Category: Software Supply Chain Security
AI Bill of Materials
Also known as: AIBOM, AI-BOM, AI SBOM, Software Bill of Materials for AI
Simply put
An AI Bill of Materials (AIBOM) is a structured, machine-readable inventory that lists the components used to build and operate an AI system, such as datasets, models, and software elements. Much like an ingredients list, it is intended to give organizations greater visibility into what goes into an AI system so they can better understand and manage their supply chain. It supports transparency and security, but on its own it is a documentation artifact rather than a guarantee that the listed components are safe or trustworthy.
Formal definition
An AIBOM is a structured inventory that documents the constituent components of an AI system, typically including datasets, models, and software components used to build and operate that system. It extends the concept of a software bill of materials (SBOM), which functions as an "ingredients list" for software, to the additional artifacts distinctive to AI systems; some framings treat the AIBOM as a specialization of an SBOM for AI. Its primary purpose is to enhance AI supply chain transparency and support security analysis. Scope and completeness vary by generation approach and tooling (for example, open-source generators such as the OWASP AIBOM Generator), and the evidence here does not establish a single standardized set of required elements; practitioners should note that an inventory documents component composition but does not by itself constitute assurance, independent verification, or risk assessment of those components.
Why it matters
AI systems assemble components from an extended and often opaque supply chain: pre-trained models, third-party datasets, open-source libraries, fine-tuning artifacts, and inference-time software. In many organizations this composition is poorly documented, which limits the ability to answer basic supply chain questions such as which external models or datasets a deployed system depends on, and where those components originated. An AIBOM is intended to make that composition explicit as a structured, machine-readable inventory, extending the established SBOM 'ingredients list' concept to the artifacts distinctive to AI. This visibility positions organizations to reason about their AI supply chain rather than treating a model as an unexamined black box.
Who it's relevant to
Third-Party and AI Supply Chain Risk Teams
For teams assessing vendors that supply or embed AI, an AIBOM can surface the underlying models, datasets, and software components a third party relies on, improving visibility that often stops at the first tier. It is an input to risk assessment, not a substitute for one: an inventory documents component composition but does not by itself verify the safety, licensing, or trustworthiness of the listed components, nor does it extend automatically to fourth-party or Nth-party dependencies unless those are captured.
Security and Vulnerability Management Functions
Security teams can use an AIBOM to support supply chain transparency and security analysis, for example by knowing which datasets, models, and software elements are in scope when a vulnerability or integrity concern arises in a shared component. However, the AIBOM identifies composition rather than assessing exposure; it does not on its own constitute a vulnerability scan, threat model, or assurance that components are free of defects.
Procurement and Contract Owners
Procurement and contract owners may request an AIBOM as part of onboarding to establish what an acquired AI system contains. Because completeness varies by generation approach and no single standardized element set is established here, buyers should specify what an AIBOM must include rather than assuming uniform coverage, and should recognize that a supplier-provided AIBOM is a documentation artifact, not independent verification of the underlying components.
Governance, Compliance, and Model Risk Stakeholders
Stakeholders responsible for AI governance and documentation can treat an AIBOM as a transparency and inventory foundation that supports broader accountability efforts. It is worth noting that regulatory expectations for AI transparency differ across regions and sectors, and an AIBOM as described here does not confer certification or compliance; it is one artifact that may support, but does not guarantee, meeting such obligations.
Inside AIBOM
Model components and provenance
An inventory of the AI models included in a system, typically noting their origin (internally developed, open-source, or third-party/commercial), version identifiers, and, where available, the party responsible for training. This supports traceability but often depends on what the supplying party is willing or able to disclose.
Training data characteristics
Information about the datasets used to train or fine-tune a model, which may include data sources, licensing terms, and known limitations. Depending on the provider, this section is frequently incomplete, as full training data disclosure is not always available or contractually obtainable.
Dependencies and third-party libraries
A listing of software libraries, frameworks, pre-trained weights, and other components the AI system relies on. This overlaps conceptually with a traditional software bill of materials (SBOM) but extends it to model-specific and data-specific dependencies.
Fourth-party and Nth-party AI elements
Identification of AI components sourced from parties beyond the direct supplier, such as an upstream foundation model embedded in a vendor's product. Visibility here is typically limited and diminishes with each additional tier.
Intended use and known limitations
Documentation of the model's intended purpose, operating constraints, and disclosed limitations or evaluation results. This describes what the model is designed to do but does not by itself validate performance in a given deployment context.
License and usage terms
The licensing conditions attached to models, weights, and data, which affect permissible use, redistribution, and downstream obligations. These terms vary significantly across open-source and commercial offerings.
Common questions
Answers to the questions practitioners most commonly ask about AIBOM.
