Why Open Cryptographic Standards Matter for AI Auditability: A Systematic Case for Third-Party Verifiable Audit Infrastructure

This paper presents a systematic argument for why open cryptographic standards are structurally necessary for meaningful AI auditability.

Drawing on over a century of cryptographic theory (Kerckhoffs’ principle, Shannon’s maxim), cross-jurisdictional regulatory analysis (EU AI Act, MiFID II, SEC Rule 17a-4, Sarbanes–Oxley, NIST), and empirical evidence from both open standards (AES, TLS) and proprietary failures (GSM A5/1, Dual_EC_DRBG), the paper demonstrates that audit infrastructures relying on proprietary or opaque mechanisms cannot satisfy regulatory requirements for independent verification by design.

The paper introduces the concept of an AI Auditability Benchmark as a vendor-neutral evaluation framework for assessing the “evidence quality” of AI and algorithmic decision systems, and explains how open protocols such as the VeritasChain Protocol (VCP) and the Verifiable AI Provenance Framework (VAP) provide the enabling conditions for compliance.

The central conclusion is that trust in AI systems must shift from organizational reputation to mathematically verifiable properties—a transition that only open cryptographic standards can support.