AI procurement questionnaire generator
Buying an AI system is a supply-chain risk decision. The vendor’s model, training data, security posture, and failure modes all become your exposure once the system touches your customers. This generator builds a structured due-diligence questionnaire tailored to the kind of AI you’re procuring, the risk level of the use case, and your industry — so your RFP covers the questions that actually matter instead of a generic security checklist.
How it works
You pick three things: the AI system type (chatbot, decision/scoring model, generative content, computer vision, recommendation engine), the risk level of the use case, and your sector. The tool then assembles questions across six domains — data governance, security controls, fairness and bias, explainability and transparency, reliability and SLAs, and regulatory compliance. Higher risk levels and regulated sectors unlock additional, deeper questions. The output is plain text you can copy straight into a vendor assessment or RFP.
Why generic security checklists miss AI-specific risks
Standard IT procurement checklists cover the right questions for most software purchases: data encryption at rest and in transit, access control, penetration testing cadence, incident response time, disaster recovery, and subprocessor lists. These questions are necessary for AI procurement but not sufficient.
AI systems introduce a second category of risk that generic checklists do not reach:
Model and training data risks — What data trained the model? Does the vendor have clear documentation of training data provenance? Were individuals’ personal data used without consent? Could the model have memorised and reproduce identifiable information from training data? Generic security checklists have no mechanism to ask these questions.
Fairness and bias risks — Has the model been tested for differential performance across demographic groups? What were the results? What is the vendor’s process for identifying and mitigating algorithmic bias in production? For decision-making AI (scoring, ranking, approvals), these questions are directly relevant to discrimination law and, in regulated sectors, to regulatory compliance.
Explainability and audit risk — Can the vendor explain why the system made a specific decision? Is there an audit log of individual decisions that can be reviewed after the fact? Can a human easily override the system’s outputs? These questions matter wherever the AI’s output affects people’s access to services, credit, insurance, or employment.
Vendor lock-in and model lifecycle risks — What happens when the vendor changes the underlying model? Will behaviour change, and when? How much notice will the vendor give before sunsetting the model version you’re relying on? How easy is it to migrate to another provider? These are procurement and operational risk questions that need answers before contract signature, not after.
Questions to ask for each AI type
Chatbot / conversational AI — How is the system prompt protected from user manipulation? What topics or responses are the system programmed to refuse? How is personally identifiable information in conversations handled?
Decision or scoring model — What is the documented accuracy across different demographic groups in your target population? What is the vendor’s re-training schedule and how are you notified of model updates? Who is liable when the model makes an incorrect decision?
Generative content — What safeguards prevent the system from producing harmful, defamatory, or copyright-infringing content? How are outputs logged for review? Does the vendor’s data use terms allow user content to be used for model training?
Tips for getting useful answers
Require evidence, not assurances: model cards, SOC 2 / ISO 27001 reports, data-processing agreements, and bias-test results in writing. Treat vague or defensive answers as findings in their own right. Send the questionnaire early — before you’ve emotionally committed to a vendor — and give procurement, security, and legal each a section to score. Re-run it at contract renewal, because a vendor’s model and data practices change over time.