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Keep sensitive or identifying information out of public AI chatbot prompts. Before you paste text or upload a file, ask whether the task can use public information or a fictional example. If real data is necessary, minimize it, remove direct identifiers, change contextual details, verify the service’s privacy controls, and use only a workplace tool that your organization has approved for that data classification.
A chat window is not a confidentiality guarantee. Depending on the service, submitted content may be stored, accessed by the provider or administrators, used to improve a system, used to profile people, or disclosed through the service. The exact handling differs by product, account type, settings, contract and law, so check the current terms and privacy notice for the specific tool.
The safest default: do not put sensitive data in a public chatbot
Publicly available generative-AI services are designed for broad consumer access, not automatically for confidential work, regulated records or another person’s private life. The Office of the Privacy Commissioner of Canada advises consumers to “do not share sensitive or identifiable personal information” when using AI chatbots (Office of the Privacy Commissioner of Canada guidance).
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That includes information that identifies you or someone else directly or indirectly:
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- Names, home and work addresses, email addresses and telephone numbers.
- Account, customer, patient, employee, case, passport, tax and government-identification numbers.
- Passwords, authentication codes, recovery questions, API keys and private cryptographic material.
- Medical, mental-health, biometric, genetic, financial, employment, legal and educational records.
- Confidential business plans, source code, contracts, unreleased research, incident reports and customer data.
- Photos, recordings or documents that reveal a person’s identity, location, health, relationships or other private facts.
If the answer can be produced from public facts, use those facts. If you need help with a private scenario, describe a generic version instead of supplying the real record.
Why a prompt can create more than one privacy risk
Submitting information creates a data-handling chain that may extend beyond the answer shown on screen. Canadian and Australian privacy regulators identify several distinct possibilities:
- Collection and retention: the provider may keep prompts, uploaded files, conversation history, technical logs or backups for periods described in its terms.
- Human or administrator access: authorized staff, contractors, account administrators or security teams may be able to access content for operations, support, abuse prevention or investigation.
- Model improvement: some services may use prompts or files to train or improve systems, while others offer controls or contracts that limit this use.
- Profiling or decisions: information can contribute to inferences, profiles or decisions about a user or another person.
- Disclosure and onward use: content may be processed by suppliers, exposed through a compromised account, or reproduced in an answer to another user if safeguards fail.
- Loss of control: once information is entered, tracking every copy, derived output and backup can be difficult; removal may be difficult or impossible.
These are possible risks, not a claim that every chatbot handles every prompt in every way. Product configuration, account type, provider policy and applicable law determine what actually happens. The Australian Information Commissioner explains the broader difficulty of tracking or controlling personal information after it is input into a commercially available AI product (OAIC guidance).
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1. Pause and test whether real data is necessary
Write the task in abstract terms first. Instead of pasting a customer’s complaint, ask for a response structure for “a delayed shipment complaint.” Instead of uploading a medical note, ask for a plain-language explanation of a fictional note with the same general features. If a public source, a blank template or a manually written example will work, use it.
2. Identify the data classification and the people affected
Decide whose information is involved and how serious the consequences of disclosure would be. Personal information about customers, patients, employees, children, clients or research participants deserves the same caution as your own information. Workplace classifications such as public, internal, confidential, protected or restricted are organization-specific; consult the policy rather than guessing.
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3. Minimize the prompt to the smallest useful set
Remove everything that does not change the answer. A request to summarize a contract may need a clause, not the parties’ names, addresses, signatures, account numbers and full document history. A debugging question may need a short error and a synthetic code sample, not production logs containing user IDs and tokens.
4. Redact direct identifiers before copying
Delete names, contact details, exact addresses, account numbers, case identifiers, URLs containing identifiers, faces, signatures and unique reference numbers. Search the remaining text for identifiers hidden in headers, footers, file metadata, tracked changes, comments and image content. Redaction must remove the underlying value, not merely cover it with a visual box in an editable document.
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Where the task needs realistic context, use aliases and change nonessential details such as dates, locations, job titles, amounts or event sequences. Keep the altered version internally consistent so the chatbot can reason about it, but do not retain a reversible key in the prompt. The Canadian privacy commissioner specifically suggests aliases and changed identifying details as a way to reduce exposure (consumer guidance).
