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Yes—you can use AI without handing over sensitive personal information. The safest method is to minimize what you submit, remove or generalize identifiers, use synthetic examples, and keep protected data inside a tool your organization has explicitly approved. Treat prompts, uploaded files, connected-app content, and generated answers as potential places where personal information can be processed or retained.
A “private,” “temporary,” or “history off” label is not a universal guarantee. Before entering anything sensitive, check the service’s current rules for model training, human review, vendor access, retention, deletion, and safety processing.
What counts as sensitive information in an AI prompt?
Information is sensitive when exposure could harm a person, violate a duty of confidentiality, enable fraud, or breach a law or workplace rule. The obvious examples are passwords and identity numbers, but context matters: a rare medical condition, a small-town address, an employee grievance, or a customer record can identify someone even without a name.
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- Prompts: questions, pasted emails, case notes, spreadsheets, code comments, and conversation history.
- Uploads: documents, images, audio, screenshots, PDFs, and exported databases.
- Connected services: cloud drives, mailboxes, calendars, customer systems, browser extensions, and plug-ins that let an AI system retrieve more data.
- Outputs: an answer can reproduce, infer, or combine personal details and may be stored in your account or shared with colleagues.
Public accessibility is not permission to submit. A name found on a public website can still be inappropriate in a prompt if the task does not require it or if combining it with other details would create a new privacy risk.
Can I use AI without sharing personal information?
Usually, yes. First decide whether the task needs any personal information at all. A request to summarize a policy, explain a programming error, draft a generic customer-service response, or brainstorm interview questions can normally be completed with fictional or generalized data.
When a task genuinely depends on protected information, do not move it to a public consumer chatbot by default. Use the organization’s approved channel, confirm that the tool is authorized for the data classification, and follow the organization’s privacy, security, records-retention, and incident-reporting rules.
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A minimum-information workflow
Use this sequence every time you prepare an AI request. It is faster than trying to undo an unnecessary disclosure.
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- Define the result. Write down the exact output you need: a summary, a formula, a classification, a rewrite, or a set of options. Remove details that do not affect that result.
- Classify the input. Decide whether it contains personal, health, financial, employment, customer, authentication, regulated, or confidential business information. If you cannot classify it, treat it as sensitive until your privacy or security contact advises otherwise.
- Reduce the data. Keep only the fields, sentences, rows, or image regions necessary for the task. Do not upload an entire file when a short excerpt or a hand-written description will do.
- Replace identities. Substitute roles such as “customer A,” “patient B,” “employee C,” or “supplier D” for names and usernames. Remove account numbers, email addresses, phone numbers, identification numbers, URLs containing tokens, and internal ticket IDs.
- Generalize unique clues. Broaden exact dates, locations, job titles, ages, and rare events when precision is not needed. “A 42-year-old surgeon in a village of 800 people” may identify someone; “an adult medical professional in a rural area” may not.
- Use synthetic data. Create fictional records that preserve the structure and edge cases of the real task. Synthetic values are preferable to lightly edited real records when a realistic example is enough.
- Check for re-identification. Read the prompt as an outsider. Could several harmless-looking details be combined to identify a person? Remove or change combinations that create that possibility.
- Verify the destination. Confirm that the account, workspace, browser extension, plug-in, and connected storage are the intended ones. A personal account should not be assumed to have workplace protections.
- Review the output. Check whether the answer has reproduced, inferred, or invented personal details before copying it into another system or sending it to someone else.
How to remove identifiers without ruining the task
De-identification should preserve what the model needs to reason about while removing what it does not. The following patterns are safer starting points, not a guarantee that a dataset is anonymous.
