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Ask an AI-platform vendor questions in seven areas before signing: the business problem and measurable outcome, data use and rights, security and resilience, model performance, governance, integration and portability, and the complete cost. Then test the shortlisted service on representative work with documented acceptance criteria. A polished demo is not evidence that the platform is suitable for your data, risk level, or workflow.
1. Define the problem before discussing products
Start with a written problem statement, not a list of attractive features. NIST procurement guidance recommends defining the problem or opportunity, the expected public or organizational benefit, and an initial risk and impact assessment before procurement. The NIST Guidelines for AI Procurement also allows room for iteration: a useful requirement may become clearer after a controlled pilot.
Questions for the business sponsor
- What specific task or workflow will the platform support, and what work is outside its intended use?
- Which measurable outcome would justify adoption: shorter cycle time, fewer errors, better service capacity, or another defined result?
- What non-AI options, including process redesign or conventional software, should be compared?
- Which employees, customers, patients, citizens, or other people could be affected by an error?
- What is the consequence of a wrong, delayed, biased, unsafe, or confidential output?
Record the baseline before a pilot. For example, measure current handling time, rework, escalation rate, accuracy, and complaints for the workflow you want to change. Define a target and a minimum acceptable result. “Improve productivity” is not an acceptance criterion; “reduce first-draft preparation time by 30% without increasing review defects” is testable.
Identify the intended users, their skill levels, languages, accessibility needs, and the decisions they may make from an output. A tool that is appropriate for drafting internal summaries may be inappropriate for making an eligibility, employment, credit, medical, or safety decision without additional controls.
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2. Establish exactly what happens to data
Data terms should be a primary procurement issue. Ask for written answers covering prompts, uploaded files, connected records, generated outputs, telemetry, backups, logs, and data sent to subprocessors. NIST’s AI Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, published July 26, 2024) warns that third-party generative-AI integrations can create intellectual-property, privacy, and information-security risks.
Data-use questions
- What prompts, files, records, metadata, and outputs leave the company’s environment?
- Are customer inputs, outputs, or usage data used to train, fine-tune, evaluate, or improve a model? Can the company opt out, and is that restriction binding in the contract rather than merely a dashboard setting?
- Where are data processed and stored, including backups and disaster-recovery copies?
- What are the retention periods for content, logs, abuse-monitoring records, and deleted accounts?
- Who at the provider and at each subprocessor can access content, under what approval and need-to-know controls?
- How are deletion, export, legal holds, and requests for correction handled?
- What protections apply to confidential, personal, regulated, or third-party information, and what data must never be entered?
Ask the provider to map every data flow: user interface, API, retrieval system, model host, moderation service, support channel, analytics system, and subprocessor. Confirm whether a customer can disable human review of prompts or outputs for service improvement, and whether support personnel can see content during troubleshooting.
Privacy, training-data, and intellectual-property questions
- What does the provider disclose about the provenance and licensing of training data?
- Who owns customer inputs and outputs, and what license does the provider receive to operate the service?
- What happens if an output resembles protected third-party material?
- What indemnities, exclusions, usage restrictions, and customer obligations apply to intellectual-property claims?
- Can the provider identify content generated with external models, datasets, or software components?
Have privacy, records-management, and legal teams classify intended inputs before the pilot. A contract promise not to train on content does not make an unrestricted upload safe if the platform exposes that content to unnecessary administrators, subprocessors, or connected applications.
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3. Verify security and service resilience
Security claims must be tied to the specific service, edition, region, and architecture you will buy. Ask for current evidence and its scope; a report covering a hosting layer may not cover model behavior, customer configuration, or an integrated application.
Security-control questions
- How are administrators and end users authenticated? Are single sign-on, multifactor authentication, role-based access, and automated offboarding supported?
- What encryption protects data in transit and at rest, and who controls the keys?
- What audit logs capture prompts, outputs, administrative actions, connector access, and policy changes? Can logs be exported to your monitoring system?
- How are vulnerabilities found, prioritized, patched, and disclosed? Is a software bill of materials available for relevant components?
- How are tenants isolated, and how are secrets, tokens, and connector credentials protected?
- What incident-response process applies to unauthorized access, harmful outputs, model compromise, or a subprocessor breach?
