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Do not paste confidential material into an AI service until you have identified the exact product and account, verified how it uses and retains data, mapped every connected service, and obtained the approval required for your organization and jurisdiction. A statement such as “not used for training” answers only one question. It does not prove that prompts are never stored, viewed for abuse monitoring, sent to a subprocessor, covered by your contract, or permitted for the data you handle.
Use the checklist below for work documents, source code, customer records, legal material, health information, financial data, trade secrets, and any other information whose disclosure could cause harm.
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Start by identifying the exact AI service and configuration
“The provider” is too broad a description for a security decision. Record the provider, model or product name, account tier, interface, region where applicable, and the tools enabled on that account. A consumer application, an organization-managed workspace, and an application programming interface (API) can have different defaults, contracts, logs, and administrative controls even when they use related models.
Make an inventory before submitting data
- Provider and product name, including the model version if your records require it.
- Account type: personal, team, enterprise, education, developer, or another tier.
- Access method: web or mobile application, managed workspace, API, software development kit, or an internal application built on the service.
- Enabled features such as file uploads, browsing, retrieval, agents, plug-ins, connectors, code execution, or external actions.
- Users, administrators, and groups that can view conversations, files, logs, or billing data.
- Data residency, processing regions, and any organization-specific settings.
OpenAI’s business privacy page names specific business products and its API rather than making one undifferentiated promise for every offering. Anthropic’s retention documentation similarly distinguishes consumer plans from certain zero-retention configurations. Treat a product page as a starting point, then verify the terms and settings for the account you will actually use.
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Separate model training from retention and access
Training use and storage are separate controls. A service can state that customer prompts are not used to train or improve a model while still retaining them for security monitoring, troubleshooting, billing, conversation history, or an application feature. Conversely, a short retention period does not by itself tell you whether data can be used for improvement during that period.
Questions about training and improvement
- Are prompts, uploaded files, generated outputs, feedback, or ratings used to train or improve models?
- Is that use off by default, or can a user or administrator opt in?
- Does the commitment cover the specific product, model, region, and feature you intend to use?
- Can human reviewers or contractors inspect content for quality, safety, or abuse investigations?
- Do settings change when you move from a managed workspace to a personal account or API project?
OpenAI states that data from its named business products and API is not used for training by default. Its API documentation says API data is not used for training unless the customer explicitly opts in. Those are provider-specific commitments, not a universal rule for every AI service or interface. Recheck the current documentation and your organization’s configuration before relying on them.
Questions about retention and deletion
- What is stored: prompts, responses, uploaded files, embeddings, metadata, account identifiers, or application state?
- Where is each category stored, and for what operational purpose?
- What is the default retention period, and can an administrator shorten it?
- How do you delete a conversation, file, project, or account, and when are backups removed?
- Are there legal, fraud-prevention, safety, or abuse-monitoring exceptions to deletion?
- Does deletion cover copies held by connected applications and subprocessors?
OpenAI’s API data-controls documentation says abuse-monitoring logs may contain prompts and responses and are retained for up to 30 days by default. The documentation also describes Modified Abuse Monitoring and Zero Data Retention for qualifying customers. The 30-day figure applies to those documented API abuse-monitoring logs; it is not a universal retention period for every OpenAI product, conversation history, uploaded file, or stored application state. Ask the provider to identify the exact log and feature covered by any retention number.
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Use a two-axis record
| Control | What a “yes” answer means | What it does not establish |
|---|---|---|
| Training or improvement disabled | The provider’s stated model-development use is off for the specified product and configuration. | That prompts are not retained, reviewed for abuse, shared with subprocessors, or accessible to administrators. |
| Short or zero retention | The specified data category is deleted or not stored under the documented conditions. | That the service is approved for your data, secure against account compromise, or exempt from legal holds. |
| Deletion available | You have a documented method to remove the specified record. | That every backup, derived artifact, or third-party copy disappears immediately. |
| Enterprise privacy commitment | A provider has made a product- or contract-specific promise. | That your users enabled the right setting or that the promise covers an unlisted feature. |
Examine security and who can access the material
Privacy language cannot replace an access review. Check how the service protects data in transit and at rest, how users authenticate, and how administrators control access. Ask whether provider employees, support personnel, contractors, or subprocessors can access content, under what circumstances, and whether those accesses are logged.
Controls to verify
- Encryption in transit and at rest, including the scope of any customer-managed key option.
- Single sign-on (SSO), multifactor authentication, session controls, and account recovery.
- Role-based permissions for users, workspaces, projects, files, and billing administrators.
- Audit logs that show sign-ins, exports, sharing, administrative changes, and content access.
- Retention and export controls that administrators can enforce rather than merely recommend.
- Incident-notification commitments, security-contact procedures, and support-access records.
- Independent assurance reports or certifications relevant to your risk, reviewed alongside the underlying scope and date.
OpenAI describes encryption and business access controls. Anthropic lists enterprise SSO, SCIM provisioning, audit logs, and role-based permissions. These are claims to validate against current documentation, your contract, and the settings visible in your tenant. Encryption or certification does not eliminate risks from a stolen account, an over-permissioned administrator, a misconfigured connector, or an unauthorized user.
Map every integration and data flow
An AI model may not be the only party receiving your prompt. File connectors, retrieval systems, agents, plug-ins, external tools, analytics services, and subprocessors can receive all or part of an instruction or attachment. A feature that appears to “search your company files” may copy content into an index, cache, vector database, or third-party system with its own retention policy.
