Privacy Theater Audit for Enterprise AI Procurement
C5/10March 21, 2026
WhatA compliance and risk assessment tool that helps enterprises determine whether their AI data sensitivity requirements genuinely require local inference or if cloud/third-party APIs are sufficient, with cost modeling for each path.
SignalMultiple commenters observe that corporate privacy concerns about AI data handling almost always evaporate when teams see the cost difference between local GPU infrastructure and cheap API calls — suggesting most privacy requirements are theater rather than genuine constraints.
Why NowThe gap between API inference costs (near-zero with DeepSeek) and local inference costs ($65K+ hardware plus ongoing infra) has become so extreme that companies need a rational framework to make this decision rather than defaulting to expensive local deployments.
MarketEnterprise IT decision-makers evaluating build-vs-buy for AI inference; $2-5B TAM in AI procurement consulting; competes with traditional consulting firms who lack AI-specific cost modeling.
MoatProprietary dataset of actual enterprise AI workload costs and privacy incident rates that improves recommendations over time.
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