Introduction

As organisations accelerate the adoption of artificial intelligence, data governance, privacy protection, and data integrity have become critical enablers of sustainable and compliant AI deployment. AI systems rely heavily on large volumes of data, making them particularly vulnerable to privacy breaches, regulatory non-compliance, and integrity failures that can undermine trust and operational performance.

The Data Governance, Privacy & Integrity in Artificial Intelligence course provides a structured and practical understanding of how organisations can govern AI data responsibly while balancing innovation and regulatory obligations. The course explores global and regional privacy regulations, governance frameworks, and integrity controls, enabling participants to design AI systems that are secure, compliant, and resilient. By linking governance, privacy, and integrity into a unified approach, this course supports confident decision-making and long-term value creation in AI-driven operations.

Key focus areas include:

 

Key Learning Outcomes

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

 

Training Methodology

The Data Governance, Privacy & Integrity in Artificial Intelligence course uses an expert-led, applied learning approach that combines structured instruction with real-world case analysis. Participants engage in guided discussions and practical scenarios to translate governance, privacy, and integrity principles into actionable controls within AI-driven environments.

Data Governance, Privacy & Integrity in Artificial Intelligence (AI)

Who Should Attend?

This training course is ideal for professionals seeking to strengthen data governance and privacy controls in AI environments, including:

  • Data Governance and Data Protection Professionals
  • Risk, Compliance, and Governance Managers
  • Technology and AI Leaders
  • Information Security and Privacy Officers
  • Legal and Regulatory Affairs Professionals
  • Senior Managers overseeing AI initiatives

Course Outline

Day 1

Global Data Privacy Landscape and AI

Global Privacy Framework

  • EU GDPR and AI Systems
  • China's Personal Information Protection Law (PIPL)
  • Saudi Arabia's Personal Data Protection Law (PDPL)
  • UAE Federal Decree Law No. 45 of 2021
  • African Data Protection Harmonization Framework

Regional Focus

  • SAMA Data Privacy Guidelines
  • Qatar Financial Centre Privacy Rules
  • African Regional Frameworks
  • Privacy implementation in AI systems
  • Cross-border data transfer solutions
  • Compliance monitoring systems
Day 2

AI Data Governance Frameworks

Core Components

  • Data Classification Systems
  • Data Lifecycle Management
  • Privacy by Design Principles
  • Data Quality Management

Implementation

  • Data Governance Operating Models
  • Privacy Impact Assessments
  • Data Protection Controls
  • Monitoring Systems
Day 3

AI Privacy Risk Management

Risk Framework

  • Privacy Risk Assessment Models
  • Data Protection Impact Assessments
  • Vendor Risk Management
  • Cross-border Data Transfers 

Technical Controls

  • Data Anonymization Techniques
  • Encryption Standards
  • Access Control Systems
  • Audit Mechanisms
  • Risk assessment methodology
  • Technical controls implementation
  • Compliance monitoring
Day 4

Practical Implementation

  • Organizational Integration
  • Privacy Governance Structure
  • Role Definition and Responsibilities
  • Training and Awareness
  • Change Management

 Monitoring

  • Privacy Metrics Development
  • Incident Response
  • Reporting Frameworks
  • Continuous Improvement
Day 5

Future Trends

  • Emerging Topics
  • Privacy-Preserving AI Techniques
  • Federated Learning
  • Synthetic Data
  • Edge Computing Privacy

Ready to Take the Next Step?

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FAQs

  This course focuses on governing data used in AI systems to ensure privacy compliance, data integrity, and regulatory alignment. It provides practical guidance on managing AI data risks while enabling responsible and trusted AI adoption.  

  Yes, the Data Governance, Privacy & Integrity in Artificial Intelligence course examines global and regional privacy frameworks to help organisations manage compliance across jurisdictions. It enables participants to understand how regulatory requirements impact AI data use and governance decisions.  

  Absolutely. The course is highly relevant for organisations operating AI systems that need stronger data governance, privacy protection, and integrity controls. It also supports organisations planning future AI initiatives by embedding governance early.  

  Yes, data integrity is a core theme of the course, covering controls across the data lifecycle used in AI models. Participants gain insight into preventing data manipulation, ensuring data quality, and maintaining reliable AI outputs.  

  Yes, the Data Governance, Privacy & Integrity in Artificial Intelligence course emphasises transparency, accountability, and compliance. These elements are essential for building trust with regulators, customers, and internal stakeholders.  

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