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Bring the AI to your data, not your data to the AI

For the past few years, enterprises that wanted to build their own agentic systems adopted AI the same way: Pick a frontier model, copy your knowledge into it, then route your tickets through it. Eventually you’re piping customer records, contacts, and workflows into someone else’s system, hoping the contract protects you. The premise was that the capability alone justified moving your data to…

Bring the AI to your data, not your data to the AI

Enterprises are rethinking their approach to AI adoption, prioritizing data security over model capabilities. Traditionally, firms have moved their data to frontier models, hoping contracts protect them. However, risks are growing as AI capabilities improve rapidly, while the data remains exposed. Satya Nadella, Microsoft’s CEO, warned that enterprises pay twice for AI intelligence – once with money and again with proprietary knowledge.

Palantir’s Alex Karp emphasized the need for control over compute, models, and data stacks. IBM’s 2025 Cost of a Data Breach Report revealed that shadow AI usage in 20% of breaches added $670,000 to the average incident cost. Enterprises are now seeking AI solutions that keep data within their governance and residency perimeters.

The EU AI Act's compliance deadlines are being pushed back, but data-residency and sovereignty rules are increasing globally. Only 22% of organizations feel confident about passing an AI governance audit within three months. To ensure a secure AI deployment, enterprises must ask vendors three crucial questions: can data stay within their governance and residency perimeter, can they provide an audit trail, and can they swap the underlying model without exposing data?

If vendors can't answer these questions, enterprises should reconsider their partnership. As technology evolves, data governance should remain a priority to safeguard the invaluable asset that is a company's data.

Written by urgent.news from Fast Company's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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