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Point One Zero built a data trust framework for a global confectionery and consumer goods manufacturer weighing AI use cases across its business. The chief information security officer needed to know what data would be used, where it would be processed and who would control it. We answered those questions before any model was selected.
AI proposals reached the security team after pilots were already designed, and the answer was often no. There was no shared rule for which data could go to external models, which needed private hosting and which should stay out of AI entirely. Business and security teams negotiated each use case from scratch.
We built a four-pillar framework covering data classification, where each class may be processed, access and audit controls, and governance of model vendors. We then mapped each proposed use case to an approved data path: an external model, a privately hosted model or a later phase. The framework and use case map were presented in a 16-slide briefing written for a security audience.
Security leadership received one standard for judging every AI proposal in place of case-by-case negotiation. Each proposed use case had a known data path before any build began, so teams could design within the rules instead of defending a finished pilot.
Each type of company data carries a clear processing rule.
Every use case assigned to external, private or deferred processing.
Access, audit and vendor governance defined up front.
Written around the CISO's questions, not a technology pitch.
Every proposed use case had an approved data path before a single model was chosen.