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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsNever 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. In practice, that means keeping credentials, personal and regulated records, confidential business material, and official government information out of public AI applications. Treat every prompt, attachment, pasted log and connected data source as a disclosure of the information it contains.
The rule to apply before every prompt
Ask whether the information is already public and whether you would accept the tool provider, its staff, contractors or an unauthorized person seeing it. If the answer is no, do not submit it to a public chatbot or API. A familiar interface or well-known provider does not by itself establish that your data is private, deleted, isolated or excluded from model training.
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UK government guidance puts the principle plainly: “You should never put sensitive information or personal data into these tools.” That is a rule for public services. A separately contracted enterprise deployment may permit specific data classes, but only after your organization has approved the service and verified its controls.
Credentials and secrets: never paste them
Passwords, API keys, access tokens, private keys and connection strings can grant direct access to systems. They do not become safe because you put them in a question, an error message or a code block. Microsoft security guidance states: “Never paste API keys, passwords, or connection strings into a prompt.”
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Keep these items out of prompts and uploads
- Account passwords, password-reset links and one-time codes.
- Cloud access keys, API tokens, OAuth refresh tokens and service-account credentials.
- SSH private keys, certificate private keys, signing keys and wallet seed phrases.
- Database URLs containing usernames or passwords, connection strings and webhook secrets.
- Session cookies, authorization headers, internal repository tokens and secrets stored in configuration files.
If an AI assistant needs to explain an error, replace the secret with a clearly fake value and remove surrounding identifiers that could reveal the system. If a real credential has already been submitted, revoke or rotate it immediately, review access logs and notify the system owner. Deleting the conversation is not a substitute for changing the credential.
Personal and sensitive personal data
Do not submit information that identifies, describes or can be linked back to a person unless the exact tool and use have been approved for that data. This includes another person’s information even when you are authorized to handle it at work. CMS guidance lists personally identifiable information (PII), protected health information (PHI), financial, health, vendor, procurement, evaluation, draft-policy, proprietary and business information among material that must not be disclosed to publicly accessible AI platforms.
Examples that should stay out of public AI tools
- Names combined with addresses, phone numbers, email addresses, account numbers or identification numbers.
- Employee files, performance reviews, disciplinary records, compensation and recruiting notes.
- Patient histories, diagnoses, prescriptions, test results, insurance details and appointment data.
- Bank, card, tax, loan, payroll and investment information.
- Biometric data, precise location, photographs used for identification and private messages.
- Information about children, victims, witnesses, customers or people who have not consented to disclosure.
Privacy obligations can apply even when the information is not obviously sensitive. A customer-support transcript, for example, may combine a name, order number, location and unusual complaint into a record that identifies a person.
Confidential business information
Keep internal material out of an unapproved public service. Microsoft advises developers to understand their organization’s policy before sending internal systems code to an external AI service. The same principle applies to any information whose value depends on secrecy or whose disclosure would breach a contract.
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Material commonly covered by confidentiality rules
- Source code, unreleased features, proprietary algorithms, architecture diagrams and security designs.
- Trade secrets, formulas, manufacturing methods, pricing models and non-public research.
- Product roadmaps, launch plans, acquisition discussions and board or investor material.
- Contracts, negotiation positions, legal advice, internal investigations and incident reports.
- Supplier quotes, procurement files, customer lists, vendor terms and evaluation scores.
- Draft policies, internal correspondence, meeting notes and documents marked confidential.
A prompt can disclose more than the text you intentionally paste. Attachments, conversation history, saved memory, retrieval connectors and tool calls may expose related context. Before using an assistant inside a work account, identify what content it can retrieve and whether administrators, support personnel or subprocessors can access logs.
Official, classified and government-sensitive information
Do not put official, classified, export-controlled or otherwise government-sensitive information into a public AI application unless it is already public or 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.”
This restriction can apply to routine-looking material. Internal correspondence, draft ministerial or agency documents, procurement details, personal data held by a public body and information subject to export controls may all require a controlled environment. Follow the classification and handling rules that apply to your organization rather than making an individual judgment based on how harmless a passage appears.
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Why a prompt is a data disclosure
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 be revealed to unauthorized personnel. Depending on the service, prompts and outputs may be retained for abuse monitoring, troubleshooting, analytics, support or product improvement. Connected tools can also copy information into logs, indexes, caches or downstream systems.
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Microsoft describes sensitive-information disclosure as exposure of confidential, internal or private information and recommends policy enforcement, approved enterprise deployments and auditing of prompts, responses, memory events, retrieval events and tool outputs. A provider’s privacy feature is not a universal guarantee: retention, access, residency, training use and deletion can differ by product, account type and contract.
OpenAI’s usage policy requires respect for people’s privacy and disallows attempts to aggregate, monitor, profile or distribute private or sensitive information without authorization. That provider rule does not replace an employer’s policy, a confidentiality agreement, privacy law, professional-secrecy duty or sector regulation.
Public tools and approved enterprise deployments are different
“AI tool” is not one privacy category. A free consumer chatbot, an API called from your own application and an enterprise service under a data-processing agreement can have different technical and contractual protections. Some enterprise deployments offer tenant controls, identity management, audit logs, regional processing and settings that prevent customer content from being used for model training. Those features must be verified for the exact product, plan and configuration; do not assume they exist.
