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No chatbot is private simply because its provider calls it secure or private. Before asking about health, money, legal rights, work, or another sensitive matter, check what data the exact service collects, where it goes, how long it is kept, whether it can be used for training or reviewed by people, and how deletion works. If those details are unclear, leave out identifying information—or do not enter the sensitive question.
What does “private” mean for an AI chatbot?
Privacy is not a single feature or promise. It is a set of data-handling properties that apply to a particular product, account tier, endpoint, and configuration. A chatbot might keep a conversation briefly for safety monitoring without using it to train a model; another might offer a training opt-out but retain logs. Those are different protections, and neither alone answers every privacy question.
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Assess the whole path your information takes, not just the text box where you type it. A hosted chatbot may send a prompt to a provider, record it in logs, pass information to subprocessors, or access files through connected services. A local system can avoid some third-party exposure, but its operator still has to secure the device, software, network, and stored data.
- Collection and transmission: What information leaves your device, and which service or feature receives it?
- Retention and deletion: Are prompts, responses, logs, files, or backups kept? For how long, and what does deletion cover?
- Training and review: Can content be used to improve models, or seen by people for safety, support, or other purposes?
- Access and security: Who can administer the account or access its conversations, connected files, and logs?
- Location and responsibility: Where is information processed and stored, and who is responsible for operating and protecting the system?
- Answer reliability: Could an inaccurate or biased response cause harm, and who will check it?
These questions matter because the privacy of a conversation and the reliability of its answer are separate issues. A service could handle data under strong controls and still give an incorrect answer.
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Can you safely ask an AI about health, finances, legal problems, or work?
For publicly available chatbots, the safest starting point is not to enter personal or particularly sensitive information. The Australian Office of the Australian Information Commissioner (OAIC) recommends that organisations avoid entering such information into publicly available generative-AI tools because of significant and complex privacy risks.
That recommendation is specifically framed as best practice for organisations. Individuals still have reason to be cautious: an intimate question can become identifiable through details in the prompt even if it contains no name. A distinctive job title, unusual event, exact location, date, or combination of personal facts may reveal who the person is.
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If a general explanation would help, change the scenario so it no longer describes a real person in a uniquely recognizable way. Remove names, account numbers, addresses, exact dates, images, and identifying details about other people. You can often ask about a general process or use altered facts rather than supplying a complete personal history. The Office of the Privacy Commissioner of Canada advises people to limit personal information and avoid sharing sensitive or identifiable information with chatbots; it also recommends changing identifying details, reviewing privacy settings, and deleting history when possible.
Removing a name does not automatically make a prompt anonymous. Consider whether the remaining facts could identify you or someone else, and whether the chatbot needs each detail to answer the question. If the answer depends on a confidential document, private account data, or a highly specific event, a public chatbot may be the wrong place to submit it.
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Bring high-impact answers to a qualified person
Do not treat a chatbot response as the final basis for a health, legal, financial, employment, or safety decision. Verify important claims with a qualified human who can consider the full circumstances and take responsibility for advice. U.S. Centers for Medicare & Medicaid Services guidance on AI use emphasizes human oversight, monitoring, and review; this is a useful guardrail, not a guarantee that every AI tool or answer has been assessed.
What should you check in a chatbot’s privacy terms?
Read the terms for the actual product and feature you plan to use, not just the provider’s general privacy page or a label on its home screen. A provider may offer different controls for consumer chat, business accounts, APIs, or specific endpoints. Record the product name, plan, endpoint or feature, region, and policy version you checked so that the decision remains tied to the right service and terms.
- Identify the data path. Check whether you are using a hosted application, a direct API, a local model, or a service connected to files, search, or other accounts. Look for relevant logging, subprocessors, and what information connectors can make available.
- Find the retention rule. Determine whether prompts and responses are stored, how long logs last, and whether separate objects, files, safety records, backups, or legal holds follow different rules.
- Separate training from storage. Check whether content is used to train or improve models by default, only with an opt-in, or under a contract. A promise not to train on content does not necessarily mean that content is never stored.
- Check human and administrator access. Look for possible access by provider staff, account administrators, or people with access to logs and connected files. Review available controls such as roles, account security, audit logs, and encryption.
- Understand deletion. Find out what a delete action removes, whether it applies to associated data, and whether any retention exceptions remain. Do not assume that removing a conversation from the visible history also removes every retained record.
- Confirm location and contract. Determine whether processing and storage locations meet your requirements and whether the applicable agreement covers the data and use case.
- Decide who checks the answer. If a mistake could affect rights, money, health, employment, or safety, identify the person who will review the output before acting on it.
Encryption, access controls, and deletion tools can be useful protections, but they answer different questions. For example, encryption does not by itself establish that content is not retained or used for training. Evaluate each protection as part of the full data path.
What do published retention policies say about two API options?
