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Before a business relies on a hosted AI model, treat it as a third-party supplier decision—not a software trial. Define the workflow and acceptable residual risk, verify data use and retention for the exact endpoint and plan, review binding security and service terms, test representative tasks and failure cases, then deploy with monitoring and a practical fallback.
1. Define the workflow and the risk you are willing to accept
Start with the business job, not a model’s general reputation. Write down what the system will do, who will use it, whose interests may be affected, and what decisions people might make from its output. “Use AI for customer service” is too broad for due diligence; “draft replies to authenticated customers, with an employee approving every message” is testable.
Describe the operating context
- List the users, administrators, connected applications and downstream recipients.
- Classify every data type that could enter prompts, files, retrieval indexes, embeddings, feedback fields or connected tools.
- Identify personal, confidential, regulated, customer, financial, health, security-sensitive and trade-secret information.
- Record whether an error could cause financial loss, discrimination, unsafe advice, a legal breach, reputational damage or denial of service.
- Set the required human review, approval and escalation points before comparing providers.
Set an explicit residual-risk decision
Decide what level of error, delay, disclosure and unavailability the business can tolerate. Assign an accountable risk owner and define conditions that require a human-only process. A low-impact internal drafting tool may tolerate occasional correction; an automated eligibility, safety or customer-remedy workflow may not.
NIST’s AI Risk Management Framework organizes this work around Govern, Map, Measure and Manage. It is voluntary guidance, not a certification or a pass/fail test. NIST says the framework is intended to help developers, users and evaluators “better manage AI risks which could affect individuals, organizations, society, or the environment.”
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2. Treat the hosted model as a third-party supplier
A hosted integration creates supplier, privacy, intellectual-property and information-security dependencies. NIST’s Generative AI Profile (NIST AI 600-1, published July 26, 2024) specifically addresses third-party generative-AI integrations and procurement due diligence. Apply your normal supplier-risk process alongside model evaluation: ownership, access, subcontracting, incident handling, continuity, audit evidence and exit planning.
Identify the exact service boundary
Record the provider, product, account type, region, model or endpoint, enabled features, connected tools, administrators and contract version. Enterprise claims on a marketing page may not apply to an API endpoint, consumer workspace, newly released model, particular geography or special-control program. Your evidence should match the configuration you will actually operate.
3. Verify data handling instead of relying on a training promise
“Not used for training” answers only one question. It does not by itself tell you whether content is retained, copied into application state, reviewed for abuse, processed by subcontractors or transferred across borders.
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- Are prompts, outputs, uploaded files, embeddings, feedback, usage data or telemetry used to train or improve any model? Is the setting opt-in, opt-out, plan-dependent or endpoint-dependent?
- What content can enter abuse, safety or quality-monitoring logs? Which personnel or vendors can access those logs, for what purpose and for how long?
- Which features create persistent chat history, files, project state, fine-tuning data, cached context or other stored content?
- Can your organization configure retention and deletion? Are zero-retention controls available to this account, region, endpoint and every feature in scope?
- What exceptions permit retention or access for legal obligations, security investigations, abuse prevention or safety review?
- Which entities process the content, where is it stored and processed, and what transfer, residency and government-access terms apply?
- What confidentiality, deletion, breach-notification, audit and data-processing commitments are in the signed agreement?
Understand application state and logs separately
OpenAI’s business and API documentation, accessed October 13, 2026, states that business and API data is not used for training by default and describes encryption and retention controls, subject to eligibility and other qualifications. Its API data-controls documentation distinguishes abuse-monitoring logs from application state: default abuse logs may include customer content and may be retained for up to 30 days, with exceptions. Endpoint-specific rules and eligibility for zero-data-retention controls therefore matter.
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Anthropic’s commercial-product retention guidance, also current as of October 13, 2026, describes deletion of API inputs and outputs within 30 days by default, with stated exceptions. Saved conversations in its work products have a different retention model. Do not transfer either example to another product, plan or feature; verify the current documentation and the terms governing your organization.
Map the complete data flow
Draw the path from user input to model endpoint, safety systems, storage, analytics, support access, subprocessors and deletion. Include data returned to your own systems and any copies created by prompt gateways, observability tools, vector databases or backups. Confirm whether your deletion request reaches replicas and backups, and what evidence you receive when deletion is complete.
4. Review contracts and assurance evidence
Request the documents that govern the service, not only public assurances. The UK Information Commissioner’s Office advises seeking guarantees about information, coding or models, requesting comprehensive documentation, and reviewing contracts and relevant policies. Its legal applicability depends on your jurisdiction and role, but the procurement principle is broadly useful.
Documents to collect
- Master service agreement, acceptable-use terms and product-specific terms.
- Data-processing agreement, transfer mechanisms and confidentiality provisions.
- Current security overview, architecture and access-control documentation.
- Independent assurance reports or attestations available to customers.
- Subprocessor list, change-notification process and data-location details.
- Incident-response policy, notification deadlines and customer communication process.
- Service-level agreement, support policy, escalation contacts and remedies.
- Technical documentation for authentication, rate limits, versioning, logging and deletion.
Separate public claims from binding commitments
For every important promise, record whether it appears in marketing material, technical documentation, a policy that can change unilaterally or the signed agreement. Only the governing contract, together with incorporated documents, gives you an enforceable baseline. Note precedence rules when documents conflict and preserve the versions approved by legal and security teams.
