Closing The AI Governance Gap Is Urgent: Your Usage Data Is The Bridge

Arti Raman is the founder and CEO of Portal26. She is an expert on managing and mitigating risk for enterprise GenAI and data.

​Although it may be an exaggeration to say AI governance has become of biblical importance, when was the last time the leader of one of the world’s major religions felt the need to comment on the subject?​

I am, of course, talking about the pope’s recent warning about where AI is heading and the need for humans to remain in control. It’s a vital debate, but there’s no doubt that AI governance is becoming much harder. Indeed, many organizations are already reaching the limits of their ability to ensure AI aligns with internal and external requirements.​

Rapidly growing model complexity and the greater autonomy being given to agents are significantly adding to the governance challenge. News from the AI frontier that OpenAI has scrapped the “rollout of a new model over safety concerns” should inform the thinking of every organizational leader.​

The Information Blindspot

​But, for the average enterprise and their governance teams, what’s really at stake, and where should they focus their efforts?​

On a fundamental level, AI governance depends on having a reliable understanding of how AI is being used across an organization. On paper, this sounds like a relatively straightforward objective. A leader should be able to ask, “Where and how are we using these systems?” and get a reliable answer.​

A big part of the challenge—and this is going to become a much bigger problem in the short to medium term—is that few organizations have a single, reliable view of all the ways AI is being used. Implementation has outpaced governance, and to an extent, that was inevitable given the unprecedented pace of the AI bandwagon.​

What’s left is a worrying governance blind spot that’s increasingly incompatible with the regulations organizations must now observe.​