Training on Data Governance, Privacy and Integrity in Artificial Intelligence (AI)

What is Data Governance, Privacy and Integrity in AI?

Data governance in AI refers to the frameworks, policies, and controls used to manage data availability, quality, security, and accountability across AI systems. Privacy focuses on protecting personal and sensitive data in compliance with regulations, while integrity ensures data accuracy, consistency, and reliability throughout the AI lifecycle.

AI systems depend on large volumes of data, yet many organizations lack structured governance frameworks. This results in data misuse, privacy violations, regulatory non-compliance, biased AI outputs, and loss of stakeholder trust. Poor data integrity further leads to unreliable models and flawed decision-making.

This course provides a structured approach to implementing robust data governance in AI. Participants will learn how to design governance frameworks, ensure privacy compliance, and maintain data integrity across the AI lifecycle. It combines regulatory guidance, technical controls, and operational best practices to enable responsible AI deployment.

Key Concepts Covered

• Data governance frameworks for AI systems
• Data privacy principles and regulatory compliance (e.g., GDPR-aligned practices)
• Data integrity, quality, and lifecycle management
• Ethical AI and responsible data use
• Data classification, ownership, and stewardship
• Risk management in AI data pipelines
• Bias, fairness, and data accountability
• Data security and access controls

Participants will apply skills to:
• Design and implement AI data governance frameworks
• Ensure compliance with privacy and data protection regulations
• Improve data quality and integrity in AI models
• Reduce bias and ethical risks in AI systems
• Build trust in AI-driven decision-making
• Manage data across the AI lifecycle (collection to deployment)

By the end of the course, participants will be able to establish effective data governance structures, ensure privacy compliance, maintain high data integrity, reduce AI risks, and build trustworthy, ethical AI systems.

Duration

5 Days

Who Should Attend

• Data governance and data management professionals
• AI and machine learning practitioners
• Data scientists and data engineers
• Compliance, risk, and legal professionals
• IT and cybersecurity specialists
• Digital transformation and innovation leaders
• Policy makers and regulators

Organization Impact

• Stronger compliance with global data privacy and AI regulations

• Increased trust in AI-driven solutions from customers and stakeholders

• Reduced risks of data misuse, breaches, and ethical violations

• Improved alignment of AI initiatives with corporate governance frameworks

Individual Impact

• Mastery of data governance principles for AI environments

• Ability to implement privacy-preserving AI practices

• Skills to manage AI data integrity and bias risks

• Career advancement in data, governance, and compliance leadership roles

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

• Explain the importance of data governance, privacy, and integrity in AI systems.

• Understand global regulatory frameworks (GDPR, CCPA, HIPAA, AI Act, ISO standards).

• Apply governance models to manage data lifecycle in AI systems.

• Ensure data quality, transparency, and fairness in AI algorithms.

• Implement privacy-preserving technologies (differential privacy, anonymization, encryption).

• Develop ethical and compliant data governance policies for AI projects.

Module 1: Foundations of Data Governance in AI

• Principles of data governance

• Data lifecycle in AI systems

• The intersection of AI, data, and organizational strategy

Module 2: Privacy in AI Systems

• Global privacy regulations (GDPR, CCPA, HIPAA, AI Act)

• Consent management and data minimization

• Privacy-preserving technologies and frameworks

Module 3: Data Integrity and Ethical AI

• Ensuring accuracy, completeness, and reliability of AI data

• Addressing bias, fairness, and transparency

• Ethical frameworks for data use in AI

Module 4: Risk Management & Compliance

• Identifying risks in AI data ecosystems

• Compliance auditing and monitoring practices

• Case studies of privacy and governance failures in AI

Module 5: Building a Governance Framework

• Designing AI data governance policies

• Aligning governance with organizational goals and compliance requirements

• Capstone: Developing a governance, privacy, and integrity action plan for AI systems