Choose an AI service by the exact route your data will take—not by a provider’s general privacy promise. Before sending sensitive information, verify the product surface and processor, training use, each kind of retention, deletion, data location, access controls, and any eligibility limits. Then send only the minimum data needed and test the workflow with appropriately de-identified material.
Start with the data and the consequences of exposure
List the information the workflow would send: for example, names, account records, health details, financial documents, source code, or confidential business plans. Decide which categories must never leave your controlled environment and which could be processed externally if specified safeguards are in place. The right choice depends on the data, the harm a disclosure could cause, and your organization’s obligations; there is no universal safest provider.
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For data that may be sent, write down requirements before comparing services. Include acceptable retention, required processing or storage locations, who may access the workflow, and whether the service must support a particular contract or security control.
Identify the exact product route and processor
Consumer chat, a business workspace, a provider’s direct API, and a model accessed through a cloud platform can have different terms and data flows. Confirm which product, account configuration, contract, and privacy documentation govern the workflow. Do not transfer a commitment made for one route to another.
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For example, Anthropic says that when Claude models are accessed through Amazon Bedrock or Google Cloud’s Agent Platform, the cloud provider is the data processor and its platform documentation governs the corresponding controls. Those routes should be assessed under the cloud provider’s terms, not assumed to inherit every direct-Claude API setting. Anthropic’s retention documentation
Check training use separately from retention
Ask whether prompts, outputs, files, and feedback are used to train or improve models; whether that use is on by default, opt-in, or opt-out; and whether the answer applies to the exact product and account. OpenAI states that data from ChatGPT Enterprise, Business, Edu, Healthcare, Teachers, and its API platform is not used for model training by default. That statement should not be generalized to an unexamined product or workflow. OpenAI’s business data commitments
“Not used for training” does not mean “not retained.” Check retention as a separate requirement, including safety or abuse monitoring and data held to operate a feature.
Map every kind of retention and its deletion rules
Ask what is kept, why, where, and for how long. Distinguish abuse-monitoring data from saved chat history, application state, uploaded files, logs, caches, and data held by connected tools. Confirm what deletion means in practice, including timing and exceptions.
OpenAI’s API data-controls documentation lists different behavior by endpoint, including application state that may remain until deletion and endpoints or features that are not eligible for Zero Data Retention (ZDR). OpenAI says ZDR and Modified Abuse Monitoring require prior approval, so a general API privacy statement is not enough to establish that a particular workflow has those controls. Check the current endpoint and feature details for the route you intend to use. OpenAI API data controls
Anthropic’s documentation for covered API and platform arrangements describes conversation content as not retained by default, while also specifying exceptions and separate retention models. It says covered models require 30-day retention; under its ZDR arrangement, prompts and responses are not stored at rest after the API response returns, and organization-level ZDR must be enabled separately. These statements apply to the covered arrangements, not automatically to other platform routes. Anthropic’s retention documentation
Questions to resolve before relying on “zero retention”
- Does ZDR apply to this organization, endpoint, model, and feature?
- Is approval or a separate account-level setting required?
- Do files, tools, or other workflow features follow a different retention model?
- What data is retained for abuse review or other stated exceptions?
- How and when can retained application state be deleted?
Separate storage location from processing location
“Data residency” can refer to different things. Ask where data is stored at rest, where model inference occurs, and where other processing for the feature takes place. Verify that the required region is available for the actual service, account, and configuration; a storage commitment alone does not establish that all processing stays in that region.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsOpenAI describes eligible storage regions separately from in-region GPU inference and supported API processing regions. Check which of these capabilities applies to your selected product and workflow rather than treating regional storage as proof of regional inference. OpenAI’s business data commitments
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For a managed cloud service such as Bedrock, confirm the selected region and the model’s retention requirements alongside the account configuration. AWS documents retention settings at account and region level, as well as model-specific allowed modes. A model may not be available if the effective mode does not satisfy its requirement. AWS Bedrock data protection
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review access, security, and contractual controls
Check who in your organization can submit data, view outputs, manage settings, and inspect logs. Review encryption, key-management options, audit logging, support access, abuse-investigation procedures, and the contractual commitments that apply. OpenAI lists encryption at rest and in transit, enterprise key management, and access controls among its business features; verify availability and configuration for the product you will use. OpenAI’s business data commitments
For cloud-hosted models, include the platform provider in the review: identify its role, applicable contract, and controls for the specific model and region. A model developer’s policy and a cloud platform’s policy are not interchangeable.
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Reduce risk in the application around the model
Provider settings are only one layer. Limit what the application sends, restrict retrieval to information each user is authorized to see, and set retention and audit practices for your own systems. Where practical, detect, mask, or anonymize personal identifiers before sending content. These safeguards can reduce exposure even when a provider has strong controls.
AWS’s guidance gives implementation examples such as VPC endpoints, IAM policies, PII detection, masking, anonymization, guardrails, S3 lifecycle retention rules, lineage, and audit logging. They are examples rather than a requirement to use every AWS service, and using them does not by itself establish compliance. AWS Bedrock data protection
Compare candidates against a written checklist
Use the same questions for each route under consideration. Record the answer and source for the specific endpoint, model, feature, region, account, and contract; mark a requirement unresolved if documentation does not establish it.
- Route and processor: Which product surface is used, and which company processes the data?
- Training: Are inputs and outputs used for model improvement, and under what setting or permission?
- Retention: What is retained for monitoring, application state, files, logs, or features, and for how long?
- Deletion and eligibility: What can be deleted, when, and are ZDR or similar controls available for every part of the workflow?
- Geography: Where are storage, inference, and other processing performed?
- Access and security: Which access, encryption, key, audit, support, and contractual controls apply?
- Application safeguards: Can you minimize fields, mask data, scope retrieval, and govern logs and retention?
- Operational fit: Does the option meet your quality, latency, availability, integration, and cost needs on representative, appropriately de-identified tasks?
Published privacy documentation does not establish which candidate will perform best or cost least on your workload. Test those operational requirements without exposing sensitive production data unnecessarily.
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Make the deployment decision—and reassess it
- Exclude routes that fail a mandatory requirement. If a category of data may not leave your environment, do not send it to an external service merely because other controls look strong.
- Resolve unknowns with the relevant provider or contract. Get the answer for the endpoint, model, feature, region, and account configuration that will actually be deployed.
- Run a limited pilot. Use de-identified or synthetic examples where feasible, and measure task quality, latency, availability, and integration needs against your acceptance criteria.
- Put safeguards in the application. Apply data minimization, access restrictions, masking where appropriate, retention rules, and logging governance.
- Recheck when the workflow changes. A new endpoint, feature, model, region, account setting, or platform route can change applicable controls.
NIST’s AI Risk Management Framework offers a voluntary organizational process organized around Govern, Map, Measure, and Manage; its Generative AI Profile provides related guidance. These are risk-management resources, not product certifications or guarantees that a particular provider is safe. NIST AI Risk Management Framework NIST Generative AI Profile
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