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Never enter non-public information into an AI tool when disclosure could create privacy, security, legal, contractual, safety, or reputational harm—unless your organization has explicitly approved that exact service and data classification. That means keeping credentials, personal and regulated records, confidential business material, private third-party information, and government-sensitive data out of unapproved public AI applications. Treat every prompt, file upload, pasted log, and connected data source as a potential disclosure.
The data that should stay out of AI prompts
The safest default is simple: if the information is not public and someone could be harmed by its exposure, do not submit it to a public or unapproved AI service. The risk applies whether you type the information directly, upload a document, paste an error log, connect a cloud drive, or ask an agent to retrieve it.
Passwords, keys, tokens, and connection details
Credentials are the clearest absolute prohibition. Never paste passwords, API keys, access tokens, private keys, session cookies, one-time codes, database connection strings, cloud credentials, recovery codes, or secret environment variables into a prompt. Microsoft security guidance states: “Never paste API keys, passwords, or connection strings into a prompt.”
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Personal and sensitive personal information
Do not enter information that identifies, describes, or can reasonably be linked to a person unless the deployment is approved for that data. Examples include full names combined with contact details, home addresses, personal email addresses, phone numbers, government identification numbers, dates of birth, employee numbers, biometric information, precise locations, family details, and unique case histories.
Health information, disability details, therapy notes, insurance records, genetic information, financial records, payment details, credit information, tax data, employment evaluations, disciplinary records, and legal matters require particular caution. UK government guidance puts the rule plainly: “You should never put sensitive information or personal data into these tools.”
The restriction also covers someone else’s information. A customer’s support transcript, a patient’s symptoms, an employee’s complaint, or a colleague’s private message does not become acceptable merely because you are trying to summarize it or improve its wording.
Confidential business and technical material
Keep confidential company information out of unapproved public services. This includes source code, proprietary algorithms, internal architecture, trade secrets, unreleased product plans, product road maps, pricing strategy, sales forecasts, acquisition discussions, internal investigations, security findings, contracts, negotiation positions, procurement documents, vendor terms, draft policies, board materials, and confidential correspondence.
Code can contain more than logic. Comments, file paths, database schemas, customer identifiers, internal hostnames, and embedded credentials may reveal how an organization operates. Microsoft advises developers to understand their organization’s policy before sending internal systems code to an external AI service.
CMS guidance identifies personally identifiable information, protected health information, financial, health, vendor, procurement, evaluation, draft-policy, proprietary, and other business information as material that should not be disclosed to publicly accessible AI platforms. A document does not become safe because it is unfinished, marked “draft,” or shared only with a small internal group.
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Official, classified, export-controlled, or government-sensitive information
Do not submit official government information to a public AI application or API unless it is already publicly available or has been cleared for publication. The UK AI Playbook states: “Never enter any official information directly into public AI applications or APIs unless it’s already publicly available or cleared for publication.”
That rule can cover policy drafts, operational details, citizen records, law-enforcement information, security plans, export-controlled technical data, and information marked with a handling restriction. Classification labels are not the only test: information can be sensitive because of its context, combination with other facts, or contractual handling instructions.
Private information received from another person or organization
Confidentiality follows the information, not the person holding it. A supplier’s quotation, a client’s business plan, a lawyer’s correspondence, a research participant’s response, or a partner’s non-disclosure material should not be pasted into a tool simply because you can access it. Check the agreement and your organization’s data-handling rules before using any external processor.
Why entering a prompt is a disclosure
A prompt is not necessarily a private scratchpad. The service may transmit, store, log, moderate, review, retrieve, or otherwise process the text and files you submit. The Department of Energy warns that confidential information entered into a generative-AI tool may be stored or processed by the tool or its provider and could become available to unauthorized personnel.
Potential exposure points include request logs, conversation history, safety-monitoring systems, support tickets, backups, analytics, connected retrieval indexes, agent memory, and third-party subprocessors. A response can also repeat a secret or private detail in a place where a wider audience sees it. Microsoft recommends governing prompts, responses, memory events, retrieval events, and tool outputs through policy enforcement, approved deployments, and auditing.
Privacy safeguards offered by a provider do not cancel your other duties. OpenAI’s usage policy, for example, requires respect for people’s privacy and disallows unauthorized attempts to aggregate, monitor, profile, or distribute private or sensitive information. That provider rule is not a substitute for your employer’s policy, a confidentiality agreement, privacy law, professional secrecy, or a sector-specific regulation.
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Public tools and approved enterprise deployments are different
“AI tool” is not one privacy category. A public consumer chatbot and an enterprise deployment may differ in retention, model-training use, administrator controls, human access, data residency, deletion, support procedures, and contractual liability. You cannot infer the controls from a familiar product name or from the fact that a login is required.
Questions to verify before using sensitive data
Ask your privacy, security, legal, or data-governance owner to confirm all of the following for the exact service, account, region, and configuration:
- Which data classifications are permitted, and which are prohibited?
- Are prompts, uploaded files, outputs, retrieval results, or memory artifacts used to train or improve a model?
- How long are each of those records retained, including backups and abuse-monitoring logs?
- Can provider staff, contractors, support personnel, or subprocessors access content?
- Where is data processed and stored, and which subprocessors handle it?
- Are tenant isolation, role-based access, encryption, and administrator audit logs available?
- Can your organization enforce deletion and verify that it occurred?
- What notification, investigation, and recovery process applies after an incident?
- Is a data-processing agreement or another contractual protection required?