6. Check the destination before sending
Confirm that you are using the intended account, domain, workspace and browser session. A personal account, a public share link or an extension connected to a different service may have different controls from an organization-managed environment. Do not assume that a product’s paid tier, “private” label or chat history display means that prompts are excluded from retention or provider access.
7. Review the current privacy notice and controls
Look for clear answers to these questions before relying on the tool:
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- Are prompts, files or feedback used to train or improve models?
- Can an individual user or an administrator disable that use?
- Who can access content for support, safety, quality review or legal compliance?
- How long are chats, files, logs and backups retained?
- Can you delete a conversation or an uploaded file, and what does deletion cover?
- Where is data processed or stored, and which suppliers receive it?
- What happens to content when an account is closed or a workspace changes ownership?
Training opt-out is only one control. It does not by itself answer retention, human access, security, legal disclosure or accidental-sharing questions. Verify each issue in the applicable terms and privacy documentation. The Government of Canada’s generative-AI guide recommends checking supplier terms and available opt-out controls (Government of Canada guide).
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Do not upload a complete file when a short excerpt is enough. Disable automatic document synchronization or browser extensions that can read pages if your policy does not permit them. Treat the generated answer as potentially containing sensitive material: check it for copied identifiers, confidential facts and incorrect inferences before forwarding, publishing or putting it into another system.
9. Stop if the tool or purpose is not authorized
If you cannot establish that the service is approved for the data involved, do not proceed with the real information. Ask your privacy, security, legal or records team for an approved workflow. A convenient answer is not a reason to bypass a classification rule.
How to remove identifying detail without destroying the task
Good minimization preserves the facts needed to answer the question while breaking unnecessary links to a real person or organization. Use a before-and-after transformation such as this:
| Original material | Safer working version | Why it helps |
|---|---|---|
| “Maria Chen, 14 Oak Street, account 884193, was charged $4,820 on 3 March after a hospital visit.” | “Client A, suburban address, account removed, was charged about $4,800 in early March after a medical appointment.” | Removes direct identifiers and precision that are not needed to draft a billing query. |
| Production log containing email addresses, IP addresses and session tokens. | Short synthetic log with placeholder user IDs and revoked or omitted tokens. | Preserves the error pattern without exposing credentials or customer records. |
| Photograph of a child attached to a school-related question. | A text description of the visual issue, with no image or identifying context. | Avoids biometric and contextual clues that can identify a minor. |
Do not paste the original and altered versions into the same conversation, and do not include a legend that maps aliases back to people. Keep any necessary mapping under your organization’s approved controls, separately from the chatbot.
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Public consumer chatbots and approved workplace systems are not interchangeable
Public-sector guidance illustrates the controls an organization may require, but it is not automatically the law for every private person or employer. Your own policy, contract and jurisdiction govern your work. The practical distinction is:
| Question | Public consumer service | Organization-approved system |
|---|---|---|
| Allowed data | Assume only public or deliberately non-sensitive examples unless the provider and your policy say otherwise. | Use only classifications and purposes explicitly approved by the organization. |
| Training use | May depend on account settings and current terms; verify before sending. | Should be defined by contract, configuration and governance, not assumed from the interface. |
| Access | Provider, support personnel or other service components may have access under the terms. | Named provider and administrator access should be documented and controlled. |
| Retention and deletion | History deletion may not remove every copy, backup or downstream use. | Retention schedules, deletion procedures and legal holds should be agreed in advance. |
| Security and location | Data residency, encryption, tenant separation and supplier arrangements may be unclear to a user. | Security review should address residency, access, logging, incident response and supplier risk. |
| Oversight | The user must make the privacy decision at the moment of use. | Policy, training, privacy review, human oversight and monitoring set boundaries before use. |
For example, the Government of Canada’s staff guidance says not to enter sensitive or personal information into tools that the government does not manage and calls for appropriate security classification and approval. Australian Government staff guidance says to assume anything entered into a public generative-AI tool could be made public (Australian Government staff guidance). Victoria’s public-sector guidance similarly limits public tools to publicly available information and requires users to respect the permitted purpose and protective marking of approved tools (Victorian Government guidance).