| Real detail | Safer substitute | When to retain precision |
|---|---|---|
| “Maria Chen, maria.chen@example.com” | “Customer A” | Only if the exact identity is essential and the approved tool permits it. |
| “Account 8841-2399-7710” | “Account ending in 7710” or “Account X” | Keep a suffix only when the task specifically requires matching; otherwise remove it. |
| “March 14, 2026, 09:17 UTC” | “Mid-March 2026” or “Day 1” | Retain exact timestamps only for a time-dependent technical diagnosis in an approved environment. |
| “17 Oak Street, Halifax” | “A residential address in Atlantic Canada” | Use an exact address only when location is the subject and the approved workflow allows it. |
| “Type 1 diabetes diagnosed at age 9” | “A chronic condition diagnosed in childhood” | Keep clinical detail only for an authorized clinical, research, or administrative purpose. |
| “The only left-handed patent lawyer in a 2,000-person town” | “A professional in a small community” | Preserve rare attributes only when they are necessary and the data is protected. |
| Real customer transactions | Fictional rows with the same columns, ranges, and error patterns | Use real rows only in a security-assessed system approved for that classification. |
Masking a name is not enough if the remaining combination is unique. A prompt that includes an employer, exact appointment date, rare diagnosis, and small location can identify a person even after direct identifiers are removed. The Office of the Privacy Commissioner of Canada recommends anonymized, synthetic, or de-identified data when personal information is not needed, and anonymized or de-identified prompts where possible and reasonable.
Information that should not go into a public AI tool
Unless a documented policy explicitly authorizes the service, keep these out of publicly accessible AI platforms:
- Passwords, passphrases, authentication codes, recovery answers, session cookies, API tokens, private keys, and seed phrases.
- Government identification numbers, passport or driver’s-licence details, tax identifiers, and full payment-card or bank-account information.
- Medical records, diagnoses tied to an identifiable person, insurance information, and protected health information.
- Employment investigations, performance records, accommodation requests, disciplinary files, and unannounced personnel decisions.
- Customer or student records, confidential contracts, non-public financial results, trade secrets, source code containing credentials, and security incident details.
- Images or recordings that reveal faces, voices, badges, screens, home interiors, documents, or location metadata when those details are not needed.
If a tool asks for a secret to troubleshoot it, replace the secret with a clearly marked dummy value and describe the format or error instead.
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How to evaluate a service’s privacy controls
Provider settings change, and similar labels can mean different things. Read the current privacy documentation for the exact product, account type, region, and mode you are using. Answer these questions before submitting sensitive material:
- Model improvement: Are prompts, files, feedback, or outputs used to train or improve models? Does turning off an activity setting apply to future data only?
- Human and vendor access: Can employees, reviewers, contractors, or service providers inspect conversations for quality, abuse prevention, or support?
- Retention: How long are chats, uploads, logs, backups, and safety records kept? Are there exceptions for legal, security, or operational purposes?
- Deletion: What does deleting a chat remove, and what remains in backups or de-identified datasets? Is there a separate deletion request for files or account data?
- Temporary modes: Does “temporary” prevent training, history, review, or only display in your account? What exceptions are stated?
- Connected data: What happens to information retrieved from mail, drives, calendars, or other applications, and can those connections be revoked?
- Account and region: Do consumer, school, government, and enterprise accounts have different terms? Are data locations or transfer rules relevant to your jurisdiction?
The Canadian privacy regulator advises transparency about primary and secondary uses, including model training, and appropriate retention schedules for training data, prompts, and outputs. A setting that limits one use does not necessarily stop all processing, logging, safety checks, or retention.
What Gemini Apps’ published settings illustrate
Google’s Gemini Apps Privacy Hub is a useful example of why labels must be read precisely. These figures describe Gemini Apps as stated on the page accessed October 13, 2026; they should not be generalized to other services.
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|---|---|---|
| Some chats reviewed by human reviewers | A subset may be reviewed; reviewed chats are retained for up to three years. | Do not submit sensitive content merely because a chat is not public. Human review and long retention can still apply. |
| Keep activity turned off | Future chats are not reviewed to improve Google services, but Google says chats may still be used to respond and to protect Google, users, and the public. | Turning off activity changes specified uses; it does not mean zero processing or zero retention. |
| Temporary chats | Chats are retained with the account for 72 hours. Google says temporary chats are not used to train its AI models. | Temporary does not mean immediate deletion. The 72-hour period and stated exceptions still matter. |
| Keep activity off with no feedback | Future chats are not used to improve models, according to Google’s stated policy. | Read the conditions carefully; submitting feedback or using another mode can change the treatment. |
These controls reduce particular uses; they do not turn an unapproved public service into a regulated records system. Recheck the provider’s current wording before relying on a setting for a high-risk task.
