Can You Trust the AI You Didn't Build?
A structured audit of your AI supply chain - from foundation model provenance to dataset integrity to third-party plugin dependencies - with a complete AI Bill of Materials.
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AI supply chain security addresses a category of risk that most security programs have not yet incorporated: the vulnerabilities, backdoors, and integrity failures that can enter your AI systems through the third-party models, datasets, and plugins you didn’t build yourself.
The Trust Problem in AI Supply Chains
When your engineering team integrates a pre-trained model from Hugging Face, downloads a fine-tuned model from a research organization, or connects an AI agent to a third-party plugin, they are making an implicit trust decision. They are trusting that the model was trained on legitimate data, that its behavior matches its documentation, that no one has injected adversarial examples into the training pipeline, and that the model has no hidden triggers or backdoors.
Most organizations make these trust decisions without any structured AI supply chain due diligence. The model works in testing. The output quality looks right. The integration is approved and deployed.
This is not sufficient. Supply chain attacks against AI systems are documented, increasing in frequency, and specifically designed to evade functional testing.
What We Audit
Our AI supply chain security audit covers every component in your external AI dependency chain:
Foundation models accessed via API from providers like OpenAI, Anthropic, Google, Mistral, and others - assessed for provider security practices, data processing agreements, known vulnerabilities, and update change management practices.
Third-party pre-trained and fine-tuned models used from Hugging Face, research repositories, or model marketplaces - assessed for training data provenance, model card completeness, known vulnerability disclosures, and backdoor indicators.
Training and fine-tuning datasets sourced from external repositories - assessed for data integrity, licensing compliance, known poisoning incidents, and documentation quality.
AI plugins and tool integrations connected to your LLM agents - assessed for least-privilege design, input validation, data handling practices, and provider security posture.
The AI-BOM as a Security Asset
The AI Bill of Materials we produce is not just a compliance document - it is an operational security asset. When a new AI vulnerability is disclosed, your security team can immediately determine whether any component in your AI-BOM is affected. When a third-party provider releases a model update, your team has a baseline to compare against. When a new AI component is proposed for integration, you have a structured framework for supply chain due diligence.
AI supply chain risk management is increasingly required by enterprise customers, regulators, and insurance underwriters. The AI-BOM and provenance analysis from this audit gives your team the documentation to meet those requirements.
Engagement Phases
Inventory
Complete enumeration of all third-party AI components: foundation models, fine-tuned models, training datasets, AI plugins, tool integrations, and external AI API dependencies. Ownership and version tracking established.
Provenance Analysis
For each component: training data source verification, model card review, provider security assessment, licensing and compliance review, and known vulnerability cross-reference against AI vulnerability databases.
Risk Assessment
Dependency risk scoring - probability and impact assessment for each supply chain component. Model update change management gap assessment. Backdoor and trojan model indicator review.
Reporting
AI-BOM generation, risk assessment report, vendor security questionnaire development, and remediation recommendations for high-risk supply chain components.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Supply Chain Visibility | Unknown - no inventory of third-party AI components | Complete AI-BOM with provenance and risk scores in 1-2 weeks |
| Vendor Risk | Third-party AI providers never security-assessed | Vendor risk scores and tailored security questionnaire for procurement |
| Compliance Evidence | No AI supply chain documentation for auditors | AI-BOM and provenance analysis ready for NIST AI RMF and EU AI Act review |
Tools We Use
Frequently Asked Questions
What is an AI-BOM and why do I need one?
An AI Bill of Materials (AI-BOM) is a structured inventory of all AI components in your system - analogous to a Software Bill of Materials (SBOM) but covering models, training datasets, and AI-specific dependencies. The EU AI Act and NIST AI RMF increasingly require documented AI supply chain information. Beyond compliance, an AI-BOM is essential for managing model update risks, responding to AI vulnerability disclosures, and conducting efficient security reviews when new AI components are added.
What supply chain risks are unique to AI?
AI supply chains introduce risks that have no equivalent in traditional software: training data poisoning (a dataset used to fine-tune a model could contain adversarial examples that create backdoors), model trojans (a pre-trained model could contain hidden behaviors triggered by specific inputs), capability drift (a model update from a provider could change behavior in ways that break safety assumptions), and data lineage opacity (the provenance of training data used by large foundation models is often poorly documented, making intellectual property and bias risks hard to assess).
Do you assess Hugging Face models?
Yes. Hugging Face-hosted models are a common source of supply chain risk - the platform hosts hundreds of thousands of models with varying documentation quality, provenance transparency, and security review. We assess Hugging Face models for known vulnerabilities, model card completeness, training data sourcing, and community trust signals. We also cross-reference reported models against AI vulnerability disclosure databases.
How often should an AI supply chain audit be repeated?
An initial audit establishes your baseline AI-BOM and identifies the highest-risk supply chain components. After that, audits should be triggered by significant events: a major model update from a provider, addition of a new third-party AI component, a supply chain vulnerability disclosure affecting AI systems you use, or a compliance audit preparation. We recommend an annual comprehensive refresh at minimum.
Can you assess closed-source models like GPT-4 or Claude?
For closed-source foundation models, we assess at the API boundary: the provider's security documentation, published model cards, terms of service, data processing agreements, and known security disclosures. We cannot inspect model weights or training procedures for closed-source models, but we can assess what the provider has documented and committed to - and identify where those commitments are insufficient for your use case.
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