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Never paste secrets, regulated personal records, confidential business material, or any information you are not authorized to disclose into an AI chatbot. Keep passwords, payment credentials, identity numbers, health and financial records, proprietary code, legal advice, unreleased results, security-incident details and private messages out of unapproved services. Remove unnecessary identifiers, use a synthetic example or short redacted summary, and use an organization-approved tool when sensitive processing is genuinely required.
The no-paste list
Use this as a hard stop before sending text, uploading a file, connecting a drive or granting an agent access.
| Data category | Examples that should stay out of an unapproved chatbot | Why it matters |
|---|---|---|
| Authentication secrets | Passwords, passphrases, one-time codes, recovery codes, private keys, API keys and access tokens | Anyone or any system that obtains the value may be able to sign in or act as you. |
| Payment and banking data | Card numbers, CVV/CVC codes, PINs, bank-login details and information that could complete a transaction | These are transaction-enabling secrets and may be subject to payment-security rules. |
| Identity information | Government identifiers and combinations of name, address, date, employer, role or other facts that identify a person | Personal information can be exposed even when one obvious identifier has been removed. |
| Health and patient records | Diagnoses, treatment notes, prescriptions, test results, insurance details and identifiable appointment information | Health information generally requires a lawful, specifically approved processing path. |
| Financial and customer records | Account histories, tax documents, credit information, customer lists and other personal records | You may lack permission to disclose another person’s data to that service. |
| Confidential business material | Contracts, legal advice, trade secrets, unreleased financial results, internal customer files and proprietary source code | Disclosure can breach confidentiality duties, intellectual-property controls or securities policies. |
| Security-sensitive material | Unpatched vulnerability details, exploit steps, incident reports, credentials found in logs and defensive gaps | More copies and wider access increase the chance of misuse during an active threat. |
| Private communications and media | Private emails, messages, photos or documents whose subjects have not authorized disclosure | The sender may not have the right to repurpose someone else’s content. |
Why credentials and payment data are an absolute “no”
Passwords and authentication tokens
A chatbot is not a password vault. Do not paste a password, passphrase, one-time authentication code, recovery code, private key, API key, session cookie or access token, even if you intend to delete the conversation afterward. The secret can be retained in browser history, exports, logs, screenshots, backups or a connected integration outside the chat window.
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Cardholder and transaction data
Do not enter a primary account number, CVV/CVC, PIN, bank-login details or any combination of data that could authorize a payment. OpenAI’s financial-data guidance states: “Do not use ChatGPT inputs (such as conversations, files, and GPTs) to enter, upload, or share cardholder data as defined by Payment Card Industry Data Security Standard (PCI DSS).” A model does not need the real number to explain a payment error; use a fictitious number with the same format or describe the situation in words.
Personal, health and financial records require an approved workflow
Identifying information is more than a name
Removing a name does not necessarily make a record anonymous. A rare job title, small-town location, exact dates, unusual event and age can identify a person when combined. NIST Special Publication 800-122 defines the protection goal plainly: “PII should be protected from inappropriate access, use, and disclosure.” Treat government identifiers and identifying combinations as protected unless your organization has established a lawful process for that specific service.
Medical and patient information
Do not upload medical records, clinical notes, prescriptions, lab results, insurance information or identifiable appointment details to a general chatbot. Health data can be subject to professional duties, contracts and sector-specific law. If an approved clinical or organizational system is available, verify its permitted purpose, retention period, access controls and contractual terms before use. For general education, describe a fictional patient and omit dates, locations and uncommon details.
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Financial and customer information
Bank statements, tax forms, credit records, customer lists and account histories contain personal data even when the task seems routine. A useful question is not merely “Can the model answer this?” but “Am I authorized to disclose these records to this provider for this purpose?” The FTC notes that model-as-a-service companies may receive confidential internal documents as well as personal, health and financial data, so your organization’s approval and data-processing terms matter.
Confidential work, intellectual property and legal material
Keep confidential contracts, legal advice, merger or acquisition information, unreleased financial results, product roadmaps, trade secrets, proprietary source code, internal customer files and private board or HR material out of an unapproved chatbot. Confidentiality can be lost even when the prompt is well intentioned: a pasted draft may be copied into a transcript, shared with administrators, included in retention backups or exposed through an account that other staff can access.
Source code deserves special care. Replace proprietary functions with a small synthetic example that reproduces the behavior, remove package names and internal URLs, and strip comments that reveal architecture or credentials. For legal text, ask for a general explanation of a fictional clause rather than uploading the signed agreement or legal advice. A non-disclosure agreement, employment policy or client contract may impose stricter rules than your chatbot’s default settings.
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Security incidents and connected agents add another exposure path
Incident and vulnerability details
Do not paste an active incident timeline, unpatched vulnerability, exploit proof, exposed log, internal hostname, defensive gap or compromised credential into a public or unapproved service. Share only the minimum sanitized facts with the incident team and approved tooling. Keep evidence in the organization’s case-management system, where access, chain of custody and retention are controlled.
