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Do not paste sensitive information into an AI service until you can answer seven questions: are you authorized to disclose it, what is the minimum needed, which exact product and account are you using, whether the content can be used for model improvement, how long it is retained, who can access it, and whether connected tools or subprocessors receive it. If any critical answer is unknown, pause and ask your privacy, security, legal, or data owner—or use a version stripped of identifying and confidential details.
“AI” is not one data-handling environment. A consumer chatbot, an organization-managed workspace, an API endpoint, and an AI feature embedded in another application can have different contracts, settings, retention behavior, and access paths. Evaluate the exact route and feature, not a provider-wide slogan.
Use this rule before you send anything
Classify the material first. Personal information, customer records, health or financial data, source code, trade secrets, legal advice, credentials, unpublished research, and regulated records all deserve more scrutiny than public text. The more damaging an unauthorized disclosure would be, the more you should require written approval and contractual safeguards rather than relying on a default setting.
- Stop if authority is unclear. Identify the owner and the permission, law, contract, consent, or workplace policy that allows disclosure to this service.
- Minimize the payload. Send only what the task requires, with identifiers and unnecessary context removed.
- Verify the route. Record the product surface, account, model, endpoint, feature, integration, and region involved.
- Check separate data questions. Training use, retention, safety monitoring, application storage, human access, and onward sharing are not interchangeable.
- Document the decision. Keep the approved use, configuration, data category, owner, and limits so a later settings change does not silently alter the decision.
1. Establish authority and purpose
Identify whose data it is
Ask whether you are handling your own information, an employee’s information, a customer’s records, a client’s confidential material, or data entrusted by a partner. Ownership and permission may differ. A customer contract can restrict disclosure even when your organization technically controls the file.
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Define the task narrowly
Write down the intended result: for example, “classify these anonymized support themes” is narrower than “review this complete customer export.” A specific purpose makes it easier to remove fields that the model does not need and to verify that the chosen service is approved for that purpose.
Escalate high-risk categories
For regulated, privileged, safety-critical, or highly confidential information, obtain approval from the responsible privacy, security, compliance, or legal owner. That owner should assess the particular service, plan, model, feature, integration, and jurisdiction—not merely approve “AI” in the abstract.
2. Minimize what leaves your control
Remove direct identifiers
Replace names, email addresses, phone numbers, account numbers, street addresses, access tokens, and exact dates with consistent placeholders such as CLIENT_001 or DATE_A. Keep a mapping table outside the AI service if you must restore the originals later, and protect that table separately.
Reduce contextual clues
Redaction is not a guarantee of anonymity. A rare job title, unusual incident, location, timeline, or combination of facts can identify a person when details are combined. Generalize dates, locations, quantities, and narrative details when the task permits. Test whether a colleague who knows the situation could still recognize it.
Send excerpts, not repositories
For summarization, provide the relevant pages or fields instead of an entire folder. For code help, isolate the failing function and replace proprietary names, URLs, keys, and business logic that are unrelated to the defect. Never include passwords, private keys, session cookies, recovery codes, or live credentials; an AI model is not a secrets vault.
Preserve utility deliberately
Over-redaction can make an answer useless. Keep the categories, relationships, and constraints needed for the task, but document which fields were removed and why. If the model needs realistic structure, use synthetic or fabricated examples rather than production records.
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3. Identify the exact service and account
Before uploading, capture the service name, account type, model, endpoint or interface, enabled features, connected applications, and organization that administers the account. Terms can differ between surfaces and between features of the same product.
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| Surface | Questions to answer | Common mistake |
|---|---|---|
| Consumer chatbot | Is the account personal? What are the current training, history, deletion, and review settings? Who controls the account? | Assuming a business or API policy applies to a personal conversation. |
| Organization-managed workspace | Which administrator, contract, identity provider, roles, audit logs, and regional controls apply? Are personal accounts prohibited? | Uploading into a shared workspace without checking member access or export controls. |
| API | Which endpoint and feature are called? What are the default logs, retention controls, abuse monitoring, and application-state behaviors? | Assuming “API” automatically means zero retention. |
| Embedded or connected tool | Which host application, connector, plugin, cloud storage, or subprocessors receive the prompt or file? What permissions do they have? | Checking only the model provider while ignoring the application that sends the data. |
Use the account intended for the approved work. Do not move a confidential file from a managed environment into a personal account merely because the personal interface is easier to use.
4. Check training and improvement use separately
Read the current terms and privacy controls for the exact product and confirm the account’s opt-in or opt-out state. A statement that content is “not used for training” answers only one question. It does not by itself describe retention, safety monitoring, application-state storage, human review, or access by a connected service.
OpenAI’s business-data page describes no-training-by-default for listed business products, while its API documentation separately describes abuse-monitoring logs and approval-based retention controls. Those statements are scoped to the products and configurations named there; they should not be generalized to every consumer conversation, model, endpoint, or embedded feature. Anthropic’s published practices for covered models likewise distinguish consumer plans from specific organizational zero-data-retention configurations and identify model- or route-specific exceptions. Verify the current terms before each high-risk use.
5. Check retention and deletion
Ask what is stored
Determine whether prompts, uploaded files, generated outputs, conversation history, metadata, feedback, and safety records are retained. Ask where they are stored, for how long, and what exceptions apply for security, legal obligations, abuse investigations, backups, or account administration.