What an organization should verify before approving a tool
A procurement or privacy review should document the answers rather than relying on marketing language. The following comparison axes combine the due-diligence questions in Canadian, Australian and New South Wales guidance:
| Review area | Questions to document |
|---|---|
| Use case and classification | Which tasks are allowed? Which data classes are prohibited, conditionally allowed or permitted only in a segregated environment? |
| Model-improvement use | Are prompts, files, feedback or outputs used for training? Is the restriction contractual, configurable, or merely a user preference? |
| Access and administration | Which provider personnel, subcontractors and customer administrators can view content? Are access events logged and reviewed? |
| Retention and deletion | What are the default and maximum retention periods? How are backups, exports, deleted chats and account closure handled? |
| Security and residency | Where is information processed and stored? What encryption, isolation, authentication, vulnerability management and incident-notification commitments apply? |
| Privacy and contractual safeguards | Who is the controller or service provider for the relevant data? Are purpose limits, subprocessors, audit rights and deletion obligations in the contract? |
| Human review and accuracy | Who checks generated content before a decision or disclosure? How are harmful, biased or incorrect outputs escalated? |
The OAIC recommends assessing necessity, access, intended use, suitability, testing, human oversight and privacy and security risks before an organization uses personal information with a commercially available AI product (OAIC organizational guidance). New South Wales guidance emphasizes an approved tool, acceptable-use policy, privacy impact assessment and ongoing management of privacy risks (NSW privacy guidance).
Why de-identification is helpful but not a guarantee
Removing a name does not necessarily make a record anonymous. A rare job, exact date, distinctive event, location, writing style, image background or combination of ordinary facts can identify a person when matched with other information. The Canadian privacy commissioner notes that re-identification can remain possible even after information is de-identified (principles for privacy-protective generative AI).
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Use de-identification as risk reduction, not permission to paste anything. Increase the protection by generalizing dates and locations, removing unique narratives, shortening documents, excluding images and separating any re-identification key. For high-impact or regulated use, obtain a formal privacy and security assessment rather than deciding that a text is “anonymous” by inspection.
Special caution for other people’s information
You may be able to choose what happens to your own information, but you may have no authority to disclose someone else’s. Do not use a chatbot to process a colleague’s performance issue, a customer’s complaint, a patient’s record, a student’s work, a witness statement or a family member’s image merely because you can access it. Children and minors warrant particular care; avoid entering their personal information or images unless an approved purpose, legal basis and protective control are established.
In Australia, whether an organization’s input of personal information is a use or disclosure under the Privacy Act can depend on whether the information remains under the organization’s effective control. That jurisdiction-specific question is one reason to involve your privacy team instead of making a personal judgment from the chat interface.
If you already submitted sensitive information
- Stop further exposure. Do not continue the conversation, upload related files or repeat the information in a support ticket unless instructed through a secure channel.
- Record what happened. Note the service, account or workspace, approximate time, information categories, files, recipients and relevant settings. Preserve only the minimum evidence needed for an investigation.
- Use the service’s current controls. Follow its documented process for deleting the conversation, removing files, revoking shared links and disabling any available model-improvement setting. Deletion controls may not remove every copy or downstream use, so do not describe the incident as fully erased without confirmation.
- Escalate organizational data immediately. Contact your employer’s privacy, security, legal or incident-response team if the material involved work, customers, regulated records, credentials or another person’s information. They can determine notification, containment and evidence requirements.
- Contain credentials separately. If a password, token, key or recovery code was exposed, revoke and replace it through the normal security process; deleting a chat is not a substitute for credential rotation.
- Review the cause. After containment, change the workflow, permissions, training or approved-tool list that allowed the disclosure. Do not copy the exposed data into a new chatbot to ask how to fix it.
A compact decision test before every prompt
- Necessity: Can I complete this with public facts, a template or synthetic data?
- Minimum: What is the smallest excerpt and least precise context that will work?
- Identity: Have I removed direct and indirect identifiers, metadata and hidden comments?
- Destination: Is this the correct account and an approved tool for this classification?
- Handling: Do I understand training use, access, retention, deletion, residency and supplier terms?
- Authority: Am I allowed to disclose every person’s information included here?
- Consequence: Would I be prepared for the prompt to become public? Australian Government staff guidance uses that assumption as a protective rule.
If any answer is no or unknown, replace the real material with a generic example or stop and obtain the required privacy and security review.
What this guidance cannot promise
No setting makes a public chatbot a universal confidential channel. Turning off training, deleting history or replacing names can lower particular risks, but none automatically resolves retention, administrator access, security incidents, legal process, re-identification or accidental disclosure. Requirements also differ by country, industry, organization and data type. Treat this as practical risk reduction, not legal advice or a guarantee that any chatbot will keep information confidential.
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