Artificial Intelligence and Machine Learning: How to Implement Good Information Governance

• An understanding of organizational information governance policies
• Familiarity with data protection or privacy regulations
• Some exposure to AI or machine learning concepts is helpful

Audience

• Information governance professionals
• Data protection officers and compliance managers
• Digital transformation or IT governance leads

AI and Machine Learning are rapidly evolving technologies with significant implications for data governance and organizational compliance.

This instructor-led, live training (online or onsite) is aimed at intermediate-level governance, privacy, and compliance professionals who wish to understand how to implement robust information governance practices in environments using AI and machine learning.

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

• Understand the core concepts of AI and machine learning in an information governance context.
• Identify typical areas where AI/ML is deployed in organizations and related IG risks.
• Evaluate how AI/ML interacts with Data Protection Principles, records management, and data quality.
• Apply DPIAs, governance frameworks, and risk mitigation approaches to AI/ML projects.

Format of the Course

• Interactive lecture and discussion.
• Real-world case examples and framework analysis.
• Group activities and guided document walkthroughs.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Understanding AI and Machine Learning

• What is AI and how is it defined?
• Machine Learning as a subset of AI
• Types of AI: weak, strong, generative, supervised, unsupervised

AI in Practice Across the Organization

• Where AI/ML currently exists in business functions
• Automation, decision support, customer service, and analytics
• Use cases in HR, finance, operations, and compliance

Common Governance Challenges

• Conflicts with the Data Protection Principles
• Lawfulness, fairness, and transparency in automated decision-making
• Accuracy, data minimization, and storage limitations

Foundations in Information and Data Management

• Information and records management in AI contexts
• The importance of metadata and audit trails
• Maintaining data quality and integrity for training datasets

Approaching Information Governance Challenges

• Designing governance controls for AI/ML pipelines
• Human oversight and explainability
• Building cross-functional governance teams

Conducting DPIAs for AI/ML

• Legal requirement and purpose of DPIAs
• Steps to assess proposed AI/ML implementations
• Documenting risk assessments, mitigations, and justifications

Governance Frameworks and Risk Management

• Overview of AI-specific governance frameworks
• ISO, NIST, ICO, and OECD approaches
• Risk registers and policy documentation

Culture, Integration, and Related Frameworks

• Embedding a culture of responsible AI use
• Linking AI governance with cybersecurity, ethics, and ESG policies
• Continuous improvement and monitoring

Summary and Next Steps