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Evaluate the exact AI product, plan, feature, and processing route—not the provider’s general privacy slogan. Your review should establish four separate facts: what data is kept and when each category is deleted; whether prompts, outputs, feedback, or related records can be used for training or improvement; who can access the data and for which purposes; and which identity, permission, logging, and audit controls your organization can actually configure.
Then verify every answer against the service-specific contract, data-processing terms, administrative settings, and current technical documentation. A consumer chatbot, commercial workspace, direct API, and cloud-marketplace deployment may have different processors, retention rules, and training choices even when the model brand is the same.
Start with the processing route, not the brand name
An AI provider’s name is not a sufficient scope for a privacy decision. Identify the route your data will take before comparing policies:
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- Commercial workspace: an organization-managed environment with administrators, members, and enterprise terms.
- Direct API: your application sends prompts and receives responses through a provider endpoint. Different endpoints and features can maintain different state.
- Cloud-hosted model: a model consumed through a cloud platform or marketplace. The cloud tenant, cloud provider, and model company may have different processor roles.
- Embedded or connected feature: an assistant operating inside a productivity suite, search product, customer-support system, or connector. Files, permissions, and logs may be governed by the host product as well as the model provider.
Record the product name, edition, endpoint or feature, region, account type, and contract version in your assessment. If those details are missing, a statement such as “the provider does not train on customer data” is too broad to support procurement approval.
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Why product scope changes the answer
Anthropic’s API documentation assigns processor responsibility differently depending on the route. For its API, Anthropic is the processor; for deployments through Amazon Bedrock or Google Cloud’s Agent Platform, the cloud provider is the data processor and its retention and compliance documentation applies. Anthropic’s consumer disclosure, meanwhile, expressly covers Free, Pro, Max, and consumer Claude Code use—not Claude for Work or the API.
Google’s privacy whitepaper distinguishes Google Cloud from Workspace. OpenAI publishes separate business, API, and administrative-control descriptions. These examples are not a ranking: they show why you must compare like-for-like deployments.
Build a data inventory before asking about retention
“How long does the provider retain our data?” is incomplete until you identify the data categories. Put each category in a separate row in your assessment and ask for a default period, configurable period, deletion trigger, backup treatment, and exceptions.
| Data category | Questions to answer | Evidence to request |
|---|---|---|
| Prompts and instructions | Are requests stored, for how long, and does retention differ by endpoint or plan? | Endpoint documentation, service terms, and an administrator setting or contract clause. |
| Generated outputs | Are responses retained independently of prompts? Are they included in transcripts, abuse review, or analytics? | Feature-specific retention description and deletion procedure. |
| Uploaded files and attachments | Where are files stored, how are they indexed, and when are derivatives removed? | File-storage policy, connector documentation, and tenant deletion terms. |
| Connected sources | Does the service copy source content, or retrieve it on demand? How are source permissions rechecked? | Connector architecture, permission model, and logs. |
| Feedback and ratings | Does submitting feedback retain the related conversation, and for how long? | Feedback policy and the exact account setting that controls it. |
| Safety, abuse, and fraud records | Can records be retained after ordinary content is deleted? Who can inspect them? | Safety-retention exception, purpose limitation, and access controls. |
| Compliance and activity logs | What administrative or compliance events are recorded, for what period, and can you export them? | Audit-log schema, retention setting, and export API or console documentation. |
| Session transcripts and application state | Does a conversation remain available to users or applications after the response? Are stateful features governed separately? | Feature matrix and state-management documentation. |
| Backups and disaster-recovery copies | When are deleted records removed from backups, and are backups excluded from ordinary deletion commitments? | Contract language describing backup cycles and exceptions. |
Anthropic’s API documentation is a useful example of why this separation matters: it describes different models for prompts and outputs, compliance records, and certain session transcripts. A single “retention” number cannot represent all of those records.
Define deletion events precisely
Ask what starts the clock: a user deleting a chat, an administrator deleting a workspace, an API response completing, a contract ending, or a formal data-deletion request. Distinguish deletion from de-identification, logical removal from active systems, and eventual removal from backups. Require the provider to identify legal, safety, abuse-prevention, and compliance exceptions rather than assuming that “delete” means every copy disappears immediately.