Is an AIBOM just a software bill of materials (SBOM) applied to AI systems?
No. While an AIBOM shares the inventory-and-transparency philosophy of an SBOM, it is not simply an SBOM relabeled for AI. An SBOM typically enumerates software components, libraries, and dependencies. An AIBOM extends beyond code to capture elements distinctive to AI systems, such as models, training and evaluation datasets, model provenance, and associated configuration, that a conventional SBOM does not address. Depending on how a program defines its AIBOM, some elements may overlap with an SBOM, but treating the two as interchangeable risks omitting the data and model dimensions that make AI supply chains distinct.
Does maintaining an AIBOM verify that a supplied model is safe, unbiased, or compliant?
No. An AIBOM is an inventory and transparency artifact; it documents what components, data, and models are present, not whether they meet safety, fairness, or regulatory expectations. The presence of an AIBOM does not constitute independent verification of a model's behavior, and it should not be read as an attestation or certification of compliance. In many programs an AIBOM supports risk assessment and due diligence, but conclusions about safety, bias, or compliance require separate evaluation, testing, and, where warranted, independent validation beyond what the document itself provides.
What information does an AIBOM typically capture for a third-party AI system?
The contents vary by program and by the risk tier assigned to the system, and there is no single universally mandated schema. In many implementations an AIBOM records the models in use and their provenance, the datasets used for training or evaluation, key dependencies, and relevant configuration or versioning details. What it captures for any given supplier depends on what the supplier is willing or contractually required to disclose, and self-disclosed contents may lack independent validation.
How does an AIBOM fit into an existing third-party risk management program?
An AIBOM is typically used as an input to due diligence and ongoing monitoring rather than as a standalone control. It can inform risk assessment for AI-related supplier relationships, help identify concentration on particular models or data sources, and support incident response by clarifying what components are in use. It generally centers on the organization's direct third-party relationships; visibility into fourth-party or Nth-party dependencies embedded within a supplied model may be limited unless those are separately disclosed.
How often should an AIBOM be updated once a system is onboarded?
Because AI systems change as models are retrained, fine-tuned, or reconfigured and as dependencies are updated, a point-in-time AIBOM can become stale. Many programs therefore treat the AIBOM as a living artifact refreshed on material change or on a defined cadence tied to risk tier, rather than a one-time onboarding deliverable. The appropriate frequency depends on the volatility of the system and the update commitments a supplier is contractually willing to make.
What are the main limitations of relying on an AIBOM for supplier oversight?
Key limitations include dependence on supplier self-disclosure, which may be incomplete or lack independent validation; the point-in-time nature of any snapshot, which can lag actual changes to models or data; limited visibility beyond the first tier into embedded fourth-party or Nth-party components; and the absence of a single standardized format, which can complicate comparison across suppliers. An AIBOM also generally addresses transparency and inventory rather than directly covering financial, operational, geopolitical, or ESG risk, so it should be combined with other controls rather than relied on in isolation.
Common misconceptions
An AIBOM is simply a software bill of materials (SBOM) applied to AI.
While an AIBOM extends SBOM concepts, it typically adds model-specific and data-specific elements such as training data provenance, model versions, and intended-use limitations that a conventional SBOM does not capture. Treating the two as identical understates the additional disclosure an AIBOM aims to provide.
An AIBOM provides complete visibility into how a model was built and how it will behave.
An AIBOM is largely a disclosure artifact that reflects what a supplier is willing or able to document. Training data details and upstream (fourth-party or Nth-party) model components are frequently incomplete, and the document does not independently verify a model's behavior or performance in a specific deployment.
Producing an AIBOM addresses AI-related third-party risk on its own.
An AIBOM supports inventory and traceability but does not by itself assess or mitigate risk. It is typically a point-in-time artifact that can become stale as models, data, and dependencies change, and it does not substitute for ongoing monitoring, contractual controls, or independent validation.
Best practices
Treat the AIBOM as one input to third-party AI risk assessment rather than a standalone control, pairing it with due diligence and ongoing monitoring appropriate to the risk tier.
Specify in contracts what AIBOM elements a supplier must provide and keep current, recognizing that training data and upstream model disclosures are often incomplete and may need to be negotiated.
Extend inquiry beyond the direct supplier to identify fourth-party and Nth-party AI components, while acknowledging that visibility typically diminishes with each additional tier.
Refresh the AIBOM on a defined cadence and on material change to models, training data, or dependencies, since point-in-time documentation can become stale.
Distinguish supplier attestations within the AIBOM from independently verified information, and seek independent validation for higher-risk deployments where feasible.
Capture license and intended-use terms explicitly so downstream teams can confirm permissible use and identify constraints before deploying a model in a given context.
