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No AI chatbot is automatically private just because its provider calls it secure or offers a privacy setting. Before asking about health, money, legal rights, work, or safety, check where your prompt goes, how long it is kept, whether it may be used for training or reviewed by people, who can access it, and how deletion works. If you cannot verify those details, leave out sensitive information—or use a qualified person instead.
What does “private” mean for an AI chatbot?
“Private” is not a single feature. It is a set of data-handling properties that determine what happens to your prompt, the chatbot’s response, and any files or connected data you provide. A service can protect data in one respect while retaining it or exposing it in another.
For a useful comparison, trace the full path of the information. Does it stay on your device, go directly to an application programming interface (API), or pass through a hosted app? Does the app also send it to retrieval connectors, logging systems, or subprocessors? Then check what is kept, for how long, who can access it, and what deletion actually removes.
Separate these questions rather than treating a privacy label as an answer:
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- Collection and transmission: What information is sent, and to which service or connected tool?
- Retention: How long are prompts, outputs, files, logs, and backups kept?
- Training use: Can customer content be used to train or improve models, and under what conditions?
- Access: Could provider staff, account administrators, or other authorised parties see the content?
- Deletion: Can you delete a conversation, and does that deletion cover related retained systems?
- Deployment and jurisdiction: Where is the model running, and where is information processed or stored?
- Answer reliability: What review is needed before acting on the answer?
These properties vary by exact product and configuration. A company’s general privacy statement does not establish the settings for every chatbot, plan, endpoint, feature, or region it offers.
Can you safely ask an AI about health, finances, legal problems, or work?
For a publicly available chatbot, the cautious choice is not to enter personal or particularly sensitive information. Australia’s Office of the Australian Information Commissioner (OAIC) recommends that organisations avoid entering such information into public generative-AI tools because of the significant and complex privacy risks involved. That advice is especially relevant when a question includes details that could identify you or someone else.
You may still be able to ask a general question without disclosing the underlying case. For instance, ask what kinds of documents are typically relevant to a tenancy dispute rather than describing a named person’s specific dispute, address, and correspondence. A hypothetical or altered scenario can preserve much of the usefulness while reducing the personal information in the prompt.
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For an organisation, do not treat an employee’s account choice as permission to submit customer, patient, employee, or confidential business data. Decide whether the tool and its contract meet the organisation’s obligations before use. CNIL, France’s data-protection authority, says on-premise deployment generally seems more appropriate and secure when a use involves personal data or sensitive or strategic documents, such as material used for retrieval-augmented generation (RAG). That is a deployment consideration, not a guarantee that a locally hosted system is secure.
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What should you check before choosing a chatbot?
Start with the actual product surface you plan to use, not a broad promise about the provider. Record the product name, account tier, endpoint or feature, processing region, and applicable policy version; then verify the data rules for that combination.
Retention and deletion
Find the stated retention period for prompts and responses, and distinguish it from the period for safety or abuse logs, uploaded files, backups, and API objects. Check whether deleting a conversation removes only what is visible in your account or also affects other retained systems. If the provider describes exceptions—such as safety investigations or legal requirements—include them in your assessment.
A “zero data retention” option needs the same scrutiny. Establish which data it covers, which endpoints or features qualify, whether eligibility is restricted, and what exceptions remain. Do not assume that a zero-retention setting on one API path applies to a consumer chat interface, file feature, or connected service.
Training and human access
Look for a direct statement on whether customer content is used for model training, whether that use is on by default or requires permission, and whether the terms differ for an API and a consumer-facing product. Separately check whether content may be reviewed by people for safety, support, or other purposes. “Not used for training” does not by itself answer whether data is retained or who may access it.
Account access and connected data
Consider who can administer the account and see its conversations, logs, files, or connectors. For a work account, an administrator may have different visibility from an individual user; confirm the actual access model rather than assuming chats are personal. If the chatbot can search email, cloud storage, or internal records, its exposure depends on those connections as well as the model provider.
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Location, contract, and operations
Check where information is processed and stored, whether the location is acceptable for your purpose, and what contract governs the service. For local or on-premise systems, also account for who installs updates, controls access, protects backups, monitors activity, responds to incidents, and secures the physical equipment. Moving a model in-house changes who carries those responsibilities; it does not make them disappear.
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Whether the answer can be trusted
Privacy and accuracy are separate. A chatbot may produce an answer that sounds confident but is wrong, biased, or out of date. For decisions affecting health, legal rights, finances, employment, or safety, identify a qualified human who will verify important claims before anyone acts on them. CMS guidance on AI use calls for human oversight, monitoring, and review.
What do the documented Anthropic and OpenAI API policies say?
