The Tool Desk
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Then reconcile those answers with the service-specific contract, data-processing terms, administrative settings, and current documentation. A consumer chatbot, commercial workspace, direct API, and cloud-marketplace deployment can have different retention rules, processors, and controls even when they use models from the same provider.
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Start with the processing route, not the provider name
Make an inventory of the proposed use before asking a vendor for a security statement. Record the product and edition, account type, endpoint or feature, deployment region, connected tools, data categories, user population, and whether the service is bought directly or through a cloud marketplace. This scope determines which terms apply.
For example, an employee using a consumer chatbot may be governed by consumer terms, while a developer calling a direct API is governed by API terms. A model invoked through a cloud platform may make that cloud company the data processor for the request, with its own retention and compliance documentation. Treating all three as one service creates false assurance.
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Build a one-line data-flow description
Write the path in plain language: “User enters a support ticket into an enterprise workspace; the prompt is sent to an inference endpoint in a stated region; the response returns to the workspace; a connector retrieves records from a permissioned system; administrators receive audit events.” Add every storage point, subprocesser, human review path, and export destination you know about.
Ask the provider to confirm or correct that description. A broad statement about “API data” may not answer how an application-state endpoint, file upload, connector index, abuse log, or feedback record is handled.
The four questions every evaluation must answer
1. What is retained, and when is it deleted?
“We do not train on your data” does not mean “we do not retain your data.” Request a retention schedule for each category:
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- Prompts and system instructions.
- Model outputs and conversation transcripts.
- Uploaded files, images, embeddings, and indexes.
- Connector content and retrieved snippets.
- Feedback, ratings, and support tickets.
- Safety, fraud, abuse, and trust-and-safety records.
- Application, authentication, and API logs.
- Compliance exports, activity feeds, and legal-hold records.
- Backups, caches, and disaster-recovery copies.
For each category, document the default period, available shorter or longer settings, the deletion trigger, expected deletion time, backup expiry, and exceptions. “Deleted” should be defined: removed from active systems, rendered inaccessible, cryptographically erased, or scheduled for eventual expiry are not identical outcomes.
Ask about feature-specific exceptions
Retention often changes when you enable a feature. A conversation endpoint may have one policy, file analysis another, and an audit export a third. Compliance records may be retained longer than ordinary prompts. A provider may also preserve a limited record for security investigations, fraud prevention, or a legal obligation.
Anthropic’s API documentation, for instance, describes standard retention, zero-data-retention (ZDR), and HIPAA-ready arrangements while noting separate models for the Compliance API Activity Feed and certain session transcripts. It describes a covered-model exception requiring 30-day retention and says customers must check feature eligibility and exceptions. These are examples of why a single “API retention” label is insufficient.
Understand zero data retention precisely
ZDR is a scoped configuration, not a synonym for “no processing” or “nothing is ever seen.” Anthropic describes ZDR as not storing customer prompts or responses at rest after the API response, with the setting enabled per organization. Other records and features can remain under separate rules. OpenAI likewise documents API zero-data-retention availability only for eligible customers and routes.
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Before relying on ZDR, obtain the exact eligible endpoints, model and feature list, metadata retained, abuse-monitoring exceptions, support-access rules, and deletion behavior for logs and backups. Have the provider attach those details to the order form or data-processing addendum where possible.
Separate retention from training and product improvement
Retention asks whether a record exists. Training or improvement asks whether data may influence a model, evaluator, classifier, safety system, or other product process. A service can retain data for security while prohibiting model training, or use explicitly submitted feedback while deleting ordinary transcripts.
Define all improvement uses
Ask separately whether the provider may use:
- Prompts and outputs for foundation-model training.
- Data for fine-tuning, reinforcement, evaluation, or quality measurement.
- Ratings, corrections, and support conversations.
- Flagged content for safety or abuse analysis.
- De-identified or aggregated records.
- Human-reviewed samples and internal diagnostics.
Clarify whether the default is prohibited, permitted, or opt-in; who can change the setting; whether it applies retroactively; and how quickly a changed preference takes effect. Ask whether data submitted before an opt-out remains in a training pipeline and whether deletion requests can remove it from derived artifacts.
What the current disclosures illustrate
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. The statement applies to those business and API scopes, not automatically to every consumer surface or every connected feature. Confirm the exact plan and configuration.
