What question did this study set out to answer?

The research aims to establish robust standards for the provenance and validation of AI and software components.

May 9, 2026Open Access

AUGMANITAI Provenance Standards — AI-SBOM + C2PA + W3C-VC + SHACL-Shapes + System-Prompts

Key Points

The research aims to establish robust standards for the provenance and validation of AI and software components.
Developed a restricted bundle of 17 files encompassing various provenance and validation standards.
Implemented infrastructure for AI Software Bill of Materials (AI-SBOM) and related validation frameworks.
Designed templates for cryptographic proof and configurations for verifiable credentials.
Achieved standardized formats for software provenance and authenticity with AI-SBOM and C2PA.
Validated the effectiveness of SHACL shapes in RDF constraint validation.
Demonstrated reproducibility of LLM behavior through structured system-prompts.

Abstract

Restricted bundle of 17 files covering wissenschaftliche Provenance- und Validierungs-Infrastruktur: AI-SBOM (Software-/AI-Bill-of-Materials in CycloneDX + SPDX + Generator-Skript), C2PA (Coalition for Content Provenance and Authenticity Manifest-Generator + Template + Training-Mining-Assertion 'do-not-train signal'), W3C Verifiable Credentials (AUGMANITAI-Authorship VC + Issuer-Konfiguration + Cryptographic-Proof-Template), SHACL Shapes (Term-Shapes + Relation-Shapes für RDF-Constraint-Validierung), System-Prompts (kompakter + Long-Form + JSON-strukturierter Inject für reproduzierbare LLM-Behavior-Eval). Strict-Wissenschaft, Standards-konforme Provenance-Infrastruktur.

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