Choose an AI provider by evaluating a specific use case, data flow and deployment—not by relying on a brand name or certificate alone. Define what the AI will do and what information it will handle, examine the provider’s evidence and contract, test the service with representative work, and assign clear owners for risks that remain.
1. Define the use case and data before comparing providers
Start with the business task, not a vendor shortlist. Write down what the AI system will do, who will use it, who could be affected, and what decisions or actions its output may influence. Consider the consequences if it gives a wrong answer, exposes information, acts without authorization or becomes unavailable.
Map the data the system will encounter—not just what users type into a prompt. Include uploaded files, retrieval sources, connected applications, logs, feedback and telemetry. Classify each data type under your organization’s rules, such as public, internal, confidential, personal, regulated or customer data. Identify where human review is necessary and which uses or data types are off-limits.
A provider’s general “enterprise” label does not establish that a particular data type or workflow is suitable. Check the service, tier, settings and deployment mode you would actually use. The U.S. General Services Administration’s Buy AI guidance is written for federal agencies, but its prompts about mission needs, data flows, storage, protections and pilot projects can help private businesses structure their own review. Its federal contracting routes and eligibility rules should not be assumed to apply to private buyers.
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Set the risk context
Security is one part of trustworthy AI, not a synonym for it. NIST’s AI Risk Management Framework describes characteristics that include reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. Which matter most depends on the system’s purpose, context and potential impact. A tool that drafts internal meeting notes does not present the same consequences as one that influences access to services or makes consequential recommendations.
Record the expected benefit alongside the potential harms, affected people, operational dependencies and cost of failure. This gives you criteria for deciding whether to proceed, not just a list of features to request.
2. Ask the provider for evidence about data handling and access
Ask the supplier to describe its privacy and cybersecurity approach for the proposed system, including how data is protected. NIST’s AI Procurement in a Box includes that question in sample specification 3.1. Request answers that apply to your exact product, service tier and configuration, and ask for supporting documents where possible.
- Collection and use: What inputs, outputs, logs, feedback and telemetry are collected, and for what purposes? Can submitted data be used to train, fine-tune, evaluate or otherwise improve models? Do settings or contract terms change that use?
- Retention and deletion: How long is each data category kept? How are deletion requests handled, including backups and derived or inferred data?
- Location and access: Where are data processed and stored? Which provider staff, subprocessors or connected services may access it, under what approval and least-privilege controls, and how is access recorded and reviewed?
- Safeguards: What protections apply in transit, at rest and during processing? Ask for their scope and limitations rather than treating a control name as proof that every part of your workflow is covered.
- Incident handling: How are incidents detected, escalated and resolved? Ask how the provider will notify your organization, whom it will contact and what the expected process is.
NIST’s procurement workbook also prompts buyers to ask about threat identification, testing expertise, need-to-know access to data and models, and the retention, access and external sharing of usage or enriched data. Where encryption or anonymization is relevant and feasible, ask how it is applied to the proposed system. Evaluate the answers against the data and risks you identified, not against a generic promise of security.
3. Examine AI-specific threats and testing
Ask how the provider identifies and tests threats that could affect your actual deployment. NIST identifies adversarial examples, data poisoning, and the exfiltration of models, training data or intellectual property through AI endpoints among AI security concerns. For generative AI, the relevant questions can include prompt injection, unsafe use of connected tools, data leakage, manipulated inputs, vulnerable retrieval sources and exposure of proprietary material.
Request the test scope and methods, who performed the work, when it was performed, what product and configuration it covered, what limitations or exceptions were found, and how problems are remediated. Distinguish independent assessment evidence from a supplier’s own description. A company-wide certification or general security report may not cover the product, service tier, deployment mode or controls you intend to use.
NIST’s Generative AI Profile, published July 26, 2024, recommends documented, iterative testing and cautions that pre-deployment methods may be inadequate or may not reflect the deployment context. A benchmark result or security report therefore cannot, by itself, establish that your workflow is safe. Test the system’s behavior against the risks and tasks that matter in your setting.
