourcing, consent, and licensing, data quality, provenance, and lineage for models, privacy protection across AI pipelines, integrity controls against poisoning, leakage, and manipulation, and data governance structures, monitoring, and assurance for AI as one connected discipline. The programme links the datasets an organisation feeds into AI with the governance, privacy, and integrity discipline that keeps the outputs trustworthy and lawful. As a result, learners gain a practical command of AI data governance. Furthermore, the course strengthens governance judgement, privacy literacy, and data integrity discipline.
Data Governance, Privacy & Integrity in Artificial Intelligence (AI) Course Objectives
- Explain data governance principles and their application to AI.
- Apply data governance structures, stewardship, and ownership for AI data.
- Apply training data sourcing, licensing, and lawful basis assessment.
- Apply data quality, provenance, and lineage practice for model data.
- Apply privacy principles across AI pipelines and processing stages.
- Apply data minimisation, de-identification, and re-identification risk control.
- Apply integrity controls against data poisoning and manipulation.
- Apply protection against model leakage and training data extraction.
- Apply the NIST AI RMF and ISO/IEC 42001 to data governance.
- Apply AI data monitoring, audit, and governance reporting practice.
Course Methodology
- Interactive sessions built on AI data governance and integrity cases.
- Workshops on data lineage, privacy design, and integrity control mapping.
- Group exercises on training data assessment, leakage scenarios, and reporting.
- Hands-on practice with data registries, lineage maps, and integrity checklists.
- Simulation of an AI data governance review and an integrity incident.
- Facilitator coaching and peer review on personal AI data governance plans.
Who Should Take This Course
- Data governance and AI governance professionals.
- Data protection and privacy officers.
- Data scientists, ML engineers, and data engineers.
- Risk, compliance, and internal audit staff covering AI.
- Information security and integrity specialists.
- Consultants advising on AI data governance and privacy.
Data Governance, Privacy & Integrity in Artificial Intelligence (AI) Course Outlines
Data Governance Foundations for AI
- • Data governance concepts and their AI-specific demands.
- • The DAMA-DMBOK knowledge areas applied to AI data.
- • Data governance structures, councils, and stewardship for AI.
- • Roles: data owner, steward, model owner, and control owner.
- • AI data policy, sourcing standards, and approval gates.
- • Common AI data governance failures and warning signs.
- • Case discussion: a model trained on data nobody had the right to use.
Training Data Sourcing, Consent, and Licensing
- • Training data sources: internal, purchased, scraped, and synthetic.
- • Lawful basis, consent, and purpose limitation for AI training.
- • Licensing, copyright, and terms of use assessment.
- • Data acquisition due diligence and vendor attestations.
- • Synthetic data generation and its governance risks.
- • Data sharing agreements and third-party data arrangements.
- • Workshop: assessing a training data source against legal and ethical criteria.
Data Quality, Provenance, and Lineage
- • Data quality dimensions for AI: accuracy, completeness, balance, recency.
- • Representativeness and coverage assessment for training data.
- • Data labelling, annotation quality, and human review.
- • Data provenance, lineage, and versioning for models.
- • Data lineage mapping from source to model input.
- • Data registries, documentation, and reproducibility.
- • Exercise: building a lineage map and data registry entry.
Privacy and Integrity Controls in AI Pipelines
- • Privacy principles across collection, training, inference, and output.
- • Data minimisation and feature selection for privacy.
- • De-identification, pseudonymisation, and re-identification risk.
- • Data poisoning, backdoors, and manipulation defence.
- • Model leakage, training data extraction, and membership inference.
- • Access control, logging, and traceability for training data.
- • Simulation: responding to a data integrity or leakage incident.
Monitoring, Assurance, and Governance Reporting
- • Data governance frameworks: NIST AI RMF and ISO/IEC 42001 alignment.
- • Continuous monitoring of AI data quality and drift.
- • Data governance metrics, KPIs, and dashboards.
- • Audit of AI data handling and integrity evidence.
- • Privacy impact assessment and data protection review for AI.
- • Governance reporting to boards, committees, and regulators.
- • Capstone: presenting an AI data governance, privacy and integrity framework.
Conclusion
By successfully completing Data Governance, Privacy & Integrity in Artificial Intelligence (AI), participants will gain a practical command of data governance frameworks for AI, training data sourcing, consent and licensing, data quality, provenance and lineage, privacy and integrity controls across AI pipelines, and monitoring, assurance and governance reporting. They will acquire skills in AI data governance design, training data assessment, privacy and integrity control, and AI data assurance reporting that keep AI systems lawful, trustworthy and defensible. Gentex Training Center designs the programme to help professionals govern AI data with confidence.
FAQs for the Data Governance, Privacy & Integrity in Artificial Intelligence (AI) course
— What is the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course about?
It prepares professionals to govern the data that AI systems depend on. The course spans data governance foundations for AI, training data sourcing, consent and licensing, data quality, provenance and lineage, privacy and integrity controls in AI pipelines, and monitoring, assurance and governance reporting.
— What are the key benefits of the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course?