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When a chatbot can read email, cloud files, web pages or tickets and take actions, third-party content can contain instructions designed to redirect it. OpenAI’s prompt-injection guidance says: “Where possible, limit an agent’s access to only the data it needs to complete a task.” Give an agent the smallest possible data scope, separate read and write permissions, and require a human review before sending messages, changing records, approving payments or deleting files. Treat every retrieved document as untrusted input, not as an instruction from you.
De-identification: how to reduce data without fooling yourself
Start by asking what the task actually needs. If a model must classify a support issue, it usually does not need the customer’s name, full address, account number or exact timestamp. Keep only the fields required to produce the answer, generalize dates and locations, and replace names, IDs and unique events with consistent fictional labels.
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- Prefer a synthetic example: recreate the structure with invented people, numbers and text.
- Use a short summary: describe the relevant facts instead of uploading the original file.
- Redact identifiers: remove names, contact details, account numbers, government IDs, URLs containing tokens and unique case references.
- Check combinations: role, location, age, date and unusual event may identify someone together.
- Preserve only needed precision: use “early May” instead of an exact date and “a regional office” instead of a small town when precision is irrelevant.
Before sending, read the result as an outsider: could a colleague who knows the situation identify the person or company from the remaining details? If yes, reduce or generalize further.
How to use AI safely when the task is legitimate
- Map the data flow. Identify exactly what leaves your control, which provider receives it, which connected services can access it and what actions the system can take.
- Classify the material. Mark credentials, payment data, personal records, health information, confidential business content and security evidence before composing a prompt.
- Minimize and transform. Remove secrets and direct identifiers; replace the original with a synthetic example, summary or redacted excerpt.
- Choose an approved service. Use the tool your organization has authorized for that data class, not whichever chatbot is easiest to open.
- Set the smallest permissions. For agents and connectors, grant access only to required files and functions; keep write, send, purchase and delete capabilities behind confirmation.
- Review the output and action. Check for unintended disclosure, incorrect inferences and unsafe instructions before copying text into a system or approving an action.
- Dispose of working material correctly. Delete temporary redacted files and exports according to your organization’s retention policy.
How to evaluate a chatbot before an approved use
“Enterprise” or “private” in a product name is not enough. Ask the provider and your security or privacy team for documented answers to each axis below.
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| Retention and deletion | How long are prompts, files, outputs and backups retained? Can your organization delete them and verify deletion? |
| Model-improvement use | Are inputs or outputs used to train or improve models, and can that use be disabled contractually and technically? |
| Human and vendor access | Who can view content for support, safety review or administration, and are access events logged? |
| Security controls | Are data encrypted in transit and at rest? Are single sign-on, multifactor authentication, role-based access and key-management controls available? |
| Confidentiality terms | Does the contract define ownership, permitted processing, breach notification and limits on subcontractors? |
| Geography | Where are prompts processed and stored, and do cross-border transfers meet your legal and contractual requirements? |
| Auditability | Can administrators export logs, investigate access and enforce retention, deletion and connector policies? |
| Authorization | Has your organization approved this service for the relevant privacy, security and sector obligations? |
The Information Commissioner’s Office describes a risk-based approach to AI governance; its AI guidance is under review, so check the current version and your jurisdiction’s requirements rather than relying on a generic privacy label.
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If you already pasted sensitive data
- Stop further disclosure. Do not repost the material or share screenshots of the conversation.
- Preserve the relevant facts. Record the service, account, time, data categories and recipients without copying the secret into another chat.
- Rotate exposed credentials. Change passwords, revoke sessions, replace API keys and invalidate recovery codes through the legitimate provider.
- Notify the right team. Follow your organization’s incident process and contact privacy, security, compliance or the data owner as appropriate.
- Request deletion where available. Use the provider’s documented deletion and account controls, while recognizing that backups or legal retention may follow separate schedules.
- Assess affected people and systems. Determine whether customers, patients, employees, payment accounts or production systems require notification or additional protection.
What is not known
There is no authoritative cross-service statistic that gives a single probability or frequency for harm caused by pasting sensitive data into chatbots. Risk depends on the service’s retention, access, training settings, integrations, account security, contractual controls and the sensitivity of the material. Make the decision from those controls and your authorization status, not from an assumed industry-wide percentage.
Practical questions to ask before pressing Send
- Would I be permitted to email this exact content to an outside vendor?
- Does the task require the original record, or will a fictional example or summary work?
- Have I removed every credential, direct identifier, exact location, unique date and unnecessary attachment?
- Is this service approved for the data’s privacy, security and sector requirements?
- If an agent is connected, can it read or change more than this task requires?
- Who will respond if the data is exposed, retained longer than expected or used incorrectly?
The Bottom Line
If a chatbot does not need a secret or identifiable record to complete the task, do not send it. Redact or synthesize first; use an approved, contractually controlled service only when the remaining data and workflow are authorized.
Quick Recap
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