Skip to content
Policy2 min read

AI Governance Frameworks: Preparing for 2025 Regulations

With new AI regulations taking effect across multiple jurisdictions, companies need robust governance frameworks. Here's how to build one.

Share:

As AI regulation accelerates globally, companies developing or deploying AI systems must implement comprehensive governance frameworks to manage legal and operational risk.

Modern AI regulation is risk-tiered: obligations scale with the potential harm of a use case rather than the technology itself. The same underlying model can be low-risk in one application and high-risk in another, which means governance has to be organized around use cases, not around 'the AI.' The first task in any framework is therefore an inventory — a catalog of where AI is used, for what decision, affecting whom — because you cannot classify or control what you have not mapped.

High-risk uses carry the heaviest documentation burden: data-governance records, testing for accuracy and bias, human-oversight design, and technical documentation sufficient for a regulator to reconstruct how the system works. Much of this is achievable only if it is built into the development lifecycle. Retrofitting bias testing or oversight controls onto a shipped system is expensive and rarely convincing; embedding them as gates in the build process is both cheaper and more defensible.

Governance also has to be cross-functional to work. Legal defines the obligations, engineering implements the controls, and a standing review body owns the decisions about which use cases proceed and under what conditions. The organizations that struggle are the ones that treat AI governance as a legal sign-off at the end; the ones that succeed treat it as an operating process with clear owners, documented decisions, and the same audit trail they would expect for any other regulated activity.

Key Takeaways

  • Regulation is risk-tiered by use case — inventory where AI is used and what decisions it affects first.
  • High-risk uses require data-governance, bias testing, human oversight, and reconstruction-grade documentation.
  • Build controls into the development lifecycle; retrofitting oversight onto shipped systems is costly and weak.
  • Make governance a cross-functional operating process with named owners, not an end-of-line legal sign-off.

This analysis is provided for general information and is not legal advice. For guidance on how these developments apply to your situation, our team is here to help.

AI PolicyGovernanceRegulatory ComplianceRisk Management

Questions About This Topic?

Our attorneys are available to discuss how these developments affect your business.