Independent assurance and incident terms
Request the applicable independent security assessments, attestations, penetration-test summaries, and certifications, together with the boundaries, test dates, exceptions, and remediation status. NIST identifies service-level agreements, software bills of materials, and attestation reports as possible third-party risk controls; it does not say that any one report proves an AI system is trustworthy.
- How soon will the provider notify you of a breach or material service incident?
- What information will the notice contain, and how will updates be delivered?
- What uptime, support response, maintenance-window, and service-credit commitments apply?
- What backup frequency, recovery-point objective, recovery-time objective, and regional-failover commitments apply?
- What is the documented behavior if the model, connector, or entire platform is unavailable?
Test degraded-mode procedures before production. Decide whether staff can work manually, use a preapproved alternate system, or pause a process when the platform is unavailable. Record who can invoke that fallback and how queued work is reconciled afterward.
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4. Evaluate the model in your context
Vendor benchmarks and demonstrations are not substitutes for task-level evidence. NIST recommends robust, iterative, documented testing, evaluation, validation, and verification (TEVV) using representative AI actors and data. The NIST AI Risk Management Framework FAQs describe trustworthiness characteristics including validity and reliability, safety, security and resilience, transparency, privacy enhancement, and harmful-bias management.
Model and change questions
- Which underlying models, versions, retrieval components, and safety filters are used?
- Can the provider substitute, fine-tune, or update a model without notice? Which changes trigger notice, a new test, or your approval?
- What known limitations, failure modes, excluded uses, language gaps, and input-quality requirements are documented?
- How are prompts, system instructions, grounding sources, and output filters configured and versioned?
- What performance evidence exists for tasks, languages, user groups, and edge cases like misspellings, incomplete records, or adversarial inputs?
Build a representative test set
Use a controlled sample that reflects real volume, formats, languages, exceptions, and difficult cases. Remove or protect personal and confidential information, or use a segregated environment with written handling rules. Keep a human-verified answer or decision standard for each test item.
- Define acceptance measures such as factual accuracy, omission rate, refusal quality, latency, cost per task, and reviewer correction time.
- Run the same task set for each candidate under comparable settings and permissions.
- Record successful outputs, unsafe or biased outputs, hallucinations, privacy leaks, and failures to follow instructions.
- Repeat tests after configuration or model changes and document the version, date, data conditions, and reviewer.
- Set stop conditions: a critical safety, privacy, or legal failure should block expansion even if average accuracy looks good.
Decide which outputs are drafts, recommendations, or automated actions. Require human review, approval, and an override path before an output can affect a person, release confidential information, change a record, spend money, or trigger a safety-sensitive action.
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5. Assign governance and operational ownership
Adoption creates continuing work; it is not a one-time procurement checkbox. NIST’s generative-AI profile identifies acceptable-use policies, education, impact assessments, incident response, monitoring, and decommissioning as relevant practices.
Ownership questions
- Who is the accountable business owner, technical system owner, data owner, security approver, and legal approver?
- Which uses are permitted, restricted, or prohibited?
- What training will users receive on prompt handling, verification, privacy, copyright, and reporting problems?
- Which actions require human review, documented approval, or the ability to override the system?
- Who monitors quality, bias, complaints, security events, spend, and material model changes?
- Who can pause, restrict, roll back, or decommission the service, and what records must be preserved?
Create an operating record with the approved purpose, data classes, model and configuration versions, test results, user groups, review requirements, incidents, exceptions, and change approvals. Define a review cadence based on risk and usage rather than waiting for a contract renewal.
Monitoring and incident response
Monitor both technical and business signals: error and correction rates, drift in input data, refusal patterns, latency, cost, access anomalies, complaints, and near misses. Give users a simple route to report an incorrect or harmful output. Your response plan should specify containment, evidence preservation, affected-person notification where required, root-cause analysis, provider escalation, and criteria for resuming service.
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6. Check integration, permissions, contracts, and exit
Integration and access
- Which business systems, data sources, identity tools, and workflows must connect?
- What permissions will the platform receive, and can access be limited by role, field, record, tenant, or purpose?
- Are APIs, webhooks, rate limits, test environments, versioning policies, and administrative controls documented?
- Can logs, audit records, prompts, outputs, configurations, and evaluation results be exported in usable formats?
Use least privilege for every connector. Start with read-only access, a narrow dataset, and a separate test tenant where possible. Treat a system that can write records, send messages, or execute code as a higher-risk deployment than one that only drafts text.
