Building Practical AI Governance: A GC's Framework for Risk, Readiness & Compliance

This program will provide General Counsel and in-house legal leaders with a practical and actionable framework for governing enterprise AI use. Using real-world examples and a structured governance model, the program walks through the most pressing AI legal risks, how to build an AI risk-management program from scratch, and the key decision points involving policies, inventory, vendor due diligence, incident response, and model oversight.

The course will benefit GCs, CLOs, in-house counsel, privacy officers, compliance leaders, and legal departments responsible for advising on AI deployment, drafting AI governance policies, mitigating regulatory exposure, and aligning technical and business stakeholders on safe adoption.

Learning Objectives: 

1. Identify the top legal, regulatory, and operational risks posed by enterprise AI use, including privacy, bias, IP, and model security exposures, and evaluate their impact on corporate risk posture

2. Develop a foundational AI governance strategy by understanding the core components of an AI program: inventorying systems, establishing decision rights, drafting policy stacks, and implementing tier-based risk controls

3. Assess vendor AI tools and third-party risks using levels of assurance (L1-L5) and contractual protections, including no-training clauses, data-use limitations, audit rights, and technical proofs

4. Prepare for AI-related incidents by comparing traditional cybersecurity IR plans to AI-specific IR requirements (e.g., hallucinations, bias events, model drift, or supply-chain compromise)

5. Discuss best practices for aligning stakeholders, including legal, HR, security, data science, executives, and the board, and establishing ethical oversight roles such as a Chief AI Officer or Responsible AI committee