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Evaluate an AI startup against a specific business use, not a polished demo or a generic model score. Define the task, affected people, data, deployment conditions and consequences of error; require evidence from representative workflows; investigate the supplier and its dependencies; contract for data, security, continuity and change control; then run a bounded pilot with buyer-owned acceptance criteria. The result should be an explicit decision to pilot, adopt with conditions, or reject—with monitoring and an exit route documented before production use.
1. Start with the use case, not the vendor pitch
An AI system can be acceptable for drafting internal notes and unsuitable for deciding who receives a loan, a benefit or a medical appointment. The first diligence document should therefore describe the proposed use in operational terms.
Write a one-page use-case statement
- Task: State exactly what the system will generate, classify, recommend, retrieve or automate.
- Users: Identify operators, reviewers, administrators and people who may rely on the output indirectly.
- Affected people: Include customers, employees, applicants, patients, students or members of the public who do not use the interface themselves.
- Data: List every input, including prompts, uploaded files, telemetry, feedback, account data and connected-system records.
- Deployment: Record the region, cloud or on-premises setting, integrations, identity model, human-review points and expected volume.
- Consequences: Describe what happens when an answer is wrong, unavailable, biased, late, leaked or silently changed.
- Benefit: Define the business outcome that must improve, such as cycle time, resolution quality or operating cost, and how it will be measured against a non-AI alternative.
NIST’s Artificial Intelligence Risk Management Framework (AI RMF 1.0, released January 26, 2023) says deployment decisions should reflect contextual assessment of trustworthiness, risks, impacts, costs and benefits, informed by a broad set of interested parties. That makes the use-case statement a governance artifact, not just a product requirement.
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Classify the consequences before choosing controls
Ask whether a human can detect and correct an error before it affects someone, whether the output triggers an irreversible action, and whether the data is confidential, regulated or safety-sensitive. A low-consequence assistant may justify a short pilot and sampling review. A system that influences eligibility, employment, health, finance, safety or access to essential services needs stronger evidence, tighter human control, legal review and a tested fallback. Do not label a system “low risk” merely because the vendor calls it an assistant.
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2. Set buyer-owned acceptance criteria
The startup should explain its tests, but the buyer must decide what is good enough in the intended environment. Ask for evaluation methods, data characteristics, limitations, reliability results, generalization evidence and how performance has changed across releases. NIST warns that inaccurate, unreliable or poorly generalized systems can increase risk.
Turn the use case into tests
- Collect a representative, lawfully usable sample covering normal cases, edge cases, languages, formats and difficult inputs that occur in production.
- Define the expected output, allowable error, escalation rule and reviewer action for each test family.
- Separate development examples from a holdout set that the startup cannot tune against during the pilot.
- Test the complete workflow, including authentication, retrieval, tools, human review, exports, logging and downstream actions—not only the model response.
- Record false positives, false negatives, omissions, hallucinated facts, unsafe instructions, latency, outages and reviewer workload.
- Set a release gate: what must pass, what requires mitigation and what causes an immediate stop.
A benchmark result from another industry, language or model version is context, not proof of production suitability. A polished demonstration is also not evidence that the system will generalize to your records, users or operating constraints.
Ask how results can be reproduced
Request the model or service version, test dates, data scope, sampling method, prompts or configurations, human-rating instructions and confidence limitations. Determine whether the vendor can preserve an evaluation snapshot when it updates the model. If the startup cannot explain what was measured or cannot provide enough detail to repeat a test, treat that opacity as a deployment risk rather than filling the gap with marketing claims.
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3. Assess the startup as a supplier
Model quality is only one part of vendor risk. NIST’s Cybersecurity Supply Chain Risk Management: Due Diligence Assessment Quick-Start Guide (SP 1326, July 2026) defines due diligence as “the investigative process of researching all available, pertinent information about a given supplier or product so that informed decisions can be made on new acquisitions or existing systems.” The guide is scoped to information and communications technology suppliers and organizes inquiry around five areas.
Foreign ownership, control or influence
Identify the legal entity that signs the contract, its parent and controlling interests, operating jurisdictions and any government or other influence that could affect access, disclosure or service continuity. Ask where support personnel and administrators are located and which laws could compel access to your data. This is a factual risk assessment, not a conclusion based on nationality alone.