The cited API documentation describes different retention controls, but those statements apply to their documented API arrangements—not automatically to every product surface from either provider. Read the terms for the precise endpoint and features you intend to use, and verify that the documentation still applies to your account.
| Provider and documented scope | Training use | Retention details | What to verify |
|---|---|---|---|
| Anthropic API documentation | Retained data is not used for model training without express permission. | Conversation content is not retained by default, with a documented exception requiring 30-day retention for Covered Models. Its zero-data-retention arrangement says prompts and responses are not stored at rest after the response, subject to safety and legal exceptions. | Whether the specific model is a Covered Model; whether the account has the stated arrangement; and which safety or legal exceptions apply. |
| OpenAI platform documentation for the cited endpoint | Check the applicable endpoint and account terms; do not infer the rule from a general product label. | Abuse-monitoring logs are retained for up to 30 days by default for that documented endpoint. Eligible customers may use zero-data-retention, subject to limitations. Some API objects have separate deletion behavior. | The exact endpoint, eligibility for zero-data-retention, limitations, and deletion behavior for each API object or feature. |
These are policy descriptions, not universal guarantees across consumer chat products, API endpoints, or other services. A setting or commitment only helps if it applies to the precise data path you use. When the consequences of exposure are serious, preserve the policy version and contract details you relied on so the decision can be revisited if terms change.
Should you use a local or on-premise model instead?
A local model runs on a device or system you control; an on-premise deployment runs within infrastructure operated by your organisation. Either approach can reduce exposure to an external chatbot provider, but only if the information actually stays within that environment and the system is properly managed.
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Local deployment shifts work to the operator: securing access, applying patches, maintaining backups, monitoring use, responding to incidents, and protecting the hardware. It may also involve model downloads and the ongoing task of keeping the system usable. A computer sitting on your desk is not automatically a private AI system if prompts, logs, or connected services still send data elsewhere.
Choose a local or on-premise approach when control over the data path is important and there is a responsible person or team able to operate the system securely. Prefer a managed cloud service only after confirming that its retention, training, access, location, and contract terms fit the information and use case. Neither choice makes generated answers inherently reliable.
How can you reduce the risk when you do use a chatbot?
Minimise what the prompt reveals. The Office of the Privacy Commissioner of Canada advises people to limit personal information shared with chatbots, avoid sensitive or identifiable information, change identifying details, delete history when possible, and review privacy settings.
- Remove direct identifiers. Delete names, account numbers, addresses, exact locations, precise dates, and identifying images unless they are genuinely necessary.
- Change distinctive details. Replace unique facts with plausible alternatives or describe the issue hypothetically when that still allows a useful answer.
- Use only the needed excerpt. Do not upload a whole medical record, contract, email thread, or work file when a short, redacted passage is enough.
- Review the service settings and terms. Check retention, training, access, and deletion for the actual product and feature before sharing anything sensitive.
- Limit connected access. Be deliberate about permissions for files, email, cloud storage, or internal systems; a prompt is not the only information the chatbot may be able to retrieve.
- Plan for verification. Decide who will check the answer before using it in a consequential decision.
Do not treat deleting visible chat history as proof that every copy has been erased. Ask what the service’s deletion process covers and whether separate logs, objects, or backups follow different rules.
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What is the practical decision checklist?
Before entering a sensitive question, work through these checks. If you cannot answer a material question about exposure, retention, or access, omit the sensitive details or do not use that service for the task.
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- Can I remove names, identifiers, account numbers, exact locations, or unique facts without losing the value of the question?
- Does this service retain prompts and outputs, and for how long?
- Are prompts used for training or human review, and can those uses be disabled?
- Who can administer or access the account, logs, files, connectors, and backups?
- Can I delete the conversation, and what retained systems does deletion cover?
- Is the processing location acceptable, and is there an appropriate contract?
- If the answer affects health, legal rights, money, employment, or safety, who will verify it?
Frequently Asked Questions
Does deleting a chatbot conversation erase every copy of it?
Not necessarily. A visible conversation, related API objects, safety logs, and backups may follow different retention or deletion rules, so check the service’s specific deletion terms rather than treating removal from the chat list as proof of complete erasure.
Is a private AI chatbot guaranteed to give correct advice?
No. Privacy controls govern data handling, not the truth or quality of a response; a chatbot can still produce outdated, biased, or incorrect answers.
Can I paste a screenshot if I blur my name?
Only if the remaining image contains no details that identify you or someone else, such as account numbers, addresses, faces, or distinctive records. Cropping to the minimum necessary information is safer than sharing a full screenshot.
Should employees use a personal chatbot account for a work question?
A personal account does not make confidential work information safe to disclose. Follow the organisation’s approved-use rules and use only a tool and workflow cleared for the relevant data.
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