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Public tools versus organization-approved tools
There is no single “safe AI” category. The relevant question is whether the specific tool and account are approved for the specific information.
| Decision factor | Public consumer tool | Organization-approved or security-assessed tool |
|---|---|---|
| Permitted information | Usually limited to public, non-sensitive, or suitably de-identified material under your policy. | Defined by the organization’s data classification, contract, and security assessment. |
| Training and improvement | May depend on account settings, feedback, product mode, and current provider terms. | Should be documented in the agreement and configuration; verify rather than assume. |
| Human or vendor access | May include reviewers or service providers for quality, safety, or support. | Access should be controlled, logged, and limited by the organization’s requirements. |
| Retention and deletion | Consumer history, temporary-mode rules, backups, and legal exceptions vary. | Retention schedules, deletion procedures, and records obligations can be defined contractually. |
| Incident response | You may have only the provider’s account controls and support channel. | The organization can apply its breach process, monitoring, access revocation, and legal review. |
The Australian Government Digital Transformation Agency describes a risk-based approach that uses security-assessed enterprise solutions for classified information. That is agency guidance, not an endorsement of a particular consumer product. For work, ask your security or privacy team which tool, account, data class, and workflow are approved.
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Government of Canada guidance
The Government of Canada’s Guide on the use of generative artificial intelligence tells federal institutions: “Don’t enter sensitive or personal information into any tools not managed by the GC.” Its page Generative AI in your daily work, dated September 15, 2026, summarizes the advice as: “Use public tools with unclassified data only.” These statements are guidance for the applicable federal audience, not a universal law for every person or organization.
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CMS guidance
The Centers for Medicare & Medicaid Services says not to put personally identifiable information, protected health information, or other sensitive data into publicly accessible AI platforms. In its agency context, CMS also advises promptly reporting suspected or verified privacy breaches and following applicable policies and laws.
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The Office of the Privacy Commissioner of Canada states that generative AI remains subject to applicable privacy laws in Canada. Its 2026 overview of a conditional resolution concerning OpenAI describes commitments and mitigations in that specific investigation; it is not a general guarantee about ChatGPT prompts, other jurisdictions, or other providers. Apply the law, contractual duties, and organizational rules that govern your situation.
What to do if you pasted sensitive information by mistake
Act promptly, but do not assume that deleting the visible conversation reverses processing already completed.
- Stop adding data. Do not continue the conversation, submit feedback, or connect more accounts while you assess the exposure.
- Record the facts privately. Note the service, account, time, content categories, files, recipients, and relevant settings. Avoid copying the sensitive material into another unapproved system.
- Use the provider’s controls. Delete the conversation or upload, revoke connected applications, turn off sharing links, and submit the provider’s documented privacy or deletion request if appropriate. Read what each action actually removes.
- Protect compromised credentials. If a password, token, private key, or authentication code was included, change or revoke it immediately through the legitimate service—not through the chatbot.
- Report workplace or regulated disclosures. Contact your employer’s privacy, security, compliance, or incident-response channel immediately and follow its policy. CMS guidance specifically calls for reporting suspected or verified privacy breaches in its agency context.
- Follow legal and contractual instructions. A manager, privacy officer, regulator, insurer, or affected person may need to be notified. Do not make promises about deletion or risk before the responsible team assesses the event.
- Preserve required evidence. Keep the incident record your policy requires, while limiting further distribution of the sensitive content.
Deleting a chat can remove what you see in the account without removing backups, safety logs, reviewer copies, or processing that already occurred. Only the provider’s current documentation and your organization’s incident team can establish what remains.
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- Necessity: Can the task be completed with a description, a short excerpt, or a fictional example?
- Identifiability: Have names, numbers, exact addresses, dates, rare details, metadata, and combinations that enable re-identification been removed?
- Secrets: Are passwords, codes, keys, tokens, and recovery information absent?
- Authorization: Is this exact tool, account, plug-in, and connection approved for this data classification?
- Provider terms: Do you understand training, review, retention, deletion, temporary-mode exceptions, and connected-data handling?
- Downstream use: Could the output expose a person or be copied into another system? Have you planned a human review?
- Incident path: Do you know whom to contact if the wrong information is submitted?
If any answer is uncertain, stop and ask the relevant privacy or security owner. The safest prompt is often the one that describes the problem without transmitting the underlying record.
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