AI Governance and Policy Design

Establish clear rules and roles to guide responsible AI usage.

Pillar

Process – Workflow, Governance, Risk & Efficiency

Overview

This course provides a comprehensive framework for developing effective AI governance policies that ensure ethical, compliant, and responsible use of Generative AI within organizations. Participants will learn to define roles, responsibilities, and processes to manage AI risks and align with regulatory requirements.

Learning Objectives

Participants will be able to:

• Understand the principles of AI governance and policy frameworks

• Develop organizational policies for responsible GenAI use

• Define roles and accountability for AI oversight

• Manage compliance with legal and ethical standards

• Create monitoring and enforcement mechanisms for AI governance

Target Audience

• Compliance and risk management professionals

• Legal advisors and policy makers

• AI project leaders and governance teams

• Senior management and executives

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and case studies

• Policy drafting workshops

• Role-playing scenarios for governance enforcement

Materials Provided

• AI governance policy templates

• Compliance checklists and risk assessment tools

• Case study compendium on AI governance failures and successes

• Certificate of completion

Outcomes

• Establish a clear and practical AI governance framework

• Align AI usage with ethical and legal standards

• Enhance organizational accountability for AI initiatives

• Mitigate risks associated with Generative AI deployment

Outline / Content

Day 1: Foundations of AI Governance

• Introduction to AI ethics, risks, and governance principles

• Overview of regulatory landscape and standards

• Identifying AI-related risks and impact areas

Day 2: Policy Development and Organizational Roles

• Drafting AI usage policies and guidelines

• Defining roles, responsibilities, and oversight committees

• Establishing decision-making and escalation processes

Day 3: Compliance and Risk Management

• Integrating AI governance with existing risk frameworks

• Addressing data privacy, bias, and security concerns

• Monitoring and reporting mechanisms

Day 4: Enforcement and Continuous Improvement

• Implementing training and awareness programs

• Handling policy violations and incident response

• Continuous policy review and adaptation

• Final workshop: Developing a tailored AI governance policy