AI Governance: A Framework for Convergent-Cost Classification, Privacy-Preserving Routing, and Federated Vocabulary Learning

Abstract

The proliferation of specialized, high-performance AI agents has introduced the "model selection problem", where routing systems frequently prioritize cost-efficiency while neglecting data sovereignty. Relying on full-context prompt transmission to third-party providers inherently risks exposing sensitive intellectual property and consumer PII. To mitigate these systemic vulnerabilities, this work introduces a formal Governance framework that shifts the routing paradigm from data-heavy submission to a secure, keyword-centric classification cascade. By integrating privacy-by-architecture decontextualization, convergent-cost routing, and federated vocabulary learning, our framework decouples AI utility from prohibitive marginal costs and security risks. Applying the principle that "data which does not exist cannot be leaked", this architecture ensures robust protection of organizational assets. This approach transforms AI Governance from a reactive compliance measure into a proactive, structural component, providing a scalable, sustainable, and inherently secure foundation for specialized AI deployment in high-stakes environments.