// GLOSSARY

ML-BOM (AI-ML-BOM)

A component list for AI and machine-learning systems; the extension of the traditional SBOM (Software Bill of Materials) to cover the model artefact, dataset and training dependencies. Standards such as CycloneDX define a structured, machine-readable record form for ML components. Its necessity arises because an AI model's risk surface differs from traditional software: a model's security lies not only in the code that runs it but also in what data it was trained on, which pre-trained base model it inherited, and which libraries the training process depended on. A poisoned training dataset or a model weight downloaded from an untrusted source can create risk without leaving any trace in the code. ML-BOM turns these invisible dependencies into an inventory, so that when a vulnerability or poisoning is disclosed, the question 'were we affected' becomes answerable. It is the inventory leg of model supply-chain assurance, and provenance and attestation tie this inventory to evidence.