Your AI team wants the data. Your instinct says: "not so fast." Who's right? It's the question landing on every privacy desk: can we actually use this dataset to train or power AI — and can we prove it if a regulator asks? The old answer ("it's anonymized") no longer holds. Regulators now expect contextual, quantitative proof that re-identification risk is genuinely low, and "we ran it through a checklist" won't survive scrutiny. The good news: the same pressure that's raising the bar is also making it possible to clear it at scale. On July 16, 2026, OneTrust Data Guidance hosted a webinar featuring Woodway’s Khaled El Emam, where we showed how privacy teams are using LLMs to turn slow, subjective data reviews into fast, defensible, repeatable decisions.
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