The Tool Desk
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Before submitting sensitive information, identify the exact product and account, then check its contract and settings. Ask separately about model training, fine-tuning, safety review, feedback, storage, retention, and deletion.
What “not used to train models” actually means
Training or model improvement means using data to update a model or improve its future performance. A provider’s default no-training commitment addresses that use; it does not necessarily prevent the service from processing data to answer a prompt, operate a feature, or monitor for abuse.
- Fine-tuning or customization: A customer may direct a provider to adapt a model or configuration for that organization. That is distinct from the provider using customer data to train a general model.
- Inference and feature processing: The service processes a prompt to produce an answer. Connected tools, grounding, or session features may have their own data handling and storage behavior.
- Safety and abuse monitoring: Providers may analyze or review data to enforce policies. This is a separate question from general-model training.
- Feedback: A rating or report can include the conversation it relates to, and may be treated differently from ordinary prompts.
- Retention and deletion: Prompts, outputs, files, logs, and application state may persist for different periods. A no-training promise does not specify how long each is kept.
For that reason, ask whether data is used to train general models, whether it is retained or reviewed for other purposes, and what happens when users submit feedback or enable a feature.
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What major providers say about commercial services
The table summarizes statements in the providers’ cited official documentation. These are scoped provider statements, not a legal determination or a guarantee for every integration, negotiated agreement, or future product version.
| Provider and service | Stated default on model training | Important qualification |
|---|---|---|
| OpenAI Business, Enterprise, Edu, and API | Inputs and outputs are not used to improve models by default. OpenAI Help Center | API owners can enable data sharing. Feedback may include the associated conversation. Abuse-monitoring logs and endpoint application-state retention are separate issues. OpenAI API data controls |
| Anthropic Claude for Work and API | Commercial data is not used to train models by default. Anthropic Help Center | Participation in the Development Partner Program is an exception. Consumer Claude terms and safety or feedback handling are distinct. Anthropic Privacy Center |
| Google Cloud Vertex AI | Google says it will not train or fine-tune AI/ML models on customer data without prior permission or instruction. Google Cloud documentation | Some features store prompts, context, or outputs for service purposes. Review feature-specific behavior and the applicable service terms. Google Cloud service terms |
| Microsoft Copilot for Microsoft 365 and Azure OpenAI Service | Microsoft says its generative AI solutions do not use Customer Data to train foundation models without permission. Microsoft customer guide | Microsoft describes optional customer-directed fine-tuning for an organization’s own use. Check current Product Terms and the data-protection terms for the deployed service. |
| Amazon Bedrock | AWS says it does not use customer content to train models or share it with third parties. AWS Prescriptive Guidance | Data deliberately supplied for model customization is used for that customization; AWS says it is not used to train base Titan models. Amazon Bedrock documentation |
Check the model-specific terms as well as the platform’s terms. Google says third-party models are subject to third-party terms, and Bedrock offers models from multiple providers.
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Where exceptions and different handling can apply
Opt-ins, feedback, and partner programs
Do not assume the default applies after a user or administrator changes a setting, joins a program, or submits feedback. OpenAI says API customers can opt into data sharing and that feedback may include the associated conversation. Anthropic identifies participation in its Development Partner Program as an exception for commercial products. Consumer Claude data has separate terms, including safety and explicit opt-in paths.
Customer-directed customization
Fine-tuning is not the same as a provider training its general model on customer data. Microsoft describes customer-directed fine-tuning for an organization’s use. AWS says data submitted for a Bedrock customization job is used for that customization. Confirm who can access the resulting model or configuration, what data the job uses, and how the job’s inputs and outputs are retained.
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Feature-specific processing and retention
The endpoint or feature matters. OpenAI’s API documentation describes abuse-monitoring logs and application-state retention that vary by endpoint, and identifies eligibility for retention controls. It says abuse-monitoring logs are generally kept for up to 30 days unless an exception applies. Google documents feature-specific behavior, including storage associated with Grounding with Google Search or Maps and session resumption. Review the applicable feature documentation rather than treating the service’s training default as a retention policy.
Consumer products are not interchangeable with managed business accounts
Providers distinguish commercial and consumer services. OpenAI separates individual services from Business, Enterprise, Edu, and API. Anthropic distinguishes Claude for Work and API from Free, Pro, and Max. A user signed into a personal account or using a consumer product should not infer that a business-service commitment applies.
Rank #4
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How to verify the policy for your organization
- Identify the exact service and account. Record the product name, whether it is a managed business workspace or consumer account, and the model or endpoint in use.
- Read the governing documents. Check the organization’s contract, data-processing addendum, current product terms, and any model-provider or subprocessor terms. Public documentation may not reflect negotiated terms or the organization’s deployed configuration.
- Map the data being sent. Include prompts, outputs, uploaded files, connector content, feedback, and telemetry—not just typed questions.
- Check for changes to the default. Look for API sharing settings, development or partner programs, feedback submission, fine-tuning jobs, and administrator controls.
- Review retention separately. Ask how long logs, files, prompts, outputs, and application state persist; which controls change that; whether the endpoint qualifies for zero-data-retention options; and what deletion or legal exceptions apply.
- Check external features and models. Grounding, connectors, and third-party models can have additional terms or data-handling behavior.
For particularly sensitive material, get a written answer tied to the deployed product, endpoint, region, account settings, and contract—not a general assurance about enterprise AI.
Quick Recap
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