4. Map responsibilities, integrations and contract terms
Draw a responsibility map for the provider, your business and every relevant dependency: cloud or model hosts, connectors, plug-ins, data sources and implementation partners. For each control, identify the accountable party and the evidence your team can inspect. Depending on the deployment, the map may cover identity and access configuration, endpoint protection, data classification, connector permissions, user training, retention settings, incident response, continuity and employee-use policy.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThis division matters because buying a service does not transfer every security responsibility to the supplier. NIST’s procurement workbook notes that commercial off-the-shelf and bespoke AI systems may rely on controls managed by the purchasing authority. NIST’s Generative AI Profile also warns that third-party integrations can increase intellectual-property, privacy and information-security risks. Review what each connected service can access and what it can do, rather than treating an integration as a neutral feature.
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Review the contract and service documents for permitted data uses, confidentiality, deletion, subprocessors, security incident notice, audit evidence, availability, changes to models or features, suspension and termination, data export or deletion at exit, and liability allocation. Consider whether the terms give you enough visibility and control to operate the system responsibly. The applicable legal duties depend on your jurisdiction, sector, data and use case; have qualified legal and privacy specialists assess the actual terms and requirements.
5. Compare providers on the same criteria
If you have multiple genuine candidates, evaluate them against the same use case, data, workload and deployment assumptions. Ask for evidence scoped to the product and configuration under consideration; do not collapse the decision into a single certificate or score.
| Decision area | What to compare |
|---|---|
| Data governance | Training and improvement use, retention and deletion, processing locations, subprocessors and access transparency. |
| Security evidence | Scope and recency of independent attestations, access controls, safeguards, incident handling, vulnerability response and AI-specific testing disclosures. |
| AI risk controls | Robustness evidence, protections for connected tools, model and feature change controls, monitoring, human override and disclosure of limitations. |
| Buyer control | Configuration options, identity integration, audit logs, data controls, ability to disable features, portability and exit support. |
| Operational fit | Reliability and performance on representative tasks, availability, support, integration burden and ability to investigate failures. |
| Contract and cost | Clarity of responsibilities, acceptable data terms, incident notice, continuity and termination terms, predictable pricing and cost controls. |
Record unanswered questions and differences in evidence, not just the supplier’s stated capabilities. A provider that best fits one use case may not be the best fit for another; these criteria support a context-specific decision, not a universal ranking.
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6. Pilot the service before broad rollout
Run a limited pilot with an authorized group before expanding access. Use representative tasks and data that your organization permits for testing, and set success criteria and stop conditions in advance. Include measures relevant to the use case, such as output quality, reliability, inappropriate disclosure, unauthorized actions, latency or outages, human review burden, and whether consequential actions can be stopped or reversed.
- Choose participants and scenarios: Include representative user roles, routine work and relevant edge cases. Keep access limited to what the pilot needs.
- Test safeguards and failure modes: Exercise the risks identified in your review, including data boundaries, permissions and connected tools where applicable. Record limitations and incidents instead of treating isolated successes as proof.
- Preserve human control: For higher-impact decisions, provide meaningful human review and an override or interruption path. NIST’s procurement workbook asks suppliers to describe human decision-making at critical control points and whether operators or data subjects can intervene in harmful or incorrect decisions.
- Evaluate against a baseline: Compare results with the existing process or another candidate when there is a real alternative. Use the agreed thresholds to decide whether to expand, change controls, repeat testing or stop.
GSA recommends testbeds, sandboxes or pilots and starting with a small user group before larger purchases; that guidance is for federal agencies. NIST’s broader Generative AI Profile supports iterative, documented evaluation while noting that current testing methods have limitations. Treat pilot findings as evidence about the tested tasks and configuration—not as a blanket claim that a provider is secure.
7. Document the decision and keep reassessing it
Choose the provider that best meets the documented use-case requirements and your organization’s risk tolerance. Record why the system is appropriate, what information it may process, prohibited uses, evidence reviewed, tests run, residual risks accepted, and the person accountable for those risks. Assign owners and deadlines to gaps, and define the conditions that would trigger a fresh review or suspension.
Set clear ownership for operational monitoring and incident handling. Reassess when the provider changes data practices, models, features, subprocessors, deployment architecture or contract terms, and when your business expands the use case or changes the data it supplies. Keep the responsibility map and approved-use rules current as well.
NIST describes the AI RMF as voluntary guidance for managing risks across AI design, development, use and evaluation. Its status page says AI RMF 1.0 is being revised and notes a concept note released April 7, 2026, for a critical-infrastructure profile. The framework can help organize risk work, but it is not a provider certification, a security guarantee or a substitute for your organization’s own procurement, privacy, security and legal decisions.
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