Anthropic’s consumer disclosure is explicitly for Free, Pro, Max, and consumer Claude Code use, not Claude for Work or the API. Dated March 16, 2026, it says consumer conversations may be used to improve models when a user opts in, when a conversation is flagged for safety review, or when the user otherwise explicitly opts in. It says Incognito chats are not used to improve Claude even if Model Improvement is enabled. It also says feedback can result in storage of the related conversation for up to five years. Those consumer terms must not be applied to commercial accounts.
Google’s Cloud/Workspace privacy whitepaper says customer data is not used to train its models without customer permission and says Workspace content is not used to train or improve underlying generative models outside Workspace without permission. The whitepaper is dated 2023 and distinguishes Cloud from Workspace contexts, so use it as a starting point and verify current product terms, settings, and the data-processing addendum before procurement.
Map who can access data and why
Access analysis has two sides: provider personnel and your own administrators, users, applications, and connected tools.
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Provider-side access
Ask whether support, security, reliability, abuse-prevention, or engineering staff can access content. For each purpose, require:
- The business purpose and least-privilege boundary.
- Approval requirements and separation of duties.
- Authentication strength and privileged-access controls.
- Session recording, audit trails, and review cadence.
- Notification or reporting available to customers.
- Rules for subcontractors and cross-border support.
- Emergency-access and legal-request procedures.
“Access is limited” is not enough without a purpose, authorization path, monitoring method, and retention period for the resulting access logs. Ask for independent assurance reports or contractual commitments that describe these controls, and distinguish a control design claim from evidence that it operated during a stated period.
Customer-side access
Verify the controls your administrators can use rather than assuming enterprise branding guarantees them. Look for SAML single sign-on, multifactor authentication, role granularity, SCIM or equivalent lifecycle provisioning, administrator separation, API-key management, service accounts, connector permission inheritance, and exportable audit logs.
Check whether a user who loses access to a source system can still see copied content in an AI conversation or index. Determine whether administrators can revoke sessions, rotate keys, disable a connector, quarantine a workspace, and identify every person or application that accessed a file. Confirm the plan tier for each control; OpenAI lists SSO, MFA, roles, SCIM for selected plans, API administration, and audit logs, but availability and scope vary by product and platform.
Confirm location, processors, and contractual responsibility
Record where data is collected, stored, processed for inference, backed up, and reviewed. “Data residency” may cover storage but not inference, support, telemetry, or subprocessors. Ask for the current subprocessor list, notice period for changes, transfer mechanism, and regional availability of every feature you plan to enable.
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Google’s Cloud and Workspace materials likewise distinguish the two contexts. Do not combine a Cloud commitment with a Workspace assumption, or treat a dated whitepaper as a substitute for current service-specific terms.
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A practical due-diligence procedure
- Classify the information. Mark regulated data, personal data, confidential business information, credentials, source code, customer records, and information that may be legally privileged. Define what is prohibited regardless of provider controls.
- Freeze the proposed scope. Name the product, edition, endpoint, model, region, connectors, users, and purchasing route. Capture policy and documentation version dates.
- Draw the data flow. Include collection, inference, temporary state, persistent storage, backups, logs, human review, exports, and subprocessors.
- Fill a retention matrix. For every data category, record default retention, configurable period, deletion trigger, backup treatment, legal or safety exceptions, and evidence source.
- Fill an improvement-use matrix. Record whether each category can be used for training, evaluation, safety, human review, or analytics; the default setting; opt-in or opt-out mechanics; and the effect of changing the setting.
- Test access controls in a non-sensitive tenant. Create separate administrator, standard-user, auditor, and service-account roles. Verify provisioning, deprovisioning, MFA enforcement, connector permissions, key rotation, and audit exports.
- Request provider-access evidence. Ask for support-access procedures, approval and logging details, independent assurance reports, incident notification terms, and the process for responding to government requests.
- Reconcile documents. Compare marketing pages, product documentation, service-specific terms, the DPA, SLA, security exhibit, and order form. Escalate conflicts to legal and security reviewers rather than choosing the most favorable sentence.
- Obtain written exceptions. List every unresolved feature, region, retention, or access exception and obtain a named owner, compensating control, and review date.
- Reassess after launch. Monitor policy changes, new endpoints, connector additions, subprocessor notices, audit-log alerts, and changes to the tenant configuration.
