Data Governance & Stewardship for AI

Define roles and rules for ethical, compliant AI data use.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course explores the critical role of data governance and stewardship in responsible AI adoption. Participants will learn how to establish clear policies, assign roles, and implement frameworks to ensure data used in GenAI projects is managed ethically, securely, and in compliance with regulations.

Learning Objectives

Participants will be able to:

• Understand principles of data governance and stewardship in AI contexts

• Define roles and responsibilities for managing AI data assets

• Develop policies to ensure ethical and compliant use of data

• Implement controls to maintain data privacy and security

• Monitor and audit data practices for continuous improvement

Target Audience

• Data governance officers and stewards

• AI program managers and compliance leads

• Data scientists and engineers

• Legal and risk management professionals

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive sessions on governance frameworks and case studies

• Workshops for role definition and policy drafting

• Group discussions on ethical challenges and regulatory compliance

• Practical exercises in data stewardship best practices

Materials Provided

• Sample data governance frameworks and templates

• Checklists for compliance and ethical guidelines

• Case studies highlighting governance successes and failures

• Tools for auditing and monitoring data use

Outcomes

• Ability to design and implement AI-specific data governance programs

• Clear understanding of stewardship roles in AI initiatives

• Enhanced capability to ensure ethical and compliant data usage

• Frameworks for ongoing governance and risk management

Outline / Content

Day 1: Fundamentals of Data Governance and Stewardship

• Introduction to data governance principles in AI

• Key roles and responsibilities

Day 2: Developing Policies and Ethical Guidelines

• Drafting data use policies for AI projects

• Ethical considerations and frameworks

Day 3: Implementing Controls and Compliance Measures

• Privacy, security, and regulatory compliance

• Auditing and monitoring data practices

Day 4: Continuous Governance and Improvement

• Governance lifecycle and feedback loops

• Case studies and lessons learned