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Draw the path of a sensitive request
- List the text, files, images, code, and metadata sent from the user interface.
- Identify the model endpoint and any regional processing location.
- Record every connector, retrieval index, plug-in, agent tool, and external action invoked.
- Identify subprocessors that host storage, monitoring, support, authentication, or analytics.
- Document what each party stores, for how long, and how deletion propagates.
- Check whether an administrator can disable each integration and prevent users from adding new ones.
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile identifies privacy, information-security, and intellectual-property risks associated with third-party generative-AI integrations. The exact parties and flows depend on your configuration, so a provider’s general privacy page cannot substitute for an integration inventory.
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Check legal, contractual, and internal authorization
Whether a use is lawful or permitted depends on the data, your role, the jurisdiction, and the agreement governing the information. Classify the material before sending it: personal data, health or financial information, regulated records, export-controlled technology, privileged legal work, customer confidential information, source code, credentials, or trade secrets may trigger different rules.
Questions for legal, privacy, and procurement teams
- Does a confidentiality agreement restrict disclosure to an AI provider or its subprocessors?
- Is a data-processing agreement required, and does it identify processing locations and subprocessors?
- Do sector rules, cross-border transfer requirements, records obligations, or legal holds apply?
- Does the provider reserve rights to use content for safety, abuse prevention, or service operation?
- Are generated outputs, uploaded documents, or model feedback covered by intellectual-property terms?
- Has the employer explicitly approved this product, account tier, model, and feature?
The U.S. Federal Trade Commission’s 2024 staff post, “AI Companies: Uphold Your Privacy and Confidentiality Commitments,” states: “Model-as-a-service companies that fail to abide by their privacy commitments to their users and customers, may be liable under the laws enforced by the FTC.” That statement emphasizes the importance of honoring published promises; it does not decide whether your particular disclosure is lawful or contractually allowed.
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The UK Information Commissioner’s Office guidance on security and data minimisation in AI calls for evaluating those controls. The ICO notes that its AI guidance is under review following the Data (Use and Access) Act, so use current UK material for live legal questions rather than treating the guidance as a universal legal conclusion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Minimize and redact before you prompt
The safest sensitive value is the one the service never receives. Decide what the task genuinely requires, then remove unrelated context and direct identifiers.
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- Replace names, email addresses, account numbers, customer IDs, exact locations, and dates of birth with consistent labels such as
Customer_A. - Remove passwords, API keys, private certificates, access tokens, and hidden instructions from files.
- Summarize a long document instead of uploading the entire record when a short excerpt answers the question.
- Use synthetic figures or a fabricated example to test formatting, classification, or code structure.
- Check metadata, tracked changes, comments, filenames, and screenshots for embedded secrets.
- Ask whether the output can be produced from a policy, schema, or placeholder rather than the original content.
Redaction reduces exposure but does not guarantee anonymity. A rare combination of dates, events, job titles, or narrative details can still identify a person or project. Keep the mapping between placeholders and real identities in an approved system, not in the prompt.
Confirm the organization’s approved path
Public vendor pages cannot show whether your employer has approved a service or configured it safely. Use the managed workspace, model endpoint, and feature set named in internal policy. Do not move work to a personal account because the approved interface is inconvenient, and do not enable a connector without checking its data flow.
Before the first production use
- Obtain written approval from the data owner, security or privacy team, and legal or procurement team when required.
- Verify that training, retention, sharing, logging, and regional settings match the approval.
- Assign an owner for access reviews, connector changes, incident response, and deletion requests.
- Test with non-sensitive data to confirm that permissions, exports, and audit logs behave as expected.
- Record the approved purpose, prohibited data classes, model version, and review date.
Recheck after a model change, plan upgrade, new integration, policy update, or change in the type of data being processed. A configuration that was acceptable for a public marketing draft may be unsuitable for a customer complaint or an unreleased product design.
Compare services on the same decision axes
When several services are available, score each one using identical questions rather than relying on a “secure” label.
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|---|---|
| Training and improvement | Default, opt-in settings, covered products, human review, and contractual language. |
| Retention | Separate periods for prompts, files, outputs, logs, embeddings, backups, and application state. |
| Deletion and zero retention | Eligibility, setup steps, exceptions, propagation to backups and subprocessors, and proof of completion. |
| Residency and processing | Regions, transfer mechanisms, customer-selected locations, and subcontractor locations. |
| Encryption and keys | In-transit and at-rest protection, key ownership, rotation, and scope of customer-managed keys. |
| Identity and audit | SSO, multifactor authentication, SCIM, roles, administrative separation, exports, and audit-log coverage. |
| Human and support access | When staff can view content, approval requirements, logging, and confidentiality obligations. |
| Integrations and subprocessors | Data sent to each connector, tool, index, analytics service, and subprocessors’ retention terms. |
| Contract and approval | Data-processing terms, confidentiality language, incident duties, and your organization’s written authorization. |
Distinguish a marketing statement, technical documentation, independent assurance, and a negotiated contractual commitment. They provide different levels of evidence and may cover different products.
Stop or escalate when an answer is unclear
Do not treat an unanswered retention, access, or subprocessor question as permission. Pause the upload and ask the provider or your internal owner for a written answer that names the product, feature, data category, and applicable account setting. If the task can be completed with a redacted or synthetic example, use that while the review is pending. If the material includes credentials or information subject to a legal hold, follow the organization’s incident and records procedures rather than experimenting in a public interface.
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