Provenance
Map where the product, model weights, training or retrieval data, software components and hosted services came from. Ask how the startup tracks licenses, consent, restrictions and modifications. For generated content, clarify whether outputs can be used by the buyer and what restrictions apply to inputs, outputs and fine-tuning material.
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Resilience
Examine the startup’s ability to keep operating through an outage, provider failure, regional disruption, staffing loss or sudden demand. Request service objectives, backup and recovery arrangements, dependency maps and a communication path for incidents. A small company may be viable, but its resilience assumptions must be explicit.
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Ask how the company manages identity, privileged access, secrets, vulnerability remediation, secure development, logging, encryption, employee access and incident response. Request independent control evidence where appropriate and verify that the scope covers the service you will use rather than a different corporate environment.
Supply-chain tiers
Obtain a current list of material subprocessors, foundation-model providers, cloud regions, external APIs, data suppliers and other dependencies. Identify which party controls each layer, what happens when it changes, and whether the startup can notify you before a material substitution. A contract with the startup does not eliminate risk at deeper tiers.
4. Map data, rights and downstream access
Before sending a confidential prompt or file, draw the data path from collection to deletion. Include every system that receives, stores, indexes, logs, reviews or derives information from the content.
Questions the data map must answer
- What categories of data enter prompts, uploads, context windows, logs, support tickets and analytics?
- Where is each category processed and stored, and how long is it retained?
- Which employees, contractors, subprocessors and model providers can access it?
- Can the vendor use content for training, evaluation, product improvement, abuse monitoring or human review?
- Can you disable those uses, and does the restriction apply to backups, derived data and subprocessor copies?
- How are deletion, correction, export and account closure requests executed and verified?
- Who owns inputs, outputs, custom instructions, retrieval indexes, fine-tunes and feedback?
- What geographic transfers, government-access processes and breach-notification duties apply?
The Federal Trade Commission’s vendor-security guidance advises buyers to specify how vendors may use, share, sell, retain and delete data, and to verify that promised controls exist. NIST’s Generative AI Profile (AI 600-1, 2024) recommends contracts that define content ownership, usage rights, quality, security requirements and provenance expectations. The profile is guidance for generative AI, not a universal legal requirement for every AI product.
Put the answers in enforceable terms
Marketing language such as “we never train on your data” is insufficient unless the contract defines the data covered, permitted exceptions, subprocessors, retention period, deletion evidence and remedies. Attach the data-flow and subprocessor lists to the agreement, require notice and approval for material changes where feasible, and make conflicting online terms subordinate to the negotiated contract. Have privacy and sector counsel test the arrangement against the jurisdictions in which you operate.
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5. Test security, resilience and incident readiness
Security diligence should cover both the AI-specific attack surface and ordinary supplier controls. Ask how the service handles prompt injection, malicious files, data exfiltration through connected tools, model or embedding poisoning, insecure outputs and cross-tenant exposure. Also inspect conventional controls for identity, networks, endpoints, code, backups and personnel.
Evidence to request
- Independent assessment or audit reports whose scope, period and exceptions cover the proposed service.
- Architecture and trust-boundary diagrams showing tenant isolation, administrative paths and data flows.
- Vulnerability disclosure, patching and penetration-test processes, including severity targets.
- Access-control design, key management, encryption details and audit-log availability.
- Incident definitions, notification deadlines, investigation cooperation and customer communications.
- Business-continuity, disaster-recovery and restoration tests, with dependencies and assumptions.
FTC guidance cautions, “Don’t just take their word for it.” Verify claims through contracts, evidence, demonstrations of controls and references appropriate to the risk. A certification or report is evidence about a defined scope and period; it is not proof that every weakness is absent.
Design for failure before launch
For a high-risk third-party AI system, document what happens if the supplier is unavailable, loses a dependency, suffers a breach or materially changes the model. NIST’s Generative AI Profile calls for contingency processes and fallback planning. Decide whether the fallback is a human workflow, a previous approved version, a rules-based process, another supplier or a safe shutdown. Test the route, including access to records and the ability to export configuration and data.