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Understand the API example
OpenAI’s current API data-controls documentation, accessed October 13, 2026, states that abuse-monitoring logs may be retained for up to 30 days by default, subject to listed exceptions. It also says Zero Data Retention and Modified Abuse Monitoring require prior approval and that some endpoints or capabilities may still store application state. This is a provider-specific policy period, not a general rule for AI services, and API documentation does not establish the behavior of consumer ChatGPT.
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Test deletion claims
Confirm whether deleting a chat also deletes uploaded files, derived records, logs, backups, or content copied into an integrated application. Check who can request deletion, how quickly it takes, and which records must be kept for legal or security reasons. A “temporary” mode may limit history while still allowing short-term safety or abuse retention.
6. Check access and onward processing
Human and contractor access
Read who may view content for customer support, safety review, abuse investigation, quality assurance, or legal compliance. Determine whether access is limited by need-to-know, logged, time-bound, and subject to confidentiality obligations.
Connected tools and subprocessors
A model may pass data to a search connector, code repository, storage provider, plugin, workflow platform, or other subprocessor. Inspect the permissions granted to each connection and whether the downstream service keeps its own copy. Disable unused connectors and avoid sending a file through a route that has broader access than the original system.
“Third party GAI integrations may give rise to increased intellectual property, data privacy, or information security risks, pointing to the need for clear guidelines for transparency and risk management regarding the collection and use of third-party data for model inputs.”
That warning appears in the National Institute of Standards and Technology’s 2024 Generative AI Profile, in its discussion of third-party considerations. Treat every integration as a separate data flow to map and approve.
7. Check account security and organizational controls
- Require strong, unique authentication and multifactor authentication where available.
- Use role-based access so only the people who need the workspace or project can submit or retrieve sensitive material.
- Check whether administrators can export conversations, files, logs, or audit records, and whether those exports are protected.
- Review sharing links, public workspaces, browser extensions, and local downloads. A correctly configured provider cannot protect a file that a user posts publicly.
- Confirm that employee offboarding, lost-device response, and token rotation cover the AI account and its integrations.
The Federal Trade Commission’s business guide states: “Effective data security starts with assessing what information you have and identifying who has access to it.” Apply that principle before the upload, not only after an incident.
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8. Check contracts, regulation, and jurisdiction
For business use, review the data-processing agreement, confidentiality terms, subprocessor list, breach-notification commitments, audit rights, deletion language, and any sector-specific addendum. Confirm whether the service is permitted for the relevant health, financial, education, legal, export-controlled, or government data.
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The FTC has warned AI companies that they must honor privacy and confidentiality commitments, including statements about training and other data use; misleading claims or material omissions can create enforcement risk. For your organization, that means marketing language is not a substitute for the operative contract and configuration.
9. Keep a written decision record
For workplace use, maintain a short register for each approved route. Include:
- Service, account, model, endpoint, feature, and connected tools.
- Data categories allowed and explicitly prohibited.
- Purpose, owner, approving privacy or security contact, and date.
- Training setting, retention period, deletion process, monitoring, and human-access conditions.
- Authentication, roles, region, contract, and subprocessor requirements.
- Redaction rules, incident contact, and a review date.
Recheck the record when the provider changes models, settings, terms, integrations, or regional processing. A one-time approval can become inaccurate after a product update.
What to do when an answer is unknown
Do not paste the sensitive content while a critical control is uncertain. Ask an approved internal owner for a decision, request the provider’s current documentation for the exact route, or proceed with a materially minimized version containing no identifying or confidential data. If the task cannot be completed without the original information, wait for documented approval rather than treating convenience as authorization.
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How to compare two AI services
When selecting between real services, score each one on the same axes and verify every claim for the exact plan, model, feature, and route:
| Control area | Evidence to collect |
|---|---|
| Training and improvement | Default use, opt-in or opt-out controls, scope of feedback and files, and account-level enforcement. |
| Retention and deletion | Prompt, file, output, metadata, backup, and abuse-log periods; deletion exceptions; approval requirements. |
| Safety monitoring | What is inspected, who can access it, how long records remain, and whether monitoring differs by endpoint. |
| Application state | Conversation history, threads, fine-tuning data, vector stores, caches, and feature-specific storage. |
| Access and processors | Provider staff, contractors, subprocessors, connectors, plugins, cloud regions, and auditability. |
| Organization controls | Single sign-on, multifactor authentication, roles, audit logs, export controls, and administrator visibility. |
| Contract and jurisdiction | Data-processing terms, confidentiality, breach notice, sector addenda, residency, and transfer mechanism. |
Do not rank services on a single “privacy” setting. A no-training commitment can coexist with retention for safety, a connected tool can create a second copy, and a zero-retention option may require approval or exclude particular features.
Practical decisions
Summarizing a customer complaint
Remove the customer’s name, contact details, order number, exact address, and unusual biographical facts. Send only the text needed to identify themes. Confirm that the approved account, retention period, and support-access rules permit customer data.
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Create a minimal reproducible example. Replace secrets, internal hostnames, customer data, proprietary algorithms, and comments that reveal strategy. Verify whether prompts or files are retained and whether a code connector can read more of the repository than intended.
Analyzing a regulated record
Do not rely on redaction alone. Obtain approval from the responsible compliance or legal owner, confirm the contract and processing region, document the exact model route, and use the smallest permitted dataset. If those controls are unavailable, keep the analysis inside an approved environment.
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