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Before submitting sensitive information to an AI model aggregator, verify every party that may receive it—not just the aggregator. Check the selected model provider’s training, logging, retention, and deletion practices; confirm routing restrictions; and review any contract or regional-processing commitments that apply to your exact plan and endpoint. If you cannot verify a material safeguard, do not submit the data: redact it or use a synthetic example instead.
Why an aggregator requires more than one privacy check
An AI model aggregator provides a route to models from one or more providers. Depending on the service and your settings, your prompt may pass through the aggregator to a selected provider, or routing may choose a provider automatically. Connected tools or plugins may also receive information used in a request.
That creates separate points to verify. The aggregator’s privacy policy describes its own handling, but it does not automatically establish how a downstream model provider handles the prompt or response. Review the aggregator, the provider that will process the request, and any tool involved. When routing can change, establish which providers are eligible before sending sensitive material.
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Start by tracing the request as a data path: what you enter, what the aggregator receives, which model provider or providers may receive it, and whether an external tool is part of the workflow. Include attachments and metadata in that inventory, not just the visible prompt text. Then check the terms and settings at each relevant point. A claim about one recipient does not answer what another recipient may do.
Use this checklist before you send a prompt
- Map the recipients. Identify the aggregator, the model provider, and any connected tools that may receive prompt text, outputs, attachments, or metadata. Check whether automatic routing can change the provider.
- Check training and improvement separately. Determine whether the aggregator or provider may use prompts or outputs to train, fine-tune, evaluate, or improve models. If there is an opt-out, verify that it is available for your account and active for the request route you intend to use.
- Check logging and retention by data type. Look separately at prompts, responses, metadata, uploaded files, and temporary processing storage. Find the purposes and retention periods, including safety, abuse monitoring, security, billing, and legal needs.
- Understand deletion scope. Confirm what you can delete, how to request deletion, and whether the commitment extends to downstream providers, backups, caches, or replicated copies.
- Verify routing controls. Look for provider allowlists, data-use filters, retention filters, and per-request restrictions. Confirm the exact setting and whether it applies to both account-wide and individual requests.
- Review workplace and regional protections. For business or regulated information, check the applicable data-processing agreement, confidentiality commitments, subprocessors, and regional-processing terms. Confirm that they cover your plan, model, endpoint, and feature.
- Minimize what you disclose. If a key control is unclear or unavailable, do not send the sensitive content. Replace names, account numbers, and unique details with placeholders, or use a synthetic example that preserves the task without exposing real data.
For each check, look for evidence tied to the route you will actually use: current documentation, a visible account setting, a request-level option, or a contract that covers your account. A general privacy statement may not specify whether a control is on by default, whether it is opt-in, or whether it applies to all models and features. If the service offers separate individual and organization settings, verify the one that governs your use.
Keep the questions distinct. “Does the aggregator train on my prompt?” is not the same as “Does the provider train on it?” “Can I delete my chat?” is not the same as “Are downstream copies also deleted?” And “Is this request routed in-region?” does not necessarily tell you where every related storage, support, or tool operation occurs. Ask for specifics where the documentation is unclear.
Training, logging, and retention are different questions
Training and model improvement
A statement that a service does not train on your data may refer only to that service’s own models or operations. It may not describe the separate provider that receives a request. Check both parties’ terms for prompts and outputs, and distinguish model training from evaluation, fine-tuning, and other product-improvement uses.
Also verify the status of any opt-out rather than assuming it applies automatically. Determine whether it is account-wide or must be selected for an individual request, and whether it covers the particular provider or route. An aggregator may state its own policy while warning that providers have different practices; the receiving provider’s rules remain relevant.
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Logging and operational storage
Turning off prompt history or opting out of training does not necessarily mean that no data is stored. A service may retain information temporarily or for operational purposes such as abuse prevention, security, billing, or responding to legal demands. Determine which data is logged, why it is retained, and for how long; do not infer those answers from a single “no training” statement.
Ask whether the policy distinguishes prompt and response content from metadata, and whether it describes temporary processing storage separately from longer-term logs. Retention may vary by feature and recipient, so an answer about ordinary text chats may not settle how an uploaded file or a different endpoint is handled. Record the stated purpose and period for each category rather than treating “retention” as one setting.
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Attachments can follow different rules from ordinary text prompts. Check whether uploaded files persist in a separate storage system, what action deletes them, and whether deletion applies to downstream copies and backups. A deletion button in the user interface is useful, but its scope depends on the service’s stated commitments.
Look for separate terms on active systems, backups, caches, and replicated copies. If the documentation does not say whether deletion propagates to model providers, do not assume that it does. OpenRouter’s terms, for example, say uploaded files may be retained until deletion or account closure, subject to stated exceptions. That is a feature-specific detail to verify against current terms, not a universal retention period for aggregators.
Compare providers and routes on the questions that matter
| Check | What to establish |
|---|---|
| Recipients | Which aggregator, model provider, and connected tools can receive the input? Can routing change automatically? |
| Training and improvement | Can either the aggregator or provider train, fine-tune, evaluate, or improve models using prompts or outputs? Is an opt-out available and active? |
| Logging and retention | Are prompts, responses, metadata, or files stored? For what operational, safety, billing, or legal purposes, and for how long? |
| Deletion | Can you delete the data? Do the commitments cover backups, caches, and downstream providers? |
| Routing controls | Can the account or an individual request restrict eligible providers by data-use or retention policy? |
| Contract and location | Do the DPA, confidentiality, subprocessor, and regional-processing commitments fit the specific use case? |
Do not treat a control as verified merely because a vendor mentions it. Check the actual account setting or request option, the scope of the promise, and whether it applies to the model and feature you plan to use.
