An AI Bill of Materials (AI BOM) is a detailed, structured inventory that documents every component of an AI system — from training data and model architectures to software libraries, APIs, and hardware dependencies. Analogous to a Software Bill of Materials (SBOM) in traditional software, an AI BOM provides transparency into the AI supply chain.
An AI BOM typically includes: model information (architecture, version, provider, training methodology), training data sources (datasets used, data provenance, licensing terms), software dependencies (frameworks, libraries, and their versions — e.g., PyTorch, TensorFlow, Hugging Face), third-party APIs and services (external AI services integrated into the system), hardware requirements (GPU/TPU specifications, cloud infrastructure), evaluation datasets and benchmarks (how model performance was validated), known limitations and biases (documented weaknesses and edge cases), and licensing and intellectual property information.
The AI BOM serves critical governance functions: vulnerability management (tracking which AI components have known security issues), compliance documentation (demonstrating due diligence for regulatory requirements), risk assessment (understanding the full dependency chain of AI systems), incident response (quickly identifying affected systems when a component is compromised), and vendor management (maintaining visibility into third-party AI dependencies).
As AI regulation matures, AI BOMs are becoming a best practice recommended by frameworks like the NIST AI RMF and expected under regulations like the EU AI Act for high-risk AI systems.
