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Choose the service and configuration, not just the model name. Before sending confidential business information to an AI system, establish what your organization permits for training, retention, data location, access and contractual protections. Then compare only products and features that meet those requirements; test model quality, latency and cost with approved data afterward. “Not used for training” does not mean “never stored.”
Can you put confidential company data into an AI model?
Sometimes—but only if the specific AI service, settings, contract and workflow meet your organization’s requirements for that information. A policy for a provider’s API does not automatically apply to its consumer chatbot, cloud-hosted service, file feature or other products.
Start with your organization’s data classification rules. Identify what the workflow needs, who may use it, and whether the information can be minimized, redacted or replaced with synthetic data. If policy or law requires internal approval, obtain it before using the service. The provider documentation described here does not establish that any service complies with the laws or sector-specific obligations that apply to your business.
- Lower exposure: Use synthetic, public or appropriately de-identified data to develop and test prompts.
- Controlled use: For approved business data, use the exact commercial or managed service and configuration reviewed by security, privacy, procurement and legal teams.
- Do not submit: Data your organization has not authorized for that service, especially where the service’s retention, access, location or contractual terms remain unverified.
These are decision principles, not a substitute for your company’s information-handling policy or legal advice.
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What should you check before comparing models?
Evaluate the complete data path for the exact product tier and feature—not a provider’s general privacy headline. Record the answers in procurement or security review so that the approved setup can be checked later.
- Training and improvement: Is customer content used to train or improve models by default? Are there opt-in, feedback, evaluation or contract exceptions?
- Retention: What happens to prompts, responses, uploaded files, metadata and logs? Why are they retained, for how long, and can the customer delete them or configure a shorter period?
- Access and review: Can provider personnel or subprocessors access content, for example for abuse monitoring or a feature the customer enables?
- Feature behavior: Check persistent chats, file and retrieval features, caching, search or maps grounding, tools, background processing and application state separately. A retention control may not cover every feature.
- Location: Distinguish where data is stored from where inference occurs. Ask whether backups, metadata and subprocessors are included, and whether the service and region are eligible for the required control.
- Security and governance: Verify identity and access controls, encryption and key options, audit records, administrative settings and incident handling for the actual plan. The provider policies summarized below do not compare these controls exhaustively.
- Contract and legal fit: Review the DPA, subprocessors, regulated-data terms, geography and applicable internal approvals. A policy page alone is not a contract review.
- Utility: After a service passes the data-handling threshold, evaluate it on representative approved tasks, latency, availability, cost and implementation effort.
How do the documented data controls differ?
The table compares provider-published documentation for particular developer or commercial services. It is not a security audit or a ranking of model safety. Retention periods below are provider-stated service terms in documentation accessed in 2026, not independently measured statistics; check the linked live pages and your contract before relying on them.
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| Service scope | Training or improvement position | Retention and important qualifications |
|---|---|---|
| OpenAI API | OpenAI says API data is not used to train or improve models unless the customer opts in to share it. The current data-controls page dates this position to March 1, 2023. | Default abuse-monitoring logs may include prompts and responses and are retained for up to 30 days, except where longer retention is legally required. Modified Abuse Monitoring and Zero Data Retention require prior approval and have additional requirements. ZDR does not make every endpoint or feature storage-free; application-state retention and feature exceptions apply. See OpenAI API data controls and OpenAI business data privacy. |
| Gemini Developer API | For paid services, prompts and responses are not used for product improvement by default. Logs or datasets voluntarily shared with Google may be used to develop or train products, and human review may occur for shared datasets. | Project logs have a default maximum retention of 55 days; configurable settings are 7, 14, 28 or 55 days. The separate ZDR guidance says Search and Maps grounding stores prompts, context and output for 30 days; File API uploads remain until deleted or expired; Interactions API state must be disabled explicitly; and explicit cached content can persist for a configured TTL. A generated Live API session handle can remain for up to 24 hours. See Gemini API logging and Gemini API ZDR. |
| Google Cloud Vertex AI | Google Cloud says it will not use customer data to train or fine-tune AI/ML models without prior permission or instruction. | The Vertex AI ZDR guidance also discusses prompt logging for abuse monitoring. The training restriction is not a blanket no-retention promise; check the service, configuration and applicable terms. See Vertex AI and ZDR. |
| Anthropic commercial API | Anthropic says retained data is not used for model training without express permission. Consumer Claude surfaces have different policies; do not infer their terms from the commercial API documentation. | Storage depends on the feature: where storage is unnecessary, prompts and outputs are not stored; where it is necessary, Anthropic says it aims to minimize retained data and duration. The page states 30-day retention for Covered Models. Verify the model, hosting route and exceptions. See Anthropic API and data retention. |
These descriptions are not interchangeable. For example, the Gemini Developer API’s log settings and feature-specific behavior are distinct from Google Cloud Vertex AI’s guidance. Likewise, OpenAI’s API controls do not establish the terms of a consumer chat product. The cited pages describe provider policies, not a head-to-head assessment of security, legal compliance or model performance.