Contract and service questions
- What service levels, support channels, response times, maintenance notices, and change-notice periods apply?
- Which terms govern data use, confidentiality, subprocessors, security incidents, audits, liability, indemnity, and applicable law?
- Can the provider suspend or terminate access for policy, abuse, nonpayment, or model changes, and how much notice is provided?
- What data, prompts, outputs, system instructions, fine-tuning artifacts, configurations, and logs can you recover at termination?
- How will deletion be verified, including backups and subprocessors?
Calculate the full cost
Model realistic usage, not only the advertised seat price. Include implementation, integration, data preparation, custom development, training, security review, legal work, evaluation, human review, monitoring, support, storage, overage, and exit costs. Test low, expected, and peak-volume scenarios, and identify which charges recur or change when users, tokens, documents, connectors, or model versions increase.
7. Compare candidates with one shared scorecard
Apply the same task set, data conditions, review method, and cost scenarios to every serious candidate. The dimensions below synthesize NIST procurement and risk guidance; they are not an official NIST weighting formula. Set weights according to your risk tolerance and the consequence of error.
| Dimension | Evidence to collect | Decision question |
|---|---|---|
| Business fit | Problem statement, baseline, target outcome, affected people | Does this solve a defined need better than non-AI options? |
| Measured performance | Representative test results, failure log, version and test conditions | Is performance acceptable for the actual workflow and edge cases? |
| Data use and privacy | Data-flow map, retention and deletion terms, training-use commitment, subprocessor list | Can the company lawfully and safely provide the intended data? |
| Security and resilience | Control description, independent assurance scope, incident terms, recovery commitments | Are controls and continuity proportionate to the impact of failure? |
| Model and third-party transparency | Model/version policy, limitations, provenance disclosures, dependency inventory | Can reviewers understand and manage external dependencies and changes? |
| Governance | Acceptable-use policy, training, human-review rules, monitoring and decommission plan | Is someone accountable throughout the lifecycle? |
| Integration and exit | API and permission design, export formats, portability and termination procedures | Can the service be controlled, replaced, or shut down without unacceptable disruption? |
| Legal and contract fit | Privacy, confidentiality, liability, indemnity, audit, governing-law and regulatory terms | Do written terms match the company’s obligations and risk appetite? |
| Full cost | Scenario-based total-cost model for implementation, operation, oversight, and exit | Is the expected value positive at realistic usage? |
Keep evidence beside each score and mark unknowns explicitly. A low score caused by missing documentation is different from a measured failure, but both should block approval until resolved or accepted by the named risk owner.
8. Use a gated pilot before production
A pilot should answer a decision question, not generate a favorable demo. Define scope, users, data, duration, owner, budget, acceptance criteria, and stop conditions in writing.
- Prepare: approve the use case, classify data, configure least-privilege access, establish logging, and train pilot users.
- Test: run representative tasks, including normal cases, edge cases, adversarial prompts, and deliberately difficult inputs.
- Review: have qualified people verify outputs, record corrections and harms, and compare results with the baseline and other candidates.
- Decide: expand, redesign, restrict, or stop based on the predefined criteria and documented residual risk.
- Operate: if approved, publish the acceptable-use rules, maintain monitoring, schedule reassessment, and preserve a rollback and exit path.
For higher-impact uses, require sign-off from business, privacy, security, legal, and data owners before production. The NIST AI RMF Core provides a voluntary structure for defining context, eliciting requirements from relevant AI actors, and addressing legal and third-party risks; it is guidance, not a certification or guarantee. Applicable laws and contract duties still depend on your jurisdiction, sector, data, and use case.
Quick Recap
Red flags that should pause adoption
- The vendor cannot state whether customer content is used for training or cannot put the answer in a binding term.
- It refuses to identify subprocessors, processing locations, retention periods, or deletion procedures.
- Security reports are outdated, have unclear scope, or exclude the AI service and its integrations.
- Model substitutions, material changes, or new data uses can occur without notice or a retest opportunity.
- Performance claims rely only on generic benchmarks, cherry-picked examples, or averages without failure analysis.
- The platform requires broad write access, offers no exportable logs, or provides no practical way to pause and recover data.
- No accountable owner, human-review rule, incident process, or decommission plan exists.
- The total price excludes implementation, oversight, evaluation, support, or usage-based charges.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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