How to Implement an Effective AI Governance Program

With artificial intelligence increasingly embedded in business operations, many organizations are proactively developing AI governance programs, according to Danielle Miller Olofsson of Stikeman Elliot. In this article, she discusses the regulatory approach of existing legal frameworks and acknowledges key design factors, including compliance, transparency, and accountability. 

A well-structured AI governance initiative begins by forming a cross-functional team comprising information technology professionals and representatives from various departments, including legal, compliance, human resources, procurement, and others. This team is responsible for defining acceptable AI use cases, recommending safeguards, and monitoring internal practices and external regulations. While coordinating such a broad group may seem challenging, it ensures organization-wide buy-in and a smoother implementation.

Next, it’s essential to define what constitutes AI within the organization’s unique culture. The National Institute for Standards and Technology’s Risk Management Framework provides a broad definition, but organizations must tailor it to reflect their specific technologies, use cases, and ethical standards. For instance, some generative AI tools may pose risks if they are trained using confidential or sensitive data, making them unsuitable for use in certain environments.

Another critical component is understanding the legal landscape. While comprehensive AI laws are scarce, sector-specific regulations and general legal principles still apply. Organizations must identify their “danger zones,” such as data protection, intellectual property rights, and anti-discrimination laws, to account for potential liability. 

Implementing a formal AI governance program also requires drafting governance and employee use policies, training staff, conducting regular testing and audits, and maintaining a registry of deployed AI tools. Risk assessments should guide the use of AI, and oversight mechanisms need to support continuous improvement.

Finally, AI governance extends to contracts. Reviewing master services agreements is vital to clarifying data ownership, liability, and service standards. Given AI’s rapid evolution, ongoing vigilance, legal awareness, and cultural alignment are essential to responsible AI governance.