// GLOSSARY

Model Supply Chain

An evidence-based assessment of the trustworthiness of the model and the release pipeline across the data, pipeline, registry and deployment chain. It is the backbone concept of AI Model Supply-Chain Assurance. It must not be confused with the runtime chain (Generative AI Red Team), and this distinction is the heart of the concept: one examines trust in the model's production process, the other risk in the behavior that arises while the model runs. The first asks 'can I trust this model and the pipeline that produced it', the second 'what can this model do while it runs'. Model supply-chain assurance asks the first question separately at every link of the chain: did the training data come from a trusted source, was the build pipeline hardened, was the promote decision to the registry audited, can the artefact be altered at the moment of deployment. Supply-chain attacks like SolarWinds and XZ Utils show why these questions are vital: even the best-defended system collapses from within if a component it trusts is poisoned. SLSA frames the maturity levels, and provenance and attestation the evidence of each link.