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By Pascal HERNALSTEEN Neha PAREKH*
In every AI acquisition, the same question surfaces too late: where did the training data come from, and did anyone have the right to use it that way? The answer determines whether the core asset of the deal is worth what the model says it is. In 2026, it can also determine whether that asset survives regulatory scrutiny at all. The buildup of unverified datasets filled with personal data and undocumented training cycles, what practitioners now call privacy debt, has become a contingent liability that standard due diligence routinely fails to detect.
The Technical Problem: Why Personal Data Integration Is Irreversible
The...
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