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6. Make the contract support verification and exit
Procurement should translate the risk analysis into obligations that remain useful after signature. At minimum, negotiate provisions for:
- Scope and service description: Identify model or service versions, regions, integrations, support levels and material dependencies.
- Data: Define permitted processing, ownership, retention, deletion evidence, return or export, subprocessor access and training restrictions.
- Security: Specify control requirements, audit or assessment rights, vulnerability handling and cooperation with investigations.
- Incidents: Set notification channels and timing, information to be provided, preservation duties and remediation cooperation.
- Changes: Require notice of material model, data, subprocessor, hosting or policy changes and provide a reassessment or termination right when risk changes.
- Performance: Record buyer acceptance tests, service objectives, known limitations and remedies for persistent failure.
- Continuity: Define recovery commitments, customer communications and the agreed fallback process.
- Exit: Provide termination triggers, data and configuration export, deletion certification, transition assistance and a practical timetable.
NIST’s AI RMF notes that third-party technologies may be complex or opaque and that supplier risk tolerances may not align with those of the deploying organization. Contractual verification rights and continuing control evaluation help close that gap; they do not replace technical testing.
7. Run a bounded pilot with real safeguards
A pilot is a controlled experiment, not a free production rollout. Limit data, users, permissions, integrations and duration. Prohibit high-consequence automated actions until the acceptance criteria and human controls pass.
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- Prepare governance: Name a business owner, technical owner, security and privacy reviewers, escalation contacts and the person authorized to stop the pilot.
- Prepare data: Minimize fields, remove unnecessary identifiers, document lawful use and create representative test cases. Do not use production secrets merely to make a demo realistic.
- Configure controls: Use least privilege, approved connectors, logging, output labeling, rate limits and human approval for consequential actions.
- Measure: Compare results with the current process and a reasonable alternative. Record quality, error types, latency, cost, review time, user impacts and incidents.
- Challenge the system: Include ambiguous requests, adversarial inputs, unusual formats, missing data, language variation, dependency outages and model updates.
- Review with affected users: Gather feedback from operators and people who bear the consequences of errors, not only executives or the vendor’s champions.
- Decide: Document which criteria passed, which risks remain, who accepted them and what additional controls are prerequisites for expansion.
Keep pilot artifacts: prompts or configurations, model versions, test data description, results, incidents, reviewer decisions and change history. Without them, a later decision cannot distinguish real improvement from a changed sample or model.
8. Monitor after adoption and control change
Approval is the start of oversight. Assign an owner and a review cadence proportionate to the consequences of the use case.
Monitor four kinds of change
- Performance drift: Track error patterns, coverage, reviewer overrides, latency and quality by relevant user or data segment.
- System change: Record model, prompt, retrieval, policy, hosting, subprocessor and integration changes. Re-run critical acceptance tests after material changes.
- Threat and control change: Reassess vulnerabilities, incidents, dependency health and whether vendor safeguards still match the threat environment. FTC guidance emphasizes keeping vendor controls current as threats change.
- Impact change: Watch complaints, disparate outcomes, privacy events, unsafe outputs, workarounds and new uses that were not in the approval scope.
Define thresholds that trigger human review, rollback, suspension or shutdown. Keep a tested copy of the fallback procedure and ensure staff know how to invoke it. NIST’s AI RMF Playbook organizes voluntary suggested actions under Govern, Map, Measure and Manage; those functions can structure recurring reviews without turning the framework into a certification claim.
9. Compare startups by trade-offs, not one score
A single “AI vendor score” hides decisions that cannot be averaged safely. Compare each option on the same use-case evidence and record the trade-off behind the conclusion.
