AI is everywhere in the vendor ecosystem. The race to embed AI into products has also embedded unknown risks into your supply chain. Knowing what AI your suppliers use is difficult enough. Knowing whether their due diligence actually addresses the risks that AI introduces is another challenge entirely.
Customers and regulators are increasingly probing how and where AI is used across organizational supply chains. The questions below give you a practical starting point. They’re designed to supplement traditional vendor evaluations, which typically focus on the vendor organization or technical model performance. What they don’t cover, and what this guide does, is the mechanics of the underlying AI technology, the sustainability of its performance over time, and how the vendor is managing associated risks.
Getting Started with AI Vendor Due Diligence
Most vendor evaluations weren’t built with AI in mind. Standard security and procurement questionnaires ask about data handling, access controls, and SLA commitments. Those questions still matter. But they don’t surface the governance practices, training data provenance, or bias mitigation approaches that determine whether a vendor’s AI is actually fit for purpose in your environment.
The questions that follow cover five areas: core technology, training data, performance, risk management, and ethical considerations. Together, they give you the context you need to make an informed decision, not just about what an AI system does, but how it was built, how it’s maintained, and whether the vendor behind it is operating responsibly.
Understanding Vendor AI Technology and Capabilities
Question 1: What type of AI model does your system use, and is it explainable?
The AI model type matters because different architectures come with different trade-offs between speed, complexity, and how outputs are generated. Equally important is whether the model is explainable. Explainable AI architectures allow users to understand how outputs were generated. Others are black boxes. Depending on the use case, explainability may be a regulatory requirement, not just a preference.
Question 2: What is the intended use of your system?
Some AI systems are purpose-built for specific tasks. Others are general-purpose. Creative repurposing of AI systems is common, but it comes with risks and limitations. Understanding what a system was originally designed to do is the first step toward evaluating whether it’s fit for purpose in your environment.
Evaluating AI Training Data and Model Foundation
Question 3: What is the source of your training data?
Training data is one of the most significant constraints on AI model performance. Every data source reflects the population and context that generated it. There are no universal datasets that represent every culture, demographic group, or language. Beyond representational gaps, training data can carry legal and ethical implications. Data collected from copyrighted sources, for example, may carry use restrictions in certain jurisdictions. And if your use case serves a specific population, you need to know whether the model’s training data reflects that population.
Question 4: Will inputs to the system be used as training data?
Some AI systems use live inputs to retrain or fine-tune models over time. That can be a problem if your inputs contain personally identifying information, trade secrets, or other sensitive material. Some regulations also require that model inputs be retained for monitoring purposes. Know what happens to your data once it enters the system.
Assessing AI Model Performance and Validation
Question 5: How was the AI model tested, evaluated, validated, and verified?
Model quality assurance typically includes automated evaluation against test data, benchmarking, human review, and third-party red teaming. Knowing what evaluations were conducted, and how the model performed, sets realistic expectations and surfaces gaps before they become operational problems.
Question 6: What are the known limitations of the system?
Every AI model has limitations: constraints from training data, design decisions, or update frequency. Those limitations should be clearly documented by the vendor. Your organization will also likely impose its own limitations based on your risk tolerance and use context. Understanding how existing limitations interact with your requirements is essential before deployment.
AI Vendor Risk Management and Governance
Question 7: What are your organization’s AI policies?
AI governance isn’t just about the model. It requires organizational buy-in at every level, including documented policies that address trustworthiness, transparency, and responsible design across the AI system’s full lifecycle. Vendors should be able to demonstrate what policies are in place and how they align with applicable AI governance regulations.
Question 8: How have you operationalized your AI policies?
Policies describe intent. Operationalization determines whether that intent translates into practice. Ask vendors how their policies are implemented day-to-day, who is accountable for AI governance decisions, and how compliance is tracked and evidenced over time.
Question 9: How do you document your AI systems?
Transparency in the AI supply chain depends on documentation. That documentation should cover both technical and non-technical aspects of the AI system’s lifecycle, with clear ownership and traceability for each section. This is increasingly a regulatory requirement. Vendors who can’t produce structured documentation on demand are a governance risk by definition.
AI Ethics and Bias Considerations
Question 10: What bias and fairness considerations went into your AI model?
“Fair and unbiased” is not a sufficient answer. Fairness is measurable, and there are multiple ways to measure it. Ask vendors what specific metrics they used, what populations were evaluated, and what the results showed. A vendor who can articulate quantitative bias testing gives you the foundation for your own subsequent evaluation. One who can’t is telling you something important.
Implementing Your AI Vendor Evaluation Process
Building a consistent evaluation process starts with standardizing these questions into your procurement workflows. That means incorporating them into RFP templates, vendor questionnaires, and contract language, not treating them as ad hoc checks.
Consider tiering your depth of evaluation based on risk. A vendor providing a general-purpose AI writing assistant warrants a different level of scrutiny than one providing AI-driven underwriting recommendations or clinical decision support. Higher-risk use cases should trigger deeper evaluation, including documented evidence rather than just vendor attestation.
Assign clear internal ownership for the review. Vendor AI due diligence sits at the intersection of legal, procurement, compliance, and technical risk. Without a designated owner and a structured workflow, questions like these stall in inboxes.
Ongoing AI Vendor Monitoring and Lifecycle Management
Vendor due diligence at procurement is necessary. It’s not sufficient. AI systems change. Models are retrained, capabilities are extended, data sources shift. A system that passed your evaluation criteria at signing may look different 18 months into production.
Build vendor oversight into your ongoing governance program, not just your procurement process. That means setting defined review cadences, requiring vendors to notify you of material changes, and tracking incidents and near-misses across your third-party AI portfolio.
The organizations with the most exposure to AI supply chain risk aren’t necessarily those with the weakest procurement standards. They’re the ones who treated the initial evaluation as the finish line.
Selecting the right AI vendor requires looking beyond technical capabilities and cost. The questions here are a starting point for evaluating whether a supplier’s AI will fulfill your organization’s strategic, operational, and risk requirements. The answers will tell you a great deal, and so will the vendors who can’t answer them.
Trustible helps organizations govern third-party AI with structured vendor profiles, AI-assisted document analysis, and risk assessments designed for the complexity of the modern AI supply chain. Request a Demo.
