The AI governance conversation has reached boardrooms — and it's here to stay. With advancing regulations, enterprise clients auditing their vendors, and sensitive data flowing into third-party models, the board will ask questions. These are the questions coming your way and how to answer them substantively.
The number one question, and rightly so. The correct answer requires knowing exactly which vendors touch which data and under what terms. Major vendors' enterprise agreements exclude the use of data for training — but only if you're on the right plan with the correct configuration. A data flow inventory by use case is the minimum deliverable.
Every AI-assisted decision needs a named human in charge. Practical governance defines three levels: decisions AI makes alone (low risk, reversible), decisions it proposes and a human approves, and decisions where AI only informs. Mapping each use case to its level is an afternoon exercise that saves entire crises.
“AI governance isn't an 80-page document. It's knowing what systems you have, what data they touch, what decisions they make, and who is accountable for each.”
For multi-country companies in LATAM, compliance is a mosaic: LGPD in Brazil, different data protection laws by country, regulated sectors with their own rules. The good news: a well-designed architecture solves 90% by design — data residency, anonymization in sensitive flows, and audit logs from day one cost little to build and a lot to improvise later.
If you can't compile that list on a single page today, that's your starting point — and it's better to have it ready before the board asks for it.
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