You gain a practical command of AI data governance that links the datasets an organisation feeds into AI with the governance, privacy, and integrity discipline that keeps the outputs trustworthy and lawful. The course improves governance structuring and training data assessment, strengthens lineage and provenance practice, sharpens privacy and integrity controls, and gives you monitoring and assurance reporting that stands up to regulators and audit.
— What skills will I gain from the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course?
You will develop four core skills: AI data governance framework and stewardship design, training data sourcing, consent and licensing assessment, data quality, provenance and lineage management for AI, and privacy and integrity control with AI data assurance reporting.
— What tools, methods, or standards are covered in the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course?
The course covers data governance concepts and their AI-specific demands, the DAMA-DMBOK knowledge areas applied to AI data, data governance structures, councils and stewardship, roles across data owner, steward, model owner and control owner, AI data policy, sourcing standards and approval gates, training data sources across internal, purchased, scraped and synthetic, lawful basis, consent and purpose limitation for AI training, licensing, copyright and terms of use assessment, data acquisition due diligence and vendor attestations, synthetic data generation and its governance risks, data sharing agreements and third-party data arrangements, data quality dimensions for AI across accuracy, completeness, balance and recency, representativeness and coverage assessment, data labelling, annotation quality and human review, data provenance, lineage and versioning for models, data lineage mapping from source to model input, data registries, documentation and reproducibility, privacy principles across collection, training, inference and output, data minimisation and feature selection for privacy, de-identification, pseudonymisation and re-identification risk, data poisoning, backdoors and manipulation defence, model leakage, training data extraction and membership inference, access control, logging and traceability for training data, NIST AI RMF and ISO/IEC 42001 alignment, continuous monitoring of AI data quality and drift, data governance metrics, KPIs and dashboards, audit of AI data handling and integrity evidence, and privacy impact assessment and data protection review for AI.
— How is the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course applied in real-world practice?
Participants apply it to real work such as assessing a training data source against legal and ethical criteria, building a lineage map and data registry entry, documenting a training dataset for reproducibility, testing resistance to poisoning and leakage, designing privacy controls for an AI pipeline, running a privacy impact assessment for AI, auditing AI data handling, and presenting a governance framework to a board or regulator. The same methods support dataset due diligence and model documentation.
Contact our team and let us help you find the right option.
Introduction
Data Governance, Privacy & Integrity in Artificial Intelligence (AI) Training Course prepares professionals to govern the data that AI systems depend on. Participants work through data governance frameworks for AI, training data sourcing, consent, and licensing, data quality, provenance, and lineage for models, privacy protection across AI pipelines, integrity controls against poisoning, leakage, and manipulation, and data governance structures, monitoring, and assurance for AI as one connected discipline. The programme links the datasets an organisation feeds into AI with the governance, privacy, and integrity discipline that keeps the outputs trustworthy and lawful. As a result, learners gain a practical command of AI data governance. Furthermore, the course strengthens governance judgement, privacy literacy, and data integrity discipline.
Data Governance, Privacy & Integrity in Artificial Intelligence (AI) Course Objectives
- Explain data governance principles and their application to AI.
- Apply data governance structures, stewardship, and ownership for AI data.
- Apply training data sourcing, licensing, and lawful basis assessment.
- Apply data quality, provenance, and lineage practice for model data.
- Apply privacy principles across AI pipelines and processing stages.
- Apply data minimisation, de-identification, and re-identification risk control.
- Apply integrity controls against data poisoning and manipulation.
- Apply protection against model leakage and training data extraction.
- Apply the NIST AI RMF and ISO/IEC 42001 to data governance.
- Apply AI data monitoring, audit, and governance reporting practice.
Course Methodology
- Interactive sessions built on AI data governance and integrity cases.
- Workshops on data lineage, privacy design, and integrity control mapping.
- Group exercises on training data assessment, leakage scenarios, and reporting.
- Hands-on practice with data registries, lineage maps, and integrity checklists.
- Simulation of an AI data governance review and an integrity incident.
- Facilitator coaching and peer review on personal AI data governance plans.
Who Should Take This Course
- Data governance and AI governance professionals.
- Data protection and privacy officers.
- Data scientists, ML engineers, and data engineers.
- Risk, compliance, and internal audit staff covering AI.
- Information security and integrity specialists.
- Consultants advising on AI data governance and privacy.
Data Governance, Privacy & Integrity in Artificial Intelligence (AI) Course Outlines
Data Governance Foundations for AI
- • Data governance concepts and their AI-specific demands.
- • The DAMA-DMBOK knowledge areas applied to AI data.
- • Data governance structures, councils, and stewardship for AI.
- • Roles: data owner, steward, model owner, and control owner.
- • AI data policy, sourcing standards, and approval gates.
- • Common AI data governance failures and warning signs.
- • Case discussion: a model trained on data nobody had the right to use.
Training Data Sourcing, Consent, and Licensing
- • Training data sources: internal, purchased, scraped, and synthetic.
- • Lawful basis, consent, and purpose limitation for AI training.
