Enterprise AI solutions depend on complex supply chains that include foundation models, open-source libraries, datasets, embeddings, model hubs, APIs, containers, prompts, retrieval components, plugins, agent tools, and third-party services. Weaknesses or tampering anywhere in this chain can introduce security, integrity, licensing, privacy, and operational risks. This paper proposes an Enterprise AI Supply Chain Security and Model Provenance Framework that combines asset inventory, trusted sourcing, software and model bills of materials, provenance metadata, cryptographic integrity, vulnerability management, dependency analysis, provider assurance, artifact signing, environment controls, and continuous monitoring. The framework defines controls across acquisition, development, training, fine-tuning, deployment, update, and retirement. It also addresses model lineage, dataset lineage, prompt provenance, external tools, agent dependencies, and evidence for audit and incident response. The proposed approach enables enterprises to verify what AI components are used, where they originated, how they changed, and whether they remain trusted throughout the lifecycle.

Sanjeeve Kumar Gajadi, "Enterprise AI Supply Chain Security and Model Provenance Framework" International Journal of Scientific Research and Engineering Development, V9(5): Page(1914-1916) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.