Two-Rail Verification: A Public-Private Evidence-Separation Kernel for Institutional AI

Abstract

AI agents and institutional automation increasingly require public accountability, while many operational records must remain private due to personal data, trade secrets, contractual terms, security constraints, or audit boundaries. This paper introduces Two-Rail Verification, a public-private evidence-separation kernel for institutional AI and AI-agent governance. The proposed kernel distinguishes between a Public Rail, where public claims, status, version, timestamps, hashes, and verification routes can be placed, and a Private Rail, where raw records, personal data, cost structures, contracts, internal logs, secrets, and unpublished evidence remain protected. The contribution is not a new cryptographic primitive, a certification scheme, or a production assurance claim. Rather, Two-Rail organizes existing concepts such as hashes, signatures, manifests, verification kits, verifiable credentials, selective disclosure, zero-knowledge proofs, transparency logs, audit trails, and assurance reports into an institutional evidence-separation discipline. The kernel is expressed through four minimal requirements: cross-rail write prohibition, verified public claims, a verifiable public surface, and an accountability interface. The paper discusses the public-private evidence problem, adjacent technical and governance concepts, minimal public-surface design, use cases for AI agents and institutional records, and limitations. It does not claim third-party verification, complete signature coverage, legal compliance, safety guarantees, or an effective royalty-free patent pledge. Related patent applications may be pending, but any future patent pledge or license should be published separately with an effective date and stable URL.