- • Licensing, copyright, and terms of use assessment.
- • Data acquisition due diligence and vendor attestations.
- • Synthetic data generation and its governance risks.
- • Data sharing agreements and third-party data arrangements.
- • Workshop: assessing a training data source against legal and ethical criteria.
Data Quality, Provenance, and Lineage
- • Data quality dimensions for AI: accuracy, completeness, balance, recency.
- • Representativeness and coverage assessment for training data.
- • Data labelling, annotation quality, and human review.
- • Data provenance, lineage, and versioning for models.
- • Data lineage mapping from source to model input.
- • Data registries, documentation, and reproducibility.
- • Exercise: building a lineage map and data registry entry.
Privacy and Integrity Controls in AI Pipelines
- • Privacy principles across collection, training, inference, and output.
- • Data minimisation and feature selection for privacy.
- • De-identification, pseudonymisation, and re-identification risk.
- • Data poisoning, backdoors, and manipulation defence.
- • Model leakage, training data extraction, and membership inference.
- • Access control, logging, and traceability for training data.
- • Simulation: responding to a data integrity or leakage incident.
Monitoring, Assurance, and Governance Reporting
- • Data governance frameworks: NIST AI RMF and ISO/IEC 42001 alignment.
- • Continuous monitoring of AI data quality and drift.
- • Data governance metrics, KPIs, and dashboards.
- • Audit of AI data handling and integrity evidence.
- • Privacy impact assessment and data protection review for AI.
- • Governance reporting to boards, committees, and regulators.
- • Capstone: presenting an AI data governance, privacy and integrity framework.
Conclusion
By successfully completing Data Governance, Privacy & Integrity in Artificial Intelligence (AI), participants will gain a practical command of data governance frameworks for AI, training data sourcing, consent and licensing, data quality, provenance and lineage, privacy and integrity controls across AI pipelines, and monitoring, assurance and governance reporting. They will acquire skills in AI data governance design, training data assessment, privacy and integrity control, and AI data assurance reporting that keep AI systems lawful, trustworthy and defensible. Gentex Training Center designs the programme to help professionals govern AI data with confidence.
FAQs for the Data Governance, Privacy & Integrity in Artificial Intelligence (AI) course
— What is the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course about?
It prepares professionals to govern the data that AI systems depend on. The course spans data governance foundations for AI, training data sourcing, consent and licensing, data quality, provenance and lineage, privacy and integrity controls in AI pipelines, and monitoring, assurance and governance reporting.
— What are the key benefits of the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course?
You gain a practical command of AI data governance that links the datasets an organisation feeds into AI with the governance, privacy, and integrity discipline that keeps the outputs trustworthy and lawful. The course improves governance structuring and training data assessment, strengthens lineage and provenance practice, sharpens privacy and integrity controls, and gives you monitoring and assurance reporting that stands up to regulators and audit.
— What skills will I gain from the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course?
You will develop four core skills: AI data governance framework and stewardship design, training data sourcing, consent and licensing assessment, data quality, provenance and lineage management for AI, and privacy and integrity control with AI data assurance reporting.
— What tools, methods, or standards are covered in the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course?
The course covers data governance concepts and their AI-specific demands, the DAMA-DMBOK knowledge areas applied to AI data, data governance structures, councils and stewardship, roles across data owner, steward, model owner and control owner, AI data policy, sourcing standards and approval gates, training data sources across internal, purchased, scraped and synthetic, lawful basis, consent and purpose limitation for AI training, licensing, copyright and terms of use assessment, data acquisition due diligence and vendor attestations, synthetic data generation and its governance risks, data sharing agreements and third-party data arrangements, data quality dimensions for AI across accuracy, completeness, balance and recency, representativeness and coverage assessment, data labelling, annotation quality and human review, data provenance, lineage and versioning for models, data lineage mapping from source to model input, data registries, documentation and reproducibility, privacy principles across collection, training, inference and output, data minimisation and feature selection for privacy, de-identification, pseudonymisation and re-identification risk, data poisoning, backdoors and manipulation defence, model leakage, training data extraction and membership inference, access control, logging and traceability for training data, NIST AI RMF and ISO/IEC 42001 alignment, continuous monitoring of AI data quality and drift, data governance metrics, KPIs and dashboards, audit of AI data handling and integrity evidence, and privacy impact assessment and data protection review for AI.
— How is the "Data Governance, Privacy & Integrity in Artificial Intelligence (AI)" course applied in real-world practice?
Participants apply it to real work such as assessing a training data source against legal and ethical criteria, building a lineage map and data registry entry, documenting a training dataset for reproducibility, testing resistance to poisoning and leakage, designing privacy controls for an AI pipeline, running a privacy impact assessment for AI, auditing AI data handling, and presenting a governance framework to a board or regulator. The same methods support dataset due diligence and model documentation.
Partners in Learning
Our Partners in Learning include leading companies and organizations that trust Gentex Training Center for high-quality training courses and professional development programs. Together, we deliver impactful training programs that support skills growth, leadership development, and organizational excellence.
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