AI Bills of Materials (AI-BOMs) and Model Provenance: Contracting for Cybersecurity in AI-Enabled Manufacturing Supply Chains

Key Takeaways

Artificial Intelligence (AI)-BOMs and model provenance are emerging as cybersecurity diligence tools for manufacturers that buy or deploy systems. An AI bill of materials identifies the models, datasets, software components, APIs, and other dependencies that may create cyber exposure inside an AI system, while model provenance documents where those components came from, how they were trained or modified, and how they change over time.
For manufacturers and supply chain operators, these records can determine whether an organization can trace a corrupted dataset, identify a vulnerable open-source component, evaluate whether a vendor model update introduced new cyber risk, and preserve evidence when an AI-enabled production, quality, logistics, or maintenance system fails.
Vendor contracts should require both an initial AI bill of materials and ongoing model provenance obligations tied to cybersecurity accountability. Without clear disclosure, update, representation, audit, flow-down, and evidence-preservation terms, manufacturers may lack the information needed to determine whether an AI-related failure resulted from ordinary model performance, cyber compromise, or an undisclosed supply chain dependency.

Why AI Cybersecurity Needs Its Own Transparency Framework

A manufacturer deploys an AI-powered inspection tool on a production line. After a vendor model update, the system begins missing defects. The cybersecurity question is immediate: did the failure result from ordinary model drift, corrupted training data, a compromised model artifact, a vulnerable open-source component, an insecure model serialization format, or an undisclosed third-party API? If the contract and documentation do not identify the model, datasets, software dependencies, and update history, the manufacturer may not be able to answer that question when production is disrupted, sensitive operational data is exposed, or a customer demands an explanation.

That problem builds directly on two prior pieces. Our Foley colleague’s recent article on 5 Strategies for Reinforcing Supply Chain Cybersecurity addressed foundational cybersecurity practices such as vendor oversight, incident response planning, and security-by-design. Our earlier series article on Contract Strategies for AI Predictive Maintenance in Manufacturing and Supply Chains explained why AI vendor agreements often fail to reflect manufacturing realities. This article addresses one area of overlap between these topics in more detail. For AI-enabled manufacturing systems, the next cybersecurity contracting challenge is whether the manufacturer can see enough of the AI supply chain to identify, verify, and preserve evidence of the system’s components, data lineage, and model history.

An AI-BOM adapts a familiar manufacturing and cybersecurity concept to AI systems. Manufacturers already understand a bill of materials as a list of parts that go into a product, and cybersecurity teams increasingly use software bills of materials (SBOMs) to identify the software components and dependencies inside digital products. In 2026, CISA and its G7 partners issued guidance applying similar supply-chain transparency principles to AI systems. Under that approach, an AI-BOM documents the key components and dependencies that influence how an AI system functions, including models and model versions, training and fine-tuning datasets and their provenance, software and infrastructure dependencies, APIs and external services, and other elements relevant to cybersecurity, risk management, and supply-chain transparency. Model provenance is the companion concept. In plain terms, it is the chain-of-custody record for the model and its data: where they came from, who modified them, what validation or security testing was performed, and how changes were tracked over time. Together, these records turn an opaque AI tool into something procurement, legal, cybersecurity, and operations teams can evaluate before and after a cyber incident.

Why These Records Matter for Cybersecurity

AI cybersecurity risk does not stop at the vendor’s network perimeter. A production-facing AI system may include a vendor application, a pre-trained model, open-source libraries, cloud-hosted inference, customer-provided operational data, and periodic vendor retraining. Each layer can introduce a different cyber exposure, including vulnerable code, insecure integrations, opaque data flows, or dependencies controlled by subcontractors or other third parties.

A traditional vendor questionnaire may describe the vendor’s security program, but it may not reveal the open-source model, image-labeling service, third-party dataset, external API, or cloud inference layer embedded in the AI product. Without that component-level visibility of an AI bill of material, manufacturers may not know which dependencies to monitor, which vulnerabilities require remediation, or which supplier relationship created the risk.

