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Do not paste confidential or identifiable information into a public cloud chatbot unless your organisation has approved that specific service and its controls. DeepSeek’s privacy policy, last updated 10 February 2026, says it collects prompts, uploaded files, photos, voice input, feedback and chat history, and directly processes and stores personal data in the People’s Republic of China. It also says the service is not designed for sensitive personal data. The same basic caution applies to any hosted AI service: your prompt becomes data handled under a provider’s collection, retention, security, training and jurisdiction rules.
You can still use cloud AI safely for many tasks by minimising data, replacing identifiers with realistic placeholders, checking account controls, deleting stored chats, and using an approved enterprise workspace when work data is involved. The guide below shows what to keep out, how to de-identify material, how organisations should govern use, and what to do if sensitive information has already been shared.
What does DeepSeek collect and where is it handled?
DeepSeek’s current privacy policy says it collects account information and user inputs, including text prompts, voice input, uploaded files, photos, feedback and chat history. It says this information is used to provide, develop and improve its services. The policy also states that personal data is directly collected, processed and stored in the People’s Republic of China.
That statement describes the provider’s handling of data, not merely the location of a server used for a particular request. A prompt can include personal information, confidential business material, embedded document metadata or secrets copied from another system. If any of those appear in an input, they become part of the service’s data-handling scope.
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How long can information remain available?
DeepSeek says retention varies with the amount, type and sensitivity of information and the purpose of processing. For service provision, it says account, input and payment data may be kept while an account exists, with additional retention for legal obligations or legitimate business interests. Deleting a visible conversation therefore should not be treated as proof that every associated copy, log or backup has disappeared immediately.
Can prompts be used to improve a model?
DeepSeek’s model-training disclosure says some user input may contribute to optimisation-training data only after encryption, strict de-identification and anonymisation, and that users can opt out. Treat that as a provider claim to verify against the current policy and the controls on your account. An opt-out does not mean a prompt is never retained for service operation, abuse prevention, security logging, legal compliance or human review under applicable terms.
The safest assumption for an unapproved public chatbot is that anything you submit may be stored, processed in another jurisdiction, associated with your account and accessible to authorised provider personnel or systems for stated operational purposes. Use the service only with data that remains acceptable under that assumption.
Is DeepSeek safe for confidential information?
There is no universal yes-or-no safety label. DeepSeek’s own policy says the services are not designed or intended to process sensitive personal data and advises users not to provide it. Australia’s privacy regulator likewise recommends that organisations do not enter personal information, particularly sensitive information, into publicly available generative-AI tools because of significant and complex privacy risks.
Safety depends on the data, the account type, the controls enabled, the contract, the processing locations and the consequences of disclosure. A synthetic paragraph used to test grammar is materially different from a medical record, legal-client file, unreleased product plan or production credential. If the loss of a prompt would require notifying a customer, regulator, employee or business partner, a public consumer account is the wrong default.
Data that should never go into a public chatbot
- Passwords, API keys, private certificates, recovery codes, session tokens and other authentication secrets.
- Bank, card, tax, payroll and other financial-account details.
- Government identifiers such as passport, driver-licence, national-insurance or social-security numbers.
- Health, disability, biometric, genetic, sexual-orientation or other specially protected information.
- Legal-client material, privileged advice, investigation notes and documents subject to a confidentiality agreement.
- Customer records, support transcripts, employee files and identifiable applicant information.
- Unreleased financial results, source code, security findings, acquisition plans, designs, pricing or trade secrets.
- Any document whose licence, contract or data-protection notice prohibits sending it to an external processor.
These categories are prohibited by risk, not by a particular chatbot brand. A different provider may offer stronger enterprise controls, but those controls must be verified before the data is sent.
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How do I remove sensitive details before asking an AI chatbot?
Use a repeatable redaction workflow rather than relying on a quick glance. The objective is to preserve the problem’s structure while removing information that identifies a person, organisation, account or real-world event.
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- Classify the data. Mark direct identifiers, quasi-identifiers, secrets, regulated fields, confidential business content and any contractual restrictions. If you cannot classify it, treat it as sensitive until an owner approves use.
