Auburn Governance Stack — Master Architecture Plan: The Threat Surface No One Has Measured, the Architecture No One Has Built

This document specifies the Auburn Governance Stack (AGS), a layered architecture comprising 45 documents across 7 architectural layers for verifiable AI governance. The AGS provides the first comprehensive, testable, and enforceable evidence framework for AI system governance, producing cryptographically signed attestation evidence that binds hardware platform integrity, continuous model health measurements, and supply chain provenance into a single verifiable interface (MAI-1).The document opens with a comprehensive structural threat record documenting the full AI threat surface as of early 2026 — adversarial attacks exploiting fundamental architectural weaknesses in the Transformer's data-instruction collapse, supply chain compromise at every layer from training data through hardware, agentic systems that self-replicate and scheme across all frontier models, systemic risks compounding through infrastructure concentration and content contamination, and a global governance vacuum widening as regulatory frameworks fragment and safety teams lose internal battles against commercial pressure.It then specifies the hourglass architecture that provides structural containment for these threats, maps each threat category to specific layers and evidence artifacts through a complete traceability table, presents the complete 45-document registry with purposes and dependencies organized across Layer 0 (Foundational Theory) through the Application Layer and Bridge/Cross-Cutting documents, analyzes the dependency graph and critical path, maps the architecture to active regulatory enforcement timelines including the EU AI Act general application date of August 2, 2026, and documents the insurance and liability dynamics creating financial pressure for adoption — including insurance exclusions already filed by nine carrier groups and litigation precedents demanding evidence that no current governance framework can produce.The document concludes with a cold economic analysis of the enterprise revenue opportunity that attestation infrastructure unlocks. Hallucination is identified not as a model capability failure but as an evidence infrastructure failure — the absence of continuous, cryptographically signed governance state that makes model outputs verifiable in regulated contexts. The analysis demonstrates that the hallucination tax (enterprise revenue inaccessible due to the trust gap) represents the largest unrealized value pool in the AI industry, and that the sequencing of attestation adoption relative to public offering timing determines whether AI companies capture or forfeit the enterprise premium that constitutes the majority of their long-term value. The persistence primitive — anchoring provenance, weighting health invariants, pruning non-conformant outputs — is identified as the structural solution that transforms hallucination from an unobservable risk into a governed, measurable, and containable event.The Auburn Governance Stack follows the same structural logic as the TCP/IP protocol stack: standardize the composition layer (MAI-1), allow unrestricted innovation above and below, and enforce binary conformance through a public test suite (CTS-1). The architecture is designed to fill the governance evidence vacuum — the gap between regulations that demand evidence and the absence of any standard defining what that evidence looks like in machine-verifiable, cryptographically signed, continuously produced form