Model provenance addresses the related integrity problem. NIST’s adversarial machine learning taxonomy (NIST AI 100-2, January 2024) recognizes that attackers may target training data, model parameters, or code during the learning process, and may also attack deployed models through evasion, privacy, or other techniques. Emerging supply chain vectors, including model poisoning through fine-tuning pipelines, insecure model serialization formats, and prompt injection targeting downstream integrations, further expand the attack surface that provenance records must help address. In other words, bad actors can now compromise AI systems not just by attacking the model directly, but also by tampering with the tools, data pipelines, and third-party services that feed into the model’s development and deployment. If a model begins missing defects, misclassifying inventory, generating abnormal maintenance recommendations, or exposing sensitive operational data, provenance records help determine whether the problem came from corrupted training data, a compromised model artifact, a changed version, or ordinary model drift. Without those records, causation becomes conjecture and responsibility becomes harder to allocate.

Contracting for Cybersecurity Through AI-BOMs and Model Provenance

Manufacturers and supply chain managers should address AI cybersecurity transparency expressly in vendor agreements rather than relying on general security language. The following provisions may be particularly helpful:

1. Define the Required AI Bill of Materials. Contracts should specify the minimum contents of the AI bill of materials, including models, software dependencies, datasets or dataset categories, APIs, cloud services, update mechanisms, and security-relevant dependencies. Unlike traditional SBOMs, for which mature and widely adopted formats such as SPDX and Cyclone DX exists, AI-BOM standards remain nascent and are still evolving. If the vendor cannot disclose certain details directly, the contract should require an alternative such as escrow, third-party security review, or tiered disclosure under enhanced confidentiality protections.

2. Require a Cybersecurity-Focused Model Provenance Package. The vendor should document the model’s origin, training and fine-tuning history, data provenance, validation approach, security testing, and known limitations. The agreement should also include representations regarding data rights, licensing, and controls used to protect model and data artifacts from tampering.

3. Make Transparency a Living Cybersecurity Obligation. AI-BOMs and provenance records should be updated when the vendor retrains the model, changes material datasets, switches components, or modifies update pipelines. The contract should require notice of material changes before they affect production systems, especially where changes may affect cybersecurity monitoring, data exposure, vulnerability management, or incident response.

4. Tie Disclosures to Cybersecurity Accountability. Vendors should represent that AI bill of materials and provenance records are materially accurate and complete. They should also notify the manufacturer of compromised components, unsupported dependencies, data integrity problems, or vulnerabilities affecting AI components, with audit and cooperation rights if inaccurate documentation contributes to a failure.

5. Flow Down AI Transparency Requirements. Many AI vendors rely on subcontractors, cloud providers, labeling vendors, foundation model providers, or open-source components. The contract should require flow-down obligations so the vendor can obtain the component, data, and model information needed to maintain the AI bill of materials and provenance package.

6. Use Documentation for Cyber Governance and Defensibility. AI-BOMs and provenance packages should feed into procurement review, IT/OT risk assessments, incident analysis, vulnerability management, regulatory readiness, and AI governance programs, including those aligned with ISO/IEC 42001. For manufacturers, this documentation increasingly forms part of the record used to demonstrate reasonable security practices, supplier oversight, cyber incident response readiness, and root-cause analysis capabilities. However, the evidentiary value of these records depends on contractual requirements governing accuracy, update cadence, retention, and independent verification. Vendor self-attestation alone may not provide an independently verifiable record sufficient for high-stakes litigation, regulatory examinations, or forensic investigations.

Looking Ahead

Manufacturers that ask only whether an AI vendor is “secure” may miss the more important cybersecurity question: can we identify, verify, and prove what is inside the AI system and how it got there? As CISA, NIST, ISO/IEC 42001, the EU AU Act’s transparency requirements, and international regulators move toward greater AI supply chain transparency, manufacturers and supply chain managers that build AI bill of materials and model provenance obligations into vendor relationships will be better positioned to detect cyber risk, evaluate vendor performance, preserve incident evidence, and allocate responsibility when AI systems fail.

Foley’s Manufacturing, Supply Chain, Cybersecurity, and AI teams are available to help manufacturers and supply chain managers structure AI vendor diligence, AI bill of materials requirements, model provenance obligations, and cybersecurity-focused contract protections tailored to production and supply chain environments.

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