Making AI Compliance Evidence Machine-Readable

• URL: http://arxiv.org/abs/2604.13767v1
• Date: Wed, 15 Apr 2026 11:51:54 GMT
• Title: Making AI Compliance Evidence Machine-Readable
• Abstract summary: This paper proposes OSCAL as a candidate interchange format for AI governance.<n>We define 16 property extensions covering lifecycle phases, enforcement semantics, risk traceability, and risk-acceptance justification.<n>The architecture and reference implementation are open-source under Apache RM 2.0.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI Assurance -- producing the machine-readable evidence required to demonstrate compliance with AI governance frameworks -- has mature policy scaffolding but lacks the infrastructure to operationalize it. Organizations building high-risk AI systems under the EU AI Act face a gap: frameworks such as the EU AI Act, ISO/IEC 42001, and NIST AI RMF specify what to assure but provide no executable format for how. This paper proposes OSCAL -- the NIST standard adopted for FedRAMP cybersecurity compliance -- as a candidate interchange format for AI governance, complementing rather than replacing the emerging JTC21 standards stack. We define 16 property extensions covering lifecycle phases, enforcement semantics, risk traceability, and risk-acceptance justification, and present a three-layer Compliance-as-Code architecture (policy, evidence, enforcement) that generates assurance evidence as a byproduct of model training. The SDK produces native OSCAL Assessment Results validated against the NIST JSON schema. We test the approach on two Annex III high-risk systems: a credit scoring model and a medical imaging segmentation system. The architecture and reference implementation are open-source under Apache 2.0.

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