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5. Examine reliability, support and suspension rights
A headline availability percentage is not enough. An SLA may measure only a particular API, exclude maintenance, ignore throttling or define service credits as the sole remedy.
Clarify the service commitment
- What service, region and measurement window does availability cover?
- How are planned maintenance, provider-caused dependency failures, rate limits and force-majeure events treated?
- Are latency, throughput, quota exhaustion and model-quality degradation covered, or only endpoint reachability?
- What support tier, response time, escalation path and incident-communication cadence apply?
- What remedies are available, and are credits your exclusive remedy?
- Can the provider suspend access for suspected abuse, security events, nonpayment or policy changes, and how much notice is given?
NIST’s Generative AI Profile identifies service-level agreements as one possible tool for third-party transparency and risk management. The exact protection comes from your service agreement, not from NIST or a provider’s general uptime statement.
6. Test the task, not the brand
Run a documented evaluation using representative work before granting operational authority. A polished demo can hide failures caused by your language, data, user population, integrations or edge cases.
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- Sample real inputs, after applying appropriate de-identification and approval.
- Include normal cases, ambiguous requests, missing fields, long documents, adversarial prompts, sensitive data and known failure scenarios.
- Define pass criteria for factual accuracy, format, completeness, consistency, refusal behavior and escalation.
- Include the users and languages that will actually operate the workflow.
- Specify when a human must review, correct, approve or reject an output.
Measure operational behavior
- Accuracy and consistency on the business task, not a generic benchmark.
- Hallucination, privacy leakage, discriminatory or harmful output where relevant.
- Prompt-injection and tool-misuse paths, including malicious or contaminated source content.
- Latency, concurrency, rate limits, timeout behavior and cost at expected volume.
- Failure behavior during provider errors, partial outages, quota exhaustion and model unavailability.
- Differences between model versions, regions, SDKs and safety settings you may use.
Keep the test prompts, scoring rubric, results, known limitations, reviewer names and approval of residual risk. NIST’s profile calls for robust, iterative and documented testing, evaluation, validation and verification informed by representative AI actors. Re-run material tests after model, endpoint, safety-policy, pricing or integration changes.
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7. Design controls for deployment
Limit access and data
- Use least-privilege service accounts, separate environments and short-lived credentials.
- Permit only approved data classes and block sensitive fields where the workflow does not require them.
- Restrict connected tools and write actions; require confirmation for irreversible changes.
- Train users on prohibited inputs, verification duties, prompt-injection signals and incident reporting.
- Log access and material outputs in a way that supports investigation without creating an uncontrolled new data store.
Define monitoring and escalation
Assign an owner for quality, security, privacy, cost and vendor changes. Monitor error samples, refusal rates, latency, quotas, unusual data access, user complaints and human overrides. Set thresholds that pause automation or route work to a manual queue. Define who contacts the provider, who informs affected parties and who preserves evidence after an incident.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Plan portability, fallback and exit before launch
Ask what happens if the model changes, the API is unavailable, pricing rises, the provider changes terms or your organization must leave. Export prompts, configuration, evaluation sets, output records, metadata and feedback in usable formats. Identify proprietary SDK calls, embedding dependencies, fine-tuning artifacts and workflow assumptions that make migration difficult.
Exit questions
- Can you retrieve all customer content and configuration needed to operate elsewhere?
- What notice applies to termination, price changes, model retirement and material terms changes?
- How quickly will access be disabled, and how will you obtain deletion confirmation?
- What alternate provider or local/manual process can handle critical work?
- What retraining, revalidation and user communication will migration require?
For workflows where interruption or unreliable output could cause material harm, keep a human or alternate-provider path tested and ready. A fallback that exists only on paper is not continuity planning.
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9. Compare candidate arrangements on common evidence
If you are considering multiple providers, deployment models or account types, score each on the same documented evidence rather than on brand familiarity.
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| Axis | Questions to compare |
|---|---|
| Data terms | Training or improvement use, content retention, application state, deletion, subprocessors, geography, special-control eligibility and exceptions. |
| Security and assurance | Access controls, encryption, incident response, independent assessments, technical documentation and customer audit evidence. |
| Reliability and support | Contract-defined availability, support response, escalation, remedies, maintenance exclusions and suspension conditions. |
| Task performance | Your representative-test results, failure modes, human-review burden, latency, limits and model-change process. |
| Operations and portability | Integration effort, logging, access management, fallback, export, migration and termination procedures. |
| Commercial fit | Total cost at anticipated use, predictable capacity, rate limits and terms governing price or product changes. |
There is no universal “best” hosted model. The defensible choice is the arrangement whose documented controls, tested behavior, cost and exit options fit your defined workflow and residual-risk decision.
10. Keep the decision current
Approval is not permanent. Schedule reviews based on risk and trigger an immediate reassessment after a model or endpoint change, new connected tool, data-class expansion, incident, material contract revision, price change or repeated evaluation failure. Maintain a decision record containing the workflow scope, data map, contract versions, test results, controls, owner, review date and shutdown criteria.
NIST’s AI RMF Playbook can help organize suggested actions under Govern, Map, Measure and Manage, but NIST explicitly says the Playbook is “neither a checklist nor set of steps to be followed in its entirety.” Use it to structure judgment, not to substitute for product-specific evidence, legal review or operational testing.
Quick Recap
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