Separate retention from training and model improvement
Retention answers whether a provider keeps data. Training answers whether it may use that data to improve a model or related system. A provider can retain data for security or billing without training on it, or use an explicitly submitted feedback item for improvement under a separate rule.
Document these dimensions independently:
- Which inputs, outputs, files, metadata, and feedback are covered.
- Whether use is allowed by default, prohibited by default, or requires an opt-in.
- Whether administrators, individual users, or both can change the choice.
- What happens to data collected before a preference changes.
- Whether safety, abuse, legal, or quality-review exceptions remain.
- Whether human reviewers or contractors can see material used for improvement.
What the published examples establish
OpenAI states that, by default, it does not use data from ChatGPT Enterprise, Business, Edu, Healthcare, Teachers, or its API platform—including inputs and outputs—to train or improve its models. Treat that as a product-scope statement: confirm the exact plan, API feature, region, and current terms rather than extending it to every consumer or embedded experience.
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Google’s Cloud/Workspace privacy whitepaper states that customer data is not used to train its models without customer permission or instruction. It separately says Workspace content is not used to train or improve underlying generative models outside Workspace without permission. The whitepaper is dated 2023, so use it as an initial statement and verify current product terms, the data-processing addendum, and administrative settings before relying on it for a present procurement decision.
Ask what “opt out” actually changes
An opt-out may stop future collection while leaving previously collected examples in a training pipeline, or it may apply only to a particular product. Ask whether the setting is effective immediately, whether it is organization-wide, and whether the provider will confirm treatment of data already retained. Capture screenshots or export the setting with its policy version and date.
Evaluate provider access and customer access separately
Access control has two sides. The provider may need limited access for support, security, abuse prevention, reliability, or legal compliance. Your organization needs controls over its own users, applications, connectors, and administrators. Assess both instead of treating encryption as proof that no person can see content.
Provider-side access questions
- Which provider teams, subprocessors, or support vendors may access content or metadata?
- For which purposes is access permitted: troubleshooting, safety review, fraud prevention, legal requests, or service operation?
- Is access approved through a ticket or just-in-time workflow?
- Are support sessions recorded, logged, and reviewed?
- Can the customer require notice, consent, or a customer-managed key for certain operations?
- How long are access records kept, and can the customer obtain them?
Ask for written purpose limitation and an explanation of emergency-access procedures. “Encrypted at rest” and “encrypted in transit” protect transmission and storage, but they do not by themselves define who can decrypt data during an authorized support or safety operation.
Customer-side identity and administration
Check whether the exact plan supports SAML single sign-on, multi-factor authentication, role-based administration, automated provisioning and deprovisioning, API-key restrictions, and audit-log export. OpenAI lists SAML SSO, MFA, roles, SCIM for selected plans, API administration, and audit logs as examples of available controls; availability and scope vary by product tier and platform.
For each control, record the enforcement boundary. An identity provider may enforce MFA for interactive users while service accounts, personal API keys, or a connected application bypass that policy. Confirm whether administrators can revoke sessions, rotate keys, restrict domains, and require approved applications.
Connector and delegated-permission controls
Connected tools introduce a second authorization layer. Determine whether the AI service acts with the user’s delegated permissions, a shared service account, or a provider-managed credential. Test what happens when a user loses access to a source system, changes teams, or leaves the organization. Require logs that show the requesting identity, source object, action, and result without exposing unnecessary content.
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Check location, processors, and data flows
Map where prompts, outputs, files, logs, and backups are processed and which legal entity controls each step. Data residency is not the same as data sovereignty: a record stored in one region may be accessed by support personnel or subprocessors in another.
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- Identify inference, storage, backup, and support locations.
- List subprocessors and the notice or objection process for changes.
- Identify the processor for the exact route. Anthropic’s documentation assigns the cloud provider as processor for certain marketplace deployments.