Use a consistent comparison matrix
When comparing two or more services, use identical questions and record “not stated” when the provider has not established a value. Do not turn different product scopes into a winner-and-loser ranking.
| Dimension | Evidence to capture | Decision question |
|---|---|---|
| Retention and deletion | Categories, default and configurable periods, deletion timing, backups, safety and compliance exceptions | Can the service meet the required deletion deadline for every data type? |
| Training and improvement | Covered data, default setting, opt-in or opt-out process, feedback and safety exceptions, effect of a changed preference | Can prohibited data be excluded and verified? |
| Provider access | Support and safety purposes, authorization, monitoring, audit trails, customer transparency | Is human access necessary, bounded, and evidenced? |
| Customer access | SSO, MFA, roles, lifecycle provisioning, connector inheritance, key management, audit-log export | Can your organization enforce least privilege and investigate use? |
| Location and processors | Storage and inference regions, subprocessors, transfer terms, processor identity for the route | Does the route satisfy regulatory and contractual geography requirements? |
| Contract and proof | DPA, service terms, SLA, security attestations, policy version and date, written exception responses | Can the promise be enforced and rechecked? |
Apply NIST without treating it as a certification
The National Institute of Standards and Technology describes its AI Risk Management Framework as “intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.” Use that framework to organize governance, not to award a provider a pass or fail certificate.
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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 possible risk-management artifacts. A practical interpretation is to retain the evidence behind each decision: the applicable terms, configuration screenshots or exports, test results, exception approvals, and reassessment date.
Common evaluation mistakes and corrections
Mistake: treating a provider-wide slogan as a contract
Correction: Tie every claim to the product, plan, route, region, feature, and policy version. Ask for contractual language when the risk is material.
Mistake: confusing no training with no retention
Correction: Maintain separate retention and improvement-use matrices. Include abuse records, feedback, logs, files, and backups.
Mistake: assuming ZDR covers the whole application
Correction: Identify eligible endpoints and separately assess metadata, logs, compliance feeds, transcripts, connectors, and support records.
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Mistake: ignoring the customer’s own permissions
Correction: Test deprovisioning, connector revocation, role boundaries, key rotation, and audit-log completeness in a non-production tenant.
Mistake: relying on an old whitepaper
Correction: Date-stamp the evidence and confirm current service terms, settings, and regional availability before signing or renewing.
Mistake: treating marketplace hosting as identical to direct API use
Correction: Identify the processor for the actual route and review that platform’s retention, access, subprocessor, and compliance documentation.
What a defensible approval record contains
A procurement or security file should let a reviewer reconstruct the decision months later. Keep:
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- A data-flow diagram and data-classification decision.
- Retention and training-use matrices with source document dates.
- Provider-access commitments and assurance evidence.
- Customer-control test results, including failed tests and remediation.
- The DPA, service-specific terms, SLA, security exhibit, and subprocessor record.
- Approved exceptions, compensating controls, owners, and expiry dates.
- A monitoring plan for policy, configuration, and subprocessor changes.
Approval should be conditional when evidence is incomplete. For example, permit low-sensitivity drafting while blocking regulated records until a retention exception is resolved, or require a dedicated tenant and disabled connectors until access logging is verified. The decision should state the allowed data classes and prohibited workflows, not merely name the provider.
Frequently Asked Questions
Is my data used for model training?
There is no provider-wide answer. The result depends on the product surface and account type: commercial and API terms may prohibit training by default, while a consumer service may allow improvement use after an opt-in or for specified safety or feedback cases. Confirm the applicable terms and tenant setting for the exact route you will use.
Does zero data retention mean the provider never processes my prompt?
No. ZDR concerns storage after a defined processing event. The request still has to be processed, and metadata, abuse controls, compliance records, logs, or excluded features may follow separate retention rules. Verify endpoint eligibility and exceptions in the current agreement.
What should we request before signing an AI service contract?
Request the service-specific DPA and terms, retention schedule, training-use rules, processor and subprocessor details, support-access controls, security attestations, regional processing information, audit-log capabilities, deletion procedure, and written answers to every unresolved exception.
How often should an approved AI service be reassessed?
Reassess whenever the provider changes a policy, model route, region, connector, processor, or administrative feature, and at the review interval set by your risk program. Keep the original scope and evidence so a later reviewer can see what changed.
The Bottom Line
Approve an AI service only for the data, features, route, and account configuration you have actually verified. Separate retention from training use, provider access from customer access, and direct deployments from marketplace routes; then enforce the result through contract language, tenant settings, identity controls, logging, and recurring review.
Quick Recap
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