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Use the minimum information needed, remove or replace identifying details, verify the current privacy and retention controls for the exact AI service and account, and follow your employer’s or client’s approved-tool rules. If you cannot establish how a service handles the material, do not submit sensitive information to it.
Generative AI can be useful for drafting, summarizing, coding and analysis, but a prompt or uploaded file is still information disclosed to a service. Safe use is therefore a data-handling decision, not just a matter of choosing a good prompt. The safest workflow is to classify the material, minimize it, transform it where possible, check the service and account controls, and stop when authorization or terms are unclear.
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What “safe” AI use actually requires
Sending text, images, audio or files to an AI tool can expose their contents to the provider and to the systems used to operate the service. Privacy risk is broader than whether a provider says it “trains on your data.” The NIST Generative AI Profile (2024) describes risks including memorization of data, disclosure of sensitive information, inference of sensitive facts from seemingly harmless information, and re-identification when data points are combined.
That means deleting a person’s name is not automatically anonymization. A rare job title, exact incident date, small-town location and unusual medical condition may identify someone together. A model may also infer a sensitive characteristic from several ordinary-looking facts. Your goal is not to make a prompt vaguely less personal; it is to remove every detail that does not materially affect the answer.
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There is no universal “private AI” setting that overrides the service’s contract, technical operation, account administration or applicable law. Controls differ by provider, product, plan and geography, and they can change. A request to delete a chat or a switch that disables a particular use should be treated as one control with stated limits, not as a guarantee that no copy was processed or retained.
Classify information before you write a prompt
First decide what kind of information you are handling. Legal definitions vary by jurisdiction and industry, but the following categories should trigger a cautious review. NIST discusses personally identifiable information (PII) and sensitive information, while Federal Trade Commission guidance emphasizes knowing what information you hold and protecting it.
| Information type | Examples | Default treatment |
|---|---|---|
| Direct identifiers | Full name, personal email, phone number, postal address, government ID, account username | Remove or replace with a neutral token unless the identifier is essential and the service is explicitly authorized. |
| Authentication and security data | Passwords, API keys, recovery codes, session cookies, security questions, private encryption keys | Never paste into a general AI tool. Revoke or rotate a credential if it is exposed. |
| Financial information | Bank and card numbers, transaction exports, tax records, loan details, payment disputes | Use a synthetic example or aggregate figures; obtain specific organizational approval for any real data. |
| Health, biometric and highly personal data | Medical records, diagnoses, prescriptions, genetic data, fingerprints, face images, therapy notes | Do not submit unless a qualified organization has approved the exact service and purpose. |
| Private communications | Unpublished messages, legal correspondence, school records, family discussions, customer complaints | Summarize without names and distinctive wording, or keep the material out of the tool. |
| Confidential business information | Source code, roadmaps, pricing models, contracts, security findings, internal reports | Follow the data-classification policy and approved-tool list; public tools are not automatically acceptable. |
| Customer, employee or client data | Support tickets, performance reviews, personnel files, customer exports, case notes | Require the responsible organization’s authorization, contractual safeguards and access controls. |
| Contract- or policy-protected material | Non-disclosure information, regulated records, embargoed research, licensed datasets | Treat the contract or policy as the minimum rule; ask the owner or privacy/security contact before using AI. |
When the classification is uncertain, use the stricter category. Do not decide by yourself that a public tool is acceptable for employer or client information simply because the information seems harmless.
Minimize the prompt: send only what changes the answer
The Federal Trade Commission’s Data Security guidance states the core principle plainly: “collect only what you need, keep it safe, and dispose of it securely.” Applied to AI, that means asking what each detail contributes to the requested output.
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- State the task without the source material first. Ask whether you need a summary, a structure, a calculation, a generic explanation or a transformation. Many tasks can be completed with a description of the problem rather than the original document.
- Remove unnecessary identifiers. Delete names, addresses, email addresses, phone numbers, account numbers, exact dates, precise locations and unique case references when they do not affect the result.
- Generalize details that are useful only in combination. Change “the only pediatric cardiologist in a town of 4,000” to “a specialist in a small community,” or replace an exact salary with a range if the range is sufficient.