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| Control to verify | Question to answer before submitting non-public data |
|---|---|
| Permitted classifications | Which data classes—public, internal, confidential, personal or regulated—does the organization approve? |
| Training use | Are prompts, outputs, files or feedback used to train or improve a model, and can that use be disabled contractually? |
| Retention and deletion | How long are content and metadata retained, and can deletion be enforced across backups and support systems? |
| Human and vendor access | Who can view logs or content for support, safety review or administration, and under what authorization? |
| Isolation and access | Are tenants separated, and are identity, role, least-privilege and administrator controls available? |
| Residency and subprocessors | Where is data processed and stored, and which subprocessors can handle it? |
| Auditability | Can your organization review prompts, responses, retrievals, memory events and tool outputs? |
| Incident response | What notification, investigation and recovery commitments apply if data is exposed? |
| Contractual protection | Is there an appropriate data-processing agreement, confidentiality commitment and security schedule? |
If your security, privacy or legal owner cannot answer these questions, treat the service as unapproved for sensitive content. Commercial licensing alone is not proof that a deployment is suitable.
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Removing a name is not automatically anonymization
Replacing “Jane Smith” with “Person A” may leave enough information to identify the person. NIST warns that tools that merely mask personal information may not provide sufficient de-identification. Direct identifiers, quasi-identifiers and combinations of ordinary facts can enable re-identification.
Quasi-identifiers that can reveal someone
- An unusual job title at a small employer.
- A distinctive date of birth, event date or treatment timeline.
- A precise location, rare condition, unique complaint or small demographic group.
- A combination of department, salary band, dates and project details.
Use the minimum information needed for the task. Prefer synthetic examples, broad ranges, aggregation and generated sample records. Pseudonymization can reduce casual exposure but does not remove the need for access controls. For high-stakes use, have a privacy or security reviewer validate the transformation and consider formal methods such as differential privacy where appropriate.
A five-question check before you press Send
- Could disclosure harm someone? Consider a person, customer, patient, employee, supplier, partner or your employer.
- Is a rule attached to it? Check confidentiality, privacy, health, financial, employment, legal, export-control or government requirements.
- Does it contain a secret? Look for anything that could grant access or prove identity.
- Is this exact deployment approved? Confirm the service, account, configuration and data class—not merely the provider’s name.
- Can the task use safer input? Replace real data with public, synthetic, aggregated or fully de-identified material.
If any answer raises doubt, stop. Remove or replace the sensitive material and ask your privacy, security, legal or data-governance owner for an approved path.
Safer ways to ask an AI tool for help
| Risky input | Safer alternative |
|---|---|
| A production error containing a live token and customer email | Paste the error message after removing tokens, names, addresses and unique identifiers; use a fabricated token of the same format. |
| An employee performance review | Ask for a generic review rubric or use a synthetic profile with no real dates, roles or incidents. |
| A contract under negotiation | Ask about a publicly available contract concept, or provide a fictional clause that preserves no party-specific terms. |
| A patient or customer case | Use an invented scenario or aggregated statistics that cannot be linked to an individual. |
| Internal source code | Describe the algorithm in abstract terms or create a minimal reproduction with invented names, data and credentials. |
Redaction should happen before the text reaches the service. Do not paste the original into a prompt asking the model to “remove the private parts”; the initial submission has already disclosed it.
If sensitive data has already been submitted
- Contain access. Revoke or rotate exposed passwords, tokens, keys and certificates. Disable integrations that could retrieve more data.
- Preserve facts. Record what was sent, when, from which account and which conversation or workspace was involved.
- Report promptly. Notify your security, privacy, legal or incident-response contact according to your organization’s procedure.
- Request deletion through the approved channel. Ask the provider what content, logs, backups and subprocessors are covered; do not assume deletion is complete.
- Assess obligations. Determine whether customers, employees, regulators, partners or law-enforcement authorities must be notified.
- Prevent recurrence. Add data-loss-prevention rules, prompt warnings, secret scanning, access controls and training where appropriate.
Changing the wording of a later prompt does not undo the first disclosure. Treat an exposed credential as compromised and a regulated record as a potential incident until your responsible team determines otherwise.
What organizations should require
Organizations that permit AI use should publish an approved-tools list tied to data classifications, not a blanket statement that “AI is allowed.” The policy should identify prohibited inputs, permitted enterprise environments, retention and training settings, review responsibilities, incident reporting and records-management requirements.
- Use identity-based access and least privilege for AI workspaces and connectors.
- Scan prompts, files and source repositories for secrets before submission.
- Log access and retrieval events while limiting who can read those logs.
- Review memory, plugins, agents and external actions as separate risk surfaces.
- Require human review for legal, medical, employment, financial, safety and government decisions.
- Reassess providers when terms, models, regions, subprocessors or account settings change.
These controls reduce exposure; they do not make every use safe. The data owner remains responsible for confirming that a particular task is permitted.
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Keep credentials, personal and regulated records, confidential code and business plans, and official or classified information out of public AI tools. Names removed from a document do not guarantee anonymity. Use synthetic or public examples whenever possible, and submit non-public data only through an explicitly approved deployment whose retention, access, training, residency and deletion controls have been verified.
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