Data Governance for AI Readiness

Prepare policies for responsibly using GenAI in content generation.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course focuses on establishing robust data governance frameworks to support responsible adoption of Generative AI. Participants will learn how to create policies that ensure transparency, accountability, and compliance in AI-driven content creation while managing data quality and security.

Learning Objectives

Participants will be able to:

• Understand the role of data governance in AI projects

• Develop policies to ensure responsible and ethical use of GenAI-generated content

• Implement standards for data quality and access control

• Establish accountability mechanisms for AI data use

• Align governance practices with organizational and regulatory requirements

Target Audience

• Data governance officers

• AI project managers

• Compliance and risk officers

• Business leaders involved in AI strategy

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Lectures on data governance frameworks and AI policy design

• Interactive policy development workshops

• Case studies on governance challenges in GenAI

• Group discussions on ethical and regulatory considerations

Materials Provided

• Data governance policy templates

• Checklists for AI readiness assessment

• Frameworks for ethical AI data management

• Access to governance best practice resources

Outcomes

• Ability to draft and implement data governance policies for GenAI

• Enhanced understanding of governance roles in AI adoption

• Skills to ensure compliance and accountability in AI content generation

• Frameworks to monitor and improve governance effectiveness

Outline / Content

Day 1: Introduction to Data Governance in AI

• Principles of data governance and AI readiness

• Key challenges in governing GenAI data

Day 2: Policy Development for Responsible AI Use

• Crafting policies for content generation and data handling

• Defining roles and responsibilities in AI governance

Day 3: Ensuring Data Quality and Security

• Standards and controls for trustworthy AI data

• Managing data access and protection

Day 4: Compliance and Accountability Mechanisms

• Aligning governance with legal and ethical requirements

• Monitoring, auditing, and continuous improvement