- Replace remaining values with placeholders. Use consistent tokens such as
[CLIENT],[DATE],[PRODUCT]and[AMOUNT]. Keep the mapping in a separate, access-controlled document rather than sending it to the AI service. - Use synthetic or fictional examples. For a rubric, query, workflow or code pattern, create representative data that preserves the format and difficulty without preserving real people’s details.
- Check the output before sharing it. A model can reproduce sensitive text you supplied or invent a new combination that reveals it. Remove confidential material from the result before forwarding or storing it.
Example: transform a performance-review request
Instead of uploading a named employee’s complete review, ask for a generic rubric or provide a fictionalized excerpt:
Task: Create a fair performance-feedback rubric for a mid-level project manager.
Context: The employee coordinated a six-month software project, missed two milestones,
and improved stakeholder updates after coaching.
Do not infer or include medical, demographic, family or protected-class information.
Return observable behaviors, evidence fields and a development-plan section.This example removes the person’s identity and asks for a framework rather than exposing a personnel record. Whether even a redacted excerpt is allowed still depends on the employer’s policy and the approved service.
Can I use AI without sharing personal information?
Often, yes. Use a generic question, a fictional scenario or a locally processed document when the task does not require real personal data. You can also ask the tool to explain a method, produce a template or generate code that you will run on your own data.
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“Personal information” is not limited to a name. Combinations of facts can identify a person, and AI systems may infer sensitive information from apparently separate data points. NIST explains these re-identification and inference risks in its Cybersecurity, Privacy, and AI overview, updated July 15, 2026. If a task genuinely needs personal data, use an authorized workflow with documented safeguards rather than assuming that pseudonyms make it safe.
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Check the exact service, account and plan
Before submitting any nonpublic material, read the current terms and privacy documentation for the specific product, account type and plan you will use. Do not rely on a general statement about a company or on a setting described for a different edition.
| Control to verify | Questions to answer | What to record |
|---|---|---|
| Prompt and file retention | How long are prompts, uploads, logs and outputs retained? Are backups or safety copies covered? | Policy URL, effective or access date, retention period and stated exceptions. |
| Model improvement or training | Can submitted content be used to improve models? Is an opt-out available, and does it apply to files, feedback and all users on the account? | Exact scope of the control, not just the switch label. |
| Human review and subprocessors | Can personnel or service providers access content for safety, support, abuse prevention or operations? | Relevant reviewer and subprocessor terms and geographic conditions. |
| Administration and access | Can an administrator restrict sharing, export, plugins, connectors and workspace membership? Are audit logs available? | Account role, enabled integrations and who can retrieve conversations. |
| Deletion | What can be deleted, by whom and when? Are legal, security, backup or abuse-prevention exceptions stated? | Deletion procedure and its documented limits. |
| Contract and jurisdiction | Is there a confidentiality commitment, data-processing agreement or sector-specific term? Where is data processed? | Agreement version, governing region and the data classes it covers. |
| Organizational approval | Is this exact service approved for the classification and purpose? | Approval owner, policy reference and review date. |
NIST identifies transparency limits around training data and privacy impacts in the Generative AI Profile. Federal guidance in Title 15, Chapter 119 (2023 edition) discusses risk-based access to AI services, appropriate use and vendor terms; check current legal and policy status before treating that text as binding on your situation.
If you cannot answer the questions above, do not submit the sensitive material. Ask the provider for clarification or contact your organization’s privacy, security or legal owner.
Use employer and client data only in an approved workflow
For work, customer or client information, authorization comes before convenience. Check the organization’s data-classification policy, approved-tool list and any contract or sector rules. Confirm that the approval covers the exact account, region, features and data type; an approved writing assistant does not necessarily approve file uploads, connectors or custom integrations.
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- Use the organization-managed account rather than a personal account.
- Confirm who can administer the workspace and retrieve conversation history.
- Limit access to the smallest group that needs the result.
- Check vendor confidentiality, privacy, security and deletion commitments.
- Document the intended purpose and the categories of data allowed.
- Escalate uncertainty instead of making an informal exception.
The Federal Trade Commission’s September 2025 AI-use compliance plan provides an agency-specific example: internal use of nonpublic information is conditioned on tools meeting the agency’s privacy and security standards, with controls intended to prevent unauthorized exposure. That policy is not a universal rule, but it illustrates the level of governance an organization may require.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep ordinary account and workspace security in place
Privacy controls cannot compensate for a compromised account. NIST’s AI security and resilience work frames security in terms that include confidentiality, integrity and availability. Apply ordinary protections to AI workspaces:
- Use a unique password and multifactor authentication where available.