- Check cross-border transfer mechanisms and contractual commitments.
- Confirm whether a regional setting covers all features, including safety review, analytics, and abuse monitoring.
For cloud-hosted models, read both the model company’s terms and the cloud platform’s data, retention, and compliance documentation. A cloud tenant may provide controls that are unavailable in the provider’s direct consumer or API surface, but only the applicable contract establishes that protection.
Use a repeatable evidence and scoring workflow
A written workflow prevents a persuasive privacy page from substituting for a control review.
- Define the use case and data classes. List personal data, confidential business information, regulated records, source code, credentials, and any material that must never leave a controlled boundary.
- Identify the exact service path. Record product, plan, endpoint, feature flags, region, account type, cloud host, and integrations.
- Collect authoritative documents. Obtain the service-specific terms, data-processing addendum, retention and deletion documentation, security description, subprocessor list, SLA, and current policy version.
- Complete the category-by-category retention table. Include prompts, outputs, files, connectors, feedback, safety records, logs, transcripts, and backups.
- Complete the training-use table. Mark each data type as prohibited, default-on, default-off, or opt-in; record exceptions and the effect of changing the setting.
- Map access paths. Document provider support and safety access, your administrators, end users, service accounts, connectors, and subprocessors.
- Verify technical controls in the console. Check SSO, MFA, roles, provisioning, key management, retention settings, regional controls, and audit-log export in the purchased edition—not a demonstration tenant.
- Run deletion and offboarding tests. Delete a test conversation or file, remove a user, revoke a token, and confirm what disappears, what remains, and what the logs show.
- Resolve exceptions in writing. Send unanswered questions to the provider and attach written responses to the procurement record. Do not convert an assumption into a control.
- Set review triggers. Reassess when the provider changes policy, adds a feature, changes processors, expands a region, or your organization connects a new data source.
Use an evidence register
For every conclusion, record the document title, policy or contract version, publication date, URL if supplied by the provider, date you checked it, product scope, and the person who approved the interpretation. Mark each item as contractual, configurable, documented but non-contractual, or unverified. This makes it clear which safeguards survive a configuration change or service update.
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A fair comparison uses identical questions and does not turn different product scopes into a winner-and-loser table.
| Axis | Minimum comparison questions | Decision signal |
|---|---|---|
| Retention and deletion | What categories are stored, for how long, with which defaults, deletion triggers, backup periods, and exceptions? | Prefer a documented, configurable model that covers every feature you will use. |
| Training and improvement | Are prompts, outputs, feedback, and safety records used? Is the choice default-off, opt-in, or unavailable to administrators? | Require a scope-matched contractual statement and a verifiable setting. |
| Provider access | Who can access content, for which purposes, under what approval and monitoring, and with what customer visibility? | Look for purpose limitation, least privilege, and auditable access. |
| Customer access | Are SSO, MFA, roles, lifecycle automation, key restrictions, connector permissions, and log export available in your plan? | Score the controls you can enforce, not features listed for another tier. |
| Location and processors | Where are inference, storage, backups, and support performed? Which entity is the processor for this route? | Reject unexplained transfers or require contractual approval. |
| Contract and proof | Do the DPA, service terms, SLA, security attestations, and written answers align? | Give contractual commitments more weight than marketing descriptions. |
NIST’s Generative AI Profile identifies third-party data-privacy and information-security risks and discusses due diligence, service-level agreements, and assurance reports as risk-management artifacts. The NIST AI Risk Management Framework is voluntary; it is a way to organize evidence, not a vendor certification or a numerical provider score.
Provider examples: what to verify before relying on a claim
OpenAI business and API services
OpenAI’s published business and API disclosures describe default non-use of business and API inputs and outputs for model training, encryption using AES-256 at rest and TLS 1.2 or higher in transit, configurable retention for qualifying organizations, and API zero-data-retention options for eligible customers. Administrative materials list SAML SSO, MFA, roles, selected-plan SCIM, API administration, and audit logs.
