Training on Data Governance, Privacy & Integrity in Artificial Intelligence (AI)
Learn how to implement governance frameworks, ensure data integrity, and comply with privacy regulations in AI adoption.
Next intake
20 Jul 2026 · Nakuru
Duration
5 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Advanced
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
Artificial Intelligence (AI) systems depend on large volumes of data, making governance, privacy, and data integrity essential for responsible and sustainable adoption. This course equips participants with the knowledge, tools, and frameworks needed to establish effective data governance structures, ensure compliance with global privacy regulations, and maintain the integrity and reliability of AI systems.
Through practical exercises, real world case studies, and industry best practices, participants will learn how to manage data responsibly, reduce governance and compliance risks, support ethical AI practices, and build stakeholder trust while enabling innovation and organizational growth.
Duration
5 Days
Who Should Attend
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Data governance officers, compliance managers, and risk officers
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AI developers, data scientists, and IT professionals
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Executives and managers responsible for AI strategy and adoption
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Policymakers, regulators, and legal professionals working with AI frameworks
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Professionals in industries handling sensitive data (finance, healthcare, telecom, government)
What you'll walk away with
By the end of this course, participants will be able to:
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Explain the importance of data governance, privacy, and integrity in AI systems.
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Understand global regulatory frameworks (GDPR, CCPA, HIPAA, AI Act, ISO standards).
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Apply governance models to manage data lifecycle in AI systems.
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Ensure data quality, transparency, and fairness in AI algorithms.
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Implement privacy-preserving technologies (differential privacy, anonymization, encryption).
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Develop ethical and compliant data governance policies for AI projects.
What we cover, module by module
Module 1: Foundations of Data Governance in AI
- Core principles and frameworks of data governance
- Understanding the data lifecycle in AI systems
- Aligning AI, data governance, and organizational strategy
- Case Study: Data governance challenges in AI driven organizations
- Practical: Map the data lifecycle for an AI enabled business process
Module 2: Privacy Management in AI Systems
- Overview of global privacy regulations including GDPR, CCPA, HIPAA, and the AI Act
- Consent management, data minimization, and responsible data handling
- Privacy preserving technologies and governance frameworks
- Case Study: Privacy breaches and compliance failures in AI systems
- Practical: Conduct a privacy risk assessment for an AI application
Module 3: Data Integrity and Ethical AI Practices
- Ensuring accuracy, completeness, consistency, and reliability of AI data
- Addressing bias, fairness, explainability, and transparency in AI models
- Ethical frameworks and responsible data use in AI environments
- Case Study: Bias and fairness issues in AI decision making systems
- Practical: Evaluate ethical risks and data quality issues in an AI scenario
Module 4: Risk Management and Compliance in AI Data Ecosystems
- Identifying operational, legal, and cybersecurity risks in AI data systems
- Compliance auditing, monitoring, and reporting practices
- Strengthening accountability and governance controls in AI environments
- Case Study: Governance and compliance failures in AI driven organizations
- Practical: Develop a compliance and risk monitoring checklist for AI systems
Module 5: Building an AI Data Governance Framework
- Designing AI data governance policies and operating frameworks
- Aligning governance with organizational goals and regulatory requirements
- Developing sustainable governance and oversight mechanisms for AI systems
- Case Study: Successful implementation of AI governance frameworks
- Practical: Capstone exercise: Develop a governance, privacy, and data integrity action plan for an AI system
Where the change lands
Organization Impact
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Stronger compliance with global data privacy and AI regulations
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Increased trust in AI-driven solutions from customers and stakeholders
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Reduced risks of data misuse, breaches, and ethical violations
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Improved alignment of AI initiatives with corporate governance frameworks
Individual Impact
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Mastery of data governance principles for AI environments
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Ability to implement privacy-preserving AI practices
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Skills to manage AI data integrity and bias risks
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Career advancement in data, governance, and compliance leadership roles
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
- NakuruNext
20 Jul → 24 Jul·In-Person
Book this intake - Kigali
20 Jul → 24 Jul·In-Person
Book this intake - Accra
20 Jul → 24 Jul·In-Person
Book this intake - Kisumu
27 Jul → 31 Jul·In-Person
Book this intake - Johannesburg
27 Jul → 31 Jul·In-Person
Book this intake - Dakar
27 Jul → 31 Jul·In-Person
Book this intake
Common questions.
Still not sure? Send us a note and a facilitator will get back to you within a business day.
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For corporate teams
Training 10+ professionals?
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