Verifiable AI Provenance Framework (VAP): An Architectural Framework for Evidentiary-Grade AI Decision Trails

Automated decision-making systems, including AI and algorithmic systems in critical infrastructure, currently lack standardized mechanisms for producing evidentiary-grade provenance records that can withstand independent verification. Traditional logging approaches fail to provide the cryptographic guarantees required for regulatory compliance, forensic investigation, and cross- organizational accountability. This document describes the Verifiable AI Provenance Framework (VAP), an architectural framework that defines requirements for producing verifiable decision trails using existing IETF security technologies. VAP does not define new protocols or cryptographic primitives; rather, it provides an architectural coordination layer that enables domain-specific profiles to leverage Supply Chain Integrity, Transparency and Trust (SCITT), Remote Attestation Procedures (RATS), CBOR Object Signing and Encryption (COSE), and related IETF work in a consistent manner. This document is intended to frame the problem space and facilitate discussion about whether architectural coordination work is needed in this area.