Confirm the exact plan and endpoint. OpenAI’s API data-control documentation notes that one API can have different state and retention behavior across endpoints and features. A broad statement about API data does not automatically answer questions about application state, logs, or feature-specific records.
Anthropic API and consumer services
Anthropic’s API documentation describes standard retention, zero data retention (ZDR), and HIPAA-ready arrangements, along with feature eligibility and exceptions. It describes prompts and outputs as not retained by default outside a covered-model exception requiring 30-day retention, while the Compliance API Activity Feed and certain session transcripts follow separate retention models. Under ZDR, prompts and responses are not stored at rest after the API response, but ZDR is enabled per organization and other records or features remain governed separately.
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Do not treat ZDR as “no processing.” The model still processes the request to produce a response, and metadata or feature-specific records may follow different rules. Verify the endpoint, model, metadata, and exceptions in the live feature matrix and applicable agreement.
Google Cloud and Workspace
Google’s Cloud/Workspace privacy whitepaper says customer data is not used to train models without permission, describes Cloud tenant and access-transparency commitments, and states that Workspace content is not used to train or improve underlying generative models outside Workspace without permission. Because the whitepaper is from 2023 and separates Cloud from Workspace contexts, verify current service-specific terms, the data-processing addendum, settings, and any newer product documentation before making a current claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common mistakes and how to correct them
Using a consumer policy for a commercial purchase
Problem: A consumer help article is cited to approve an enterprise API.
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Treating zero data retention as zero risk
Problem: ZDR is interpreted as no processing, no metadata, or no safety records.
Correction: Record what ZDR covers, when storage stops, which organization enabled it, and which logs, compliance records, or features remain outside the setting.
Counting encryption as an access policy
Problem: Encryption language is used instead of an answer about support personnel or reviewers.
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Correction: Obtain purpose, authorization, monitoring, and audit details for provider access, then assess customer-side roles and logs separately.
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Ignoring feedback and safety exceptions
Problem: A default training opt-out is treated as an absolute ban.
Correction: Ask whether feedback, safety-flagged conversations, abuse investigations, or legal requests create separate retention or improvement pathways.
Comparing unlike services
Problem: A managed cloud deployment is compared with a consumer chatbot as if they share terms.
Correction: Compare identical routes, features, regions, and account tiers, and date-stamp the comparison.
Failing to test offboarding
Problem: The organization assumes that removing a user deletes their conversations, files, tokens, and audit records.
Correction: Run a documented deletion and revocation test and retain the provider’s response about backups and exceptions.
Questions to put in the contract and renewal checklist
- Which categories of customer data may be retained, and for what maximum periods?
- What deletion event starts each retention clock, and how are backups handled?
- May prompts, outputs, feedback, files, metadata, or safety records be used for training or improvement?
- Can the provider change that treatment without customer instruction or notice?
- Which personnel and subprocessors may access content, for which purposes, and under what approvals?
- Which entity is the processor for every deployment route, including cloud marketplaces?
- Which regions handle inference, storage, backup, and support?
- Which SSO, MFA, role, provisioning, connector, key, and audit controls are included in the purchased plan?
- Can the customer export logs and obtain evidence of deletion or provider access?
- What notice and remedies apply when policy, subprocessors, features, or regions change?
Approval should be conditional on evidence that matches the actual service configuration. If an answer depends on a setting, make that setting part of deployment acceptance and monitor it after launch.
Frequently Asked Questions
Is my data used for model training?
It depends on the product surface and account type. Consumer, commercial-workspace, direct-API, and cloud-hosted services can have different defaults and exceptions. Check the service-specific terms and the administrator setting for your exact route; do not apply a consumer disclosure to an enterprise account or API.
Does zero data retention mean the provider never sees my prompt?
No. Zero data retention is a scoped storage configuration. The service still processes the request, and logs, compliance records, metadata, or feature-specific records may follow separate retention rules.
What evidence should procurement require?
Request the service-specific contract and data-processing terms, retention and deletion documentation, training-use rules, subprocessor and location details, security evidence, plan-specific control documentation, and written answers to unresolved exceptions. Verify settings in the purchased administrative console.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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