| Decision axis | Evidence to compare | Question for the decision owner |
|---|---|---|
| Use-case performance | Representative tests, limitations, reliability, generalization and change history | Does this option meet the acceptance criteria in our workflow? |
| Data protection and rights | Data map, retention, training permissions, ownership, deletion and subprocessor terms | Can we lawfully and operationally control the information we submit? |
| Security and resilience | Control evidence, architecture, incidents, recovery and dependency information | What happens during compromise, outage or provider failure? |
| Transparency and evaluability | Versioning, test reproducibility, logs, explanations of limits and notice of changes | Can we detect and investigate a bad result or material change? |
| Supply-chain dependence | Ownership, provenance, foundation models, APIs, data sources and tier changes | Which parties can alter or interrupt the service beyond our direct contract? |
| Integration and support | Identity, APIs, support coverage, implementation burden and incident contacts | Can our teams operate this safely at the required scale? |
| Exit options | Export formats, transition help, deletion proof, fallback and termination rights | Can we leave without losing essential records or control? |
| Total cost and benefit | Subscription and usage costs, review labor, controls, integration, downtime and measured benefit | Is the durable benefit worth the risk and operating burden versus alternatives? |
NIST cautions that trustworthiness characteristics can trade off. For example, a privacy technique may affect accuracy under some conditions. Record the consequence of each trade-off for this use case instead of assuming that the highest score on one dimension wins.
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Approve a pilot
Choose a pilot when the use case is bounded, data handling is acceptable for controlled testing, the supplier provides enough evidence to test meaningful hypotheses, and a human-controlled fallback exists. State the success criteria and stop conditions in the approval record.
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Adopt with conditions
Proceed to limited production only when acceptance tests pass and unresolved risks have named owners, deadlines and compensating controls. Conditions might include human approval, restricted data classes, a fixed model version, additional logging, geographic limits, a shorter review cycle or a prohibition on certain automated actions. Expansion should require a new decision, not happen automatically because the pilot was popular.
Reject or pause
Reject or pause when the startup cannot establish who handles the data, refuses necessary verification, has unacceptable rights or retention terms, cannot explain material dependencies, fails representative tests, lacks a credible incident and continuity plan, or cannot support a safe exit. A low price or impressive demo does not offset an unmanageable failure mode.
Common diligence mistakes and how to correct them
- Starting with a generic vendor questionnaire: Replace it with the use-case statement and risk consequences first.
- Accepting benchmark numbers without conditions: Demand test scope, version, data characteristics and reproducible buyer tests.
- Treating “no training” as a complete privacy answer: Map logs, support access, subprocessors, retention, derived data and deletion.
- Reviewing only the startup: Trace foundation models, cloud services, APIs, datasets and other supply-chain tiers.
- Assuming a certification proves safety: Check its scope, date, exceptions and relevance to the deployed service.
- Making the pilot production by default: Set a time limit, restricted permissions, acceptance gates and an explicit expansion decision.
- Ignoring model and policy changes: Require notice, preserve test sets and re-run critical evaluations after material changes.
- Leaving exit to a crisis: Export what you need, test the fallback and negotiate deletion and transition terms before launch.
A concise approval checklist
- The task, users, affected people, data, setting, consequences and expected benefit are documented.
- Buyer-owned acceptance tests use representative data and include limitations and failure modes.
- The legal entity, ownership or influence, provenance, resilience, cybersecurity and supply-chain tiers are understood.
- Data flows, access, training use, retention, deletion, ownership and downstream providers are contractually defined.
- Security evidence, incident duties, vulnerability handling and continuity commitments match the use-case risk.
- Material changes require notice or reassessment, and monitoring owners and thresholds are assigned.
- A human review, rollback, shutdown or alternative process has been documented and tested.
- The measured benefit outweighs risk, cost and operating burden compared with a credible alternative.
These checks improve the quality of a decision; they do not prove that an AI system is safe, fair, secure or legally compliant in every context. Use applicable sector rules, contracts and professional advice for the jurisdictions in which the system will operate.
Frequently Asked Questions
How do you actually assess AI vendor risk?
Start with the intended use and its consequences, then test the vendor’s system on representative workflows while investigating data rights, security, resilience, ownership, dependencies and exit terms. Make the decision conditional on buyer-owned acceptance criteria and a documented fallback, rather than relying on a demo, benchmark or questionnaire score.
What should an AI governance assessment include before adoption?
It should include the use-case statement, affected people, data and processing map, performance and limitation evidence, supplier and supply-chain due diligence, security and incident commitments, contractual rights, pilot results, monitoring duties, change controls and rollback or shutdown procedures.
Is the NIST AI Risk Management Framework mandatory?
NIST AI RMF 1.0 and its Playbook are voluntary resources. They can organize governance, mapping, measurement and management activities, but they do not replace laws, sector requirements, contracts or a context-specific risk decision.
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