Two providers’ API documentation illustrates why privacy claims must be read at endpoint level. These policy details concern documented API arrangements, not every chatbot product or account surface offered by either provider.
| Documented API detail | Anthropic | OpenAI |
|---|---|---|
| Training use | Anthropic says retained data is not used for model training without express permission. | Not stated in the cited platform-controls material; check the applicable endpoint and terms. |
| Default conversation retention | Conversation content is not retained by default, with a documented exception requiring 30-day retention for Covered Models. | Abuse-monitoring logs are retained for up to 30 days by default for the documented endpoint. |
| Zero-data-retention arrangement | Prompts and responses are not stored at rest after the response under the described arrangement; safety and legal exceptions apply. | Eligible customers may use zero-data-retention, subject to limitations. |
| Other data and endpoint differences | Exceptions and the Covered Models rule mean the exact arrangement needs checking. | Some API objects have separate deletion behavior; check the endpoint and feature rather than relying on general marketing language. |
The stated 30-day periods are provider policy durations, not research statistics or guarantees that every record disappears on a particular day. In Anthropic’s case, the documented 30-day retention requirement applies to Covered Models; in OpenAI’s case, up-to-30-day default abuse-monitoring retention applies to the documented endpoint. The terms differ in what they describe, so the figures should not be treated as a like-for-like comparison of all data handling.
Zero-data-retention is also not a universal setting for every customer or product. Anthropic’s described arrangement has safety and legal exceptions, while OpenAI’s is limited to eligible customers and subject to limitations. Before relying on either, confirm that the arrangement applies to your specific use and determine what it covers.
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Is a local or on-premise chatbot more private?
A local or on-premise deployment can reduce exposure to an outside chatbot provider because the system can be operated on infrastructure you control. It does not make data private automatically: the operator becomes responsible for securing accounts, restricting access, patching software, protecting backups, monitoring the service, responding to incidents, and securing the physical system.
France’s data-protection authority, CNIL, says that when a use involves personal data or sensitive or strategic documentation—for example, documentation used for retrieval-augmented generation (RAG)—it generally seems more appropriate and secure to favor an on-premise solution. That is a general direction, not a guarantee that every local installation is safer than every cloud service. A poorly maintained or broadly accessible local deployment can create serious risk of its own.
What changes when you operate the system?
- Third-party exposure may fall, but operating duties rise. You must keep software and models updated, set access permissions, manage monitoring, and plan for incidents.
- Local storage still needs protection. Conversation history, uploaded documents, logs, and backups may contain sensitive material even when processing happens on your own infrastructure.
- Physical and administrative access matter. People who can reach the machine or manage the deployment may be able to access its data, depending on the configuration.
- Model behavior remains a separate risk. Moving processing on-premise does not make answers accurate, current, unbiased, or suitable for professional decisions.
Choose local deployment when you can actually manage those responsibilities and when its data-control benefits fit your use case. Buying or running hardware alone is not proof of privacy; the system’s configuration and day-to-day operation determine what protection it provides.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you make a privacy decision before entering a sensitive prompt?
Use this checklist for the specific conversation, not just the provider in general. If a key answer is unknown, do not treat the unknown as a privacy safeguard.
- Can you remove names, identifiers, account numbers, exact locations, dates, images, or unique facts while keeping the question useful?
- Does this exact service retain prompts and outputs? For how long, and are there separate rules for logs, backups, safety records, or legal requests?
- Are prompts used for training or subject to human review? Can those uses be disabled, and does that control apply to this product and account?
- Who can administer or access the account, conversation history, logs, connected files, and backups?
- Can you delete the conversation, and can you establish what deletion covers in retained systems?
- Are the processing location and applicable contract acceptable for this information?
- If the response affects health, legal rights, money, employment, or safety, who will verify it before you act?
For an organisation, apply these questions to the specific workflow and the information it handles. The OAIC’s advice is to avoid entering personal and particularly sensitive information into publicly available generative-AI tools as a matter of best practice. If a business needs AI to handle that information, it should assess the product, account controls, contracts, and deployment against its own privacy and security requirements rather than assuming a public interface is suitable.
Frequently Asked Questions
Does incognito or private browsing make an AI chatbot private?
A browser’s private-browsing mode is not evidence about how a chatbot provider retains prompts, logs activity, or applies its training terms. Check the chatbot’s product- and endpoint-specific policy rather than inferring its data handling from the browser window.
Can I paste a medical report or contract into a chatbot if I remove my name?
Removing a name may not remove identifying details from the report, contract, dates, or circumstances it describes. If a general explanation will do, ask using a less specific or altered scenario; otherwise, confirm the service’s handling terms and consider asking a qualified professional.
Is an AI chatbot covered by my workplace’s confidentiality rules?
That depends on your employer’s policies, agreements, and the service and account being used; a chatbot’s privacy settings do not establish workplace approval. Ask the person responsible for your organisation’s data or security rules before submitting confidential work information.
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No. Data-handling protections and answer quality are separate matters, and a chatbot can still produce an incorrect or misleading response. For consequential decisions, have an appropriate qualified person review the output.
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