Does “not used for training” mean your data is not stored?
No. Training use and storage are separate questions. Content can be excluded from model training yet retained for abuse monitoring, application state, a requested feature, or another documented purpose. Retention also does not by itself tell you whether a person can review content, who has access, or whether it is included in backups.
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Ask the provider or account team to identify the purpose, content types, duration, deletion behavior and access rules for each relevant data store. Then verify what actually happens when the workflow uses uploads, tools, grounding, caching or persistent state. Where the documentation says a control is subject to eligibility or approval, do not treat it as active until your account and setup are confirmed.
What does zero data retention cover—and what may fall outside it?
“Zero data retention” is a scoped control, not a universal guarantee that no information is processed or stored anywhere. Eligibility may depend on the customer, endpoint, model or feature. A workflow can also involve a separate file store, cache, conversation state or tool behavior that has its own handling rules.
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For each feature in the proposed workflow, document whether it is eligible for the intended control, what data it retains, and what setting or approval is needed. For the OpenAI API, consult the live endpoint and feature table and confirm account eligibility; for Gemini, check the separate behavior documented for logs, grounding, uploads, stateful interactions and cached content. Do not infer that a service-wide ZDR label overrides a feature-specific exception.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you make the choice?
- Define the data and use case. Identify the information classification, users, task and minimum content the model needs. Set out prohibited data and any required region, retention or access limits.
- Identify the exact product surface. Name the provider, plan, API or managed-cloud service, model, endpoint and features. Do not approve a provider generally when only a particular tier has been reviewed.
- Verify terms and controls. Check the current documentation, contract and account settings for training, retention, deletion, location, access and feature exceptions. Obtain written confirmation for eligibility-dependent controls.
- Apply internal review. Have security, privacy, procurement and legal reviewers assess the DPA, subprocessors, geography and sector-specific requirements relevant to your organization.
- Test with approved evaluation data. Compare output quality on representative tasks only after the candidate services pass the data-handling requirements. Measure the dimensions that matter to the workflow—such as accuracy, latency, availability and cost—rather than assuming one provider is universally safest.
- Constrain and monitor the rollout. Limit access to authorized users, send only necessary data, apply the approved settings, and make owners responsible for checking configuration and provider-term changes.
How do you keep sensitive data exposure lower in practice?
- Remove direct identifiers and irrelevant context before sending a prompt whenever the task permits.
- Keep secrets, credentials and unrelated records out of prompts, files and tool context.
- Use approved accounts and access controls rather than personal or unreviewed accounts.
- Disable persistent state, sharing, uploads or optional features that the workflow does not need.
- Test the real workflow—including deletion and retention behavior—with non-sensitive data before production use.
- Review the configuration and current terms periodically, particularly after enabling a new feature or changing service, region or model.
These measures reduce unnecessary exposure; they do not replace a service’s contractual commitments or make an unapproved service appropriate for restricted data.
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