- Do not share credentials or leave sensitive conversations in a broadly accessible shared workspace.
- Review members, administrators, connected apps, browser extensions and API keys regularly.
- Disable integrations that do not need access to the conversation or files.
- Keep exported transcripts and generated files in approved storage with appropriate permissions.
- Remove temporary downloads and local copies according to your organization’s retention rules.
- Do not paste secrets into a prompt to debug software; use test credentials and sanitized logs.
The FTC’s Protecting Personal Information: A Guide for Business also recommends taking stock of information, controlling access and reminding personnel about confidentiality and security requirements. It is an older general guide, so verify current legal obligations for your industry and location.
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- Is the content sensitive, confidential or covered by a contract? If no, still minimize it and continue. If yes, go to the next question.
- Does the task require the real details? If no, remove them or use a synthetic example. If yes, continue.
- Is this exact service, account and feature approved for that data? If no or unknown, stop and ask the responsible owner.
- Do the current terms explain retention, model-improvement use, access, subprocessors and deletion? If any material point is unclear, do not submit the data.
- Can you restrict access and preserve an audit trail? If not, use an approved alternative or perform the task without the sensitive material.
Practical examples: safer substitutions
| Task | Risky input | Safer approach |
|---|---|---|
| Draft a customer reply | Full ticket with name, order number, address and payment history | Describe the issue and desired tone; replace the customer with [CUSTOMER] and omit payment and address details. |
| Explain an error log | Production log containing tokens, IP addresses and account IDs | Revoke exposed tokens, remove identifiers and provide a short representative stack trace with synthetic values. |
| Summarize a contract | Unpublished agreement containing names, prices and negotiation history | Ask for a clause checklist using a fictional contract, or obtain approval for a controlled document-review tool. |
| Plan a medical appointment | Named patient record with diagnosis and insurance numbers | Ask general questions about preparing for an appointment without submitting the record; consult a clinician for medical advice. |
| Analyze sales performance | Employee-level export with names and customer contacts | Aggregate by month or team, remove direct identifiers and confirm the analytics tool is approved. |
Common mistakes and how to correct them
“I removed the name, so it is anonymous.”
Correct the assumption by reviewing combinations of dates, locations, roles, rare events and free-text quotations. Generalize or remove details until a person cannot reasonably be singled out for the task’s audience.
“I turned off training, so anything is safe.”
A training preference may address only one use of data. It may not change retention, human review, subprocessors, account administrators, connected tools or legal access. Read the complete current terms for the exact plan.
“The tool is paid, so it is confidential.”
Payment does not establish a confidentiality commitment, regulatory suitability or a particular deletion behavior. Verify contractual and technical controls.
“The request is urgent, so I can use my personal account.”
Urgency is not authorization. Use an approved workflow, redact the material, or ask the responsible security or privacy contact for an emergency procedure.
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Deletion may not retract processing, copies, backups or information already included in an output. Treat deletion as a retention control with documented limits, not an undo button.
What to do if you already sent sensitive information
- Stop further disclosure. Do not continue the conversation or paste additional context.
- Preserve basic facts. Record the account, service, time, files or text sent and who may have accessed the result. Avoid copying the sensitive content into a new system.
- Notify the responsible organization promptly. Use the employer’s or client’s privacy/security incident process, even if you are unsure whether the material was retained.
- Protect exposed credentials. Revoke or rotate passwords, API keys, tokens and recovery codes that appeared in the prompt.
- Review the provider’s current deletion and incident instructions. Submit a request if appropriate, but do not promise that it removes every processed or retained copy.
- Follow applicable notification and legal advice. Whether an event is reportable depends on the information, jurisdiction, contract and organization’s procedures.
The sources cited here do not establish one provider-independent remedy after disclosure. Escalation is the safest response because the organization that owns the data can assess contractual, regulatory and security consequences.
Limits of this guidance
This is general privacy and security guidance, not legal advice and not a certification that any particular AI service is suitable. Requirements can differ by country, state, industry, contract, account type and data subject. Provider practices and interfaces change, so recheck the current official documentation at the time of use. The secure default is simple: minimize the data, use an authorized tool, and do not submit sensitive information when the controls or approval are unclear.
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