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When comparing routes, compare like with like. A provider allowlist can narrow which recipients are eligible, while a data-policy filter can narrow routes according to specified data-use or retention policies. Neither label alone tells you precisely what is enforced. Read the filter definition, confirm whether it is set at account level or on the individual request, and check whether free and paid routes or different model endpoints have different settings. Save the configuration or policy reference you relied on, especially for workplace use.
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OpenRouter is one example, not a description of all aggregators. Its Privacy Policy says it does not use inputs or outputs for its own model training, while also explaining that inputs are transmitted to model providers whose practices differ. The provider-logging documentation describes provider-specific data policies and routing filters. In practice, a user assessing a particular route must consider both OpenRouter’s handling and the receiving provider’s policy.
OpenRouter’s Terms of Service distinguish opt-in prompt storage from processing-related storage and state that retention varies by feature. They also say uploaded files may be retained until deletion or account closure, subject to stated exceptions. These distinctions are why a general statement about prompt handling is not enough: check the rule for the feature and data type you will actually use.
The provider-logging documentation also describes account-level and individual-request data-policy filtering. Such controls can narrow eligible providers, but confirm which policy the filter enforces and whether it applies to the specific request. OpenRouter documents EU and US in-region routing for Business and Enterprise plans through regional hostnames. Confirm that your plan, model, endpoint, and feature are covered; this vendor-specific documentation does not establish that every aggregator offers equivalent routing or that every part of a workflow is regionally processed.
OpenRouter’s Privacy Policy showed an update date of August 31, 2026. Policies and product settings can change, so consult the current policy and feature documentation when deciding whether a particular route is suitable.
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Extra checks for work or regulated information
For business use, a product setting is not a substitute for reviewing the terms that govern the organization’s relationship with the service. Check the applicable data-processing agreement, confidentiality language, subprocessors, and any commitments about data use and deletion. Verify that the documents apply to the plan and endpoint employees will actually use, not merely to a different enterprise offering.
- Confirm who in your organization is authorized to submit the information and which account or workspace settings apply.
- Check whether the provider list is fixed or may change through automatic routing.
- For regional processing, verify the exact data path and scope rather than assuming that a regional hostname covers all storage, support, or connected-tool processing.
- Ask your privacy, security, or legal team to assess jurisdiction-specific requirements for regulated or otherwise high-impact data.
For regional controls, check both eligibility and coverage: OpenRouter documents EU and US in-region routing for Business and Enterprise plans, but that does not establish coverage for every plan, model, endpoint, or feature. Verify the exact hostname and route in use, and ask whether the regional commitment applies to downstream processing as well as the aggregator’s own handling. Do not infer a universal regional guarantee from a vendor example.
The Federal Trade Commission’s Office of Technology describes sensitive and confidential business information as a salient risk when customers use model-as-a-service companies. In its January 2024 article, “AI Companies: Uphold Your Privacy and Confidentiality Commitments”, the FTC says companies should honor their data-use promises and warns that retaining or using consumer data for other purposes without clear and conspicuous notice and affirmative express consent can create legal risk. The FTC’s concise reminder is: “There is no AI exemption from the laws on the books.” This is U.S. regulator guidance, not a legal determination for every jurisdiction or data category.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When you should not submit the information
Do not send sensitive data when you cannot identify the likely recipients, cannot verify the relevant training or retention controls, or cannot establish that an organizational agreement covers the route. A service’s general privacy statement may not answer those specific questions.
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Instead, remove direct identifiers and details that could re-identify someone, or create a synthetic example. Preserve only the context needed for the task. For instance, ask for help drafting a response using “Customer A” and a fictional account number rather than including a real customer’s name, account, or confidential correspondence.
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Redaction is not a substitute for checking the route, but it can reduce exposure when the task can be completed without real details. Remove identifiers and unique facts that are unnecessary, and avoid including credentials or full client records. If the material must remain identifiable or confidential for the task to work, use an approved workflow or ask your organization’s privacy or security team before proceeding.
Frequently Asked Questions
Does an aggregator’s no-training statement mean the model provider will not train on my prompt?
Not necessarily. Check whether the statement covers only the aggregator’s own models and practices or also the provider receiving the request. Provider practices can differ, and training, evaluation, fine-tuning, and product improvement should be checked separately.
If prompt history is off, is the information no longer stored?
Not automatically. Logging and retention are separate from chat history and training settings. Check whether prompts, outputs, metadata, or files may be retained temporarily or for abuse prevention, security, billing, legal, or other stated purposes, and for how long.
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Not always. Check file-specific storage and deletion terms, including whether deletion covers downstream providers, backups, caches, and replicated copies. OpenRouter’s terms, for example, say uploaded files may be retained until deletion or account closure, subject to exceptions.
What does a provider or data-policy filter actually guarantee?
It can narrow eligible routes, but the exact effect depends on the filter’s definition and scope. Confirm which policy is enforced and whether the setting applies account-wide, to an individual request, and to the model route you plan to use.
Does regional routing mean all processing stays in that region?
Do not assume that from the hostname alone. Verify that the plan, model, endpoint, and feature are covered, and whether the commitment includes downstream providers, storage, support, or connected tools. OpenRouter documents EU and US in-region routing for Business and Enterprise plans, but that vendor example is not a guarantee for every workflow.
The Bottom Line
Before submitting sensitive data, identify every recipient and verify training, logging, retention, deletion, routing, contract, and location protections for the exact route and feature. If a material safeguard remains unclear, withhold the real information and use a redacted or synthetic example instead.
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