Protected: Zero Trust for Data: Beyond Labels to Continuous, Provable Control
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Zero Trust Reality Check: Questions to Assess Data and AI Defensibility
This article helps executives evaluate the credibility of Zero Trust claims in data and AI environments. It highlights how confidence often exceeds the available proof, exposing gaps in enforcement and accountability. The diagnostic questions focus on contemporaneous evidence, ownership clarity, and semantic consistency. These issues reflect predictable outcomes of scaling complex controls without explicit governance. Recognizing these gaps sharpens executive judgment during live evaluations.
