Spinning up LLMs in regulated environments? Here's what gets overlooked.

Spinning up LLMs in regulated environments? Here's what gets overlooked. When we work with organisations in healthcare, finance, and other regulated industries, we see the same critical gap: teams excited about AI capabilities, but underestimating the data sovereignty implications. Microsoft Foundry and similar platforms are powerful, but they introduce a complex question: where is your sensitive data actually going? In our experience, three things get missed: 1. PII doesn't just mean names and addresses anymore — it's any data that could identify individuals in your training sets 2. Your existing data agreements likely weren't written with LLMs in mind — they need review before you start feeding data into AI models 3. "It's in Azure" doesn't automatically mean compliant — the specifics of how Foundry processes data matters The teams getting this right aren't moving slower — they're just asking the right questions upfront. Read your data agreements. Understand your data flows. Document your decisions. This isn't about fear. It's about building AI implementations that last. Full conversation on data sovereignty, Foundry, and what regulated industries need to know: https://share.transistor.fm/s/d441ec0a #DataSovereignty #AIGovernance #MicrosoftAzure #RegulatedIndustries #ResponsibleAI