The UK AI Playbook recognizes that a commercially licensed application can have different data-handling terms, but the exception depends on verifying those terms and obtaining organizational approval. Microsoft similarly recommends approved enterprise AI services for sensitive workflows so prompts, outputs, grounding data, and memory artifacts can be governed by tenant controls.
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A decision test before you press Send
Use this short test for every prompt, attachment, integration, and agent action. If any answer creates doubt, stop and remove or replace the sensitive material.
- Consider the harm. Would disclosure harm a person, customer, patient, employee, supplier, partner, or employer?
- Check obligations. Is the information covered by confidentiality, privacy, health, financial, employment, legal, export-control, or government rules?
- Look for secrets. Does it contain a credential, key, token, private endpoint, or other detail that could grant access?
- Identify the deployment. Is this a public service, or has your organization approved this exact deployment for this exact data class?
- Minimize the input. Can the task be completed with public, synthetic, aggregated, or fully de-identified data?
- Get a decision when needed. Ask the responsible privacy, security, legal, or data-governance owner rather than guessing.
Approval must cover the actual workflow. A service permitted for public marketing copy may still be prohibited for source code, customer records, or internal investigations. A team-wide approval may also exclude a personal account, browser extension, plug-in, or connected application.
Why removing a name is not always anonymization
Deleting a name or replacing it with “Customer A” does not automatically make information anonymous. NIST warns that tools which merely mask personal information may not provide sufficient de-identification. A person can be re-identified through quasi-identifiers: a rare job title, unusual date, location, timeline, employer, medical event, or combination of ordinary facts.
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Safer ways to transform data
| Technique | What it does | Important limitation |
|---|---|---|
| Data minimization | Removes fields that are not needed for the task. | Remaining fields can still identify someone when combined. |
| Aggregation | Replaces individual records with totals, ranges, or trends. | Small groups or rare values can still reveal individuals. |
| Pseudonymization | Replaces identifiers with codes or aliases. | The mapping remains sensitive and re-identification may be possible. |
| Synthetic data | Uses invented records that reproduce the structure of a problem. | Poorly generated examples can accidentally resemble real people or secrets. |
| Differential privacy | Adds carefully calibrated noise to limit what can be learned about one record. | Requires appropriate design, parameters, and governance. |
Use invented names, dates, domains, identifiers, and sample values when asking for help with a workflow. Remove exact timestamps, rare combinations, internal URLs, and unnecessary narrative details. For high-stakes use, have a privacy or security reviewer validate the transformation; do not rely on a quick find-and-replace operation.
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Software troubleshooting
Unsafe: pasting a production configuration file containing an API key, database connection string, internal hostnames, and customer identifiers.
Safer: reproduce the error with a small synthetic configuration, replace secrets with clearly fake values, remove hostnames and identifiers, and include only the relevant error message.
Customer support
Unsafe: asking an AI service to summarize a complete ticket containing a customer’s name, address, account number, payment history, and medical explanation.
Safer: summarize an approved, de-identified description such as “A customer in region X reports delayed delivery and requests a replacement,” using only the fields needed for the response.
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Unsafe: requesting a performance-review rewrite with an employee’s name, salary, health condition, disciplinary history, and manager comments.
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Safer: ask for a generic structure or provide invented details that do not map to a real employee.
Legal and procurement work
Unsafe: uploading a draft contract, negotiation position, supplier pricing, or internal investigation notes to a public chatbot.
Safer: ask about general clause structure using a fictional agreement, or use an organization-approved deployment after legal and security approval.
If sensitive information was already submitted
Act quickly and treat the event as a potential data incident. Do not assume that deleting the conversation removes every copy.
- Stop sending additional sensitive material and disconnect any unnecessary integrations.
- Record what was submitted, when, through which account, and which people or systems may be affected.
- Notify your security, privacy, legal, or incident-response contact according to organizational policy.
- Rotate exposed passwords, keys, tokens, certificates, and connection strings; revoke sessions where appropriate.
- Ask the service administrator or provider what retention, deletion, access, and investigation options apply.
- Assess notification, contractual, regulatory, and safeguarding obligations with the responsible owner.
- Preserve relevant evidence without circulating the exposed content more widely.
Changing a password is essential when a credential was exposed, but it does not resolve disclosure of personal records, trade secrets, or regulated information. Those cases require a broader incident assessment.
Controls organizations should put around AI use
Individual caution works best when supported by technical and organizational controls:
- Publish a data-classification policy that names permitted AI uses and prohibited data types.
- Maintain an approved-tool list tied to specific accounts, regions, configurations, and data classes.
- Use single sign-on, role-based access, tenant isolation, and administrator audit logs where available.
- Block or detect secrets and sensitive identifiers before transmission with gateway or data-loss-prevention controls.
- Train staff that uploads, browser extensions, plug-ins, retrieval connectors, and agent tools are also disclosures.
- Review retention, training use, subprocessors, residency, deletion, and incident terms whenever the service or contract changes.
- Test redaction and de-identification procedures with privacy and security specialists.
- Provide a fast approval path so employees do not resort to personal accounts when legitimate work needs AI assistance.
The core rule to remember
Credentials and secrets never belong in a prompt. Personal, health, financial, regulated, confidential business, proprietary technical, and official information should remain out of public AI tools unless the exact deployment is explicitly approved and governed for that data. Public and enterprise services may offer different protections, and removing names alone may not prevent re-identification. When in doubt, minimize the data, substitute synthetic values, and ask the responsible privacy or security owner before submitting anything.
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