The Authorization Certification Layer: Closing the Self-Attestation Gap in Five-Layer AI Governance Frameworks

Contemporary AI governance frameworks organize compliance obligations across sequential layers covering data governance, model governance, system integration, control and monitoring, and audit and evidence. These frameworks correctly identify the dimensions of AI risk and provide structured guidance for practitioners. They share, however, a common architectural limitation: the evidence they produce at the control and audit layers is generated and held by the same operator whose conduct is being evaluated. This paper identifies this self-attestation gap as a structural deficiency in five-layer AI governance stacks and proposes a discrete Authorization Certification Layer designed to address it. The proposed layer operates through pre-execution cryptographic authorization certificates issued before each governed AI action, binding the identity of the authorizing human, the declared scope of permission, and the temporal validity of that permission into a tamper-evident record independently verifiable by any external party without reliance on the operator. This paper describes the architectural rationale, the regulatory alignment, and the prior art basis for this layer, which is covered by 41 USPTO provisional patent filings personally held by the author with priority date November 23, 2025, comprising 4,514 claims.