Data governance: the silent liability

Everyone is racing to ship AI features. But before asking what your AI can do, there’s a more fundamental question: do you actually know what’s happening with the data behind it?

Where did the data come from? Who owns it? Can you trust it? Is it biased or incomplete? And what happens when the answer your model produces is simply wrong?

For startups and scaleups, data governance can feel like something to worry about later — once you have more customers, a bigger team, or a compliance function. In reality, weak data practices can become a silent liability much earlier, undermining model performance, creating legal and reputational exposure, and raising uncomfortable questions during fundraising, partnerships, or due diligence.

This talk cuts through the jargon to explore what data governance actually looks like in an AI-first business, the real costs of getting it wrong, and the practical steps founders and teams can take today to build better foundations.

You don’t need a huge compliance team or a six-month governance project. You do need to know what data you have, where it came from, and whether you can trust it.


Who this is for

Startups and scaleups interested in AI and data practices.