- Replace direct identifiers. Substitute names, email addresses, telephone numbers, street addresses, account numbers, invoice numbers and customer IDs with consistent tokens such as
PERSON_01,ORG_AandACCOUNT_17. Keep the mapping offline and separate from the chatbot. - Generalise indirect clues. Exact dates can become month and year; a precise location can become a region; a rare job title can become a department; an exact age can become an age band. Remove combinations that could identify someone even after names are removed.
- Strip hidden content. Check document properties, tracked changes, comments, revision history, spreadsheet formulas, embedded images, EXIF data, hyperlinks and copied email headers. Export a clean text version when formatting is not needed.
- Minimise the excerpt. Send only the paragraphs, rows or code fragment required for the task. Remove unrelated records, attachments and previous chat context.
- Use synthetic substitutes where possible. Create fictional names, dates, numbers and examples that preserve the format and edge cases but do not correspond to a real person or transaction.
- Review the final prompt as an outsider. Ask whether a colleague who knows the project could infer the customer, employee, product or incident from the remaining details. If yes, generalise further or do not upload it.
- Destroy temporary copies. Remove redacted files and local exports from shared folders, downloads and clipboard managers when the task is complete, subject to your organisation’s retention and evidence rules.
Example of safe transformation
Instead of sending “Maria Chen at 14 King Street reported chest pain on 3 March 2026; policy 884219,” use: “A middle-aged policyholder in a metropolitan area reported a non-specific symptom during March 2026. Suggest neutral questions an agent should ask before escalating.” The second prompt preserves the workflow question without exposing a name, address, policy number or exact clinical account.
Why pseudonyms are not automatically anonymous
A tokenised name can still be personal information if the token can be linked back to an individual, or if the remaining facts make the person obvious. Keep the re-identification key out of the service, restrict access to it, and evaluate combinations such as job title plus rare event plus date. De-identification lowers exposure; it does not create a licence to ignore privacy law or confidentiality duties.
Which privacy settings should I check before using a cloud chatbot?
Review controls before submitting data, not after a surprising response. Product labels change, so record the setting names and the date you checked them.
- Chat history: Determine whether conversations are saved to the account, for how long, and whether disabling history changes service features.
- Training or improvement use: Look for a setting that prevents prompts from being used for model optimisation. Confirm whether it applies to new chats only, existing chats, uploaded files or feedback.
- Deletion: Test the account’s delete function and read what it promises about backups, legal holds and residual logs. Delete chats that no longer need to be available, while preserving records your organisation must retain.
- Connected services: Review browser extensions, cloud-drive connectors, plugins and custom actions. A chatbot may receive data from those services even when you did not paste it into the prompt.
- Account security: Use a unique, strong password and multifactor authentication where offered. Check active sessions and revoke devices you no longer recognise.
- Sharing and collaboration: Disable public links and shared conversation pages unless a deliberate, approved publication is intended. Check whether recipients can copy attachments or the complete prompt history.
- Voice, image and location permissions: Deny optional device permissions and remove location or camera metadata when those inputs are not needed.
For DeepSeek specifically, compare the controls you see in your account with the February 10, 2026 policy and training disclosure. Do not assume an account toggle overrides contractual retention, security or legal-processing provisions.
What should an organisation’s AI-use policy require?
An effective policy turns “be careful” into enforceable decisions. It should name approved tools, prohibited data classes, accountable owners and the conditions for exceptions.
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Create a data-and-tool matrix
| Data category | Public consumer chatbot | Approved enterprise workspace | Locally controlled or private deployment |
|---|---|---|---|
| Public facts and synthetic examples | Usually acceptable after ordinary account review | Acceptable | Optional |
| Internal, non-sensitive drafts | Only if policy owner approves | Preferred when access and retention are controlled | Useful for strict isolation |
| Personal or confidential records | Prohibited by default | Requires privacy and security approval, contract review and least-privilege access | May be appropriate only after threat modelling and operational validation |
| Secrets and regulated data | Never | Never place raw secrets in prompts; regulated use requires documented controls | Use controlled interfaces, secret management and approved processing only |
The matrix is a governance aid, not a substitute for a legal assessment. A private deployment can still leak through logs, plugins, model outputs, administrators or a compromised application.
Complete a privacy-impact assessment
Document what data enters the system, why it is necessary, who can access it, where it is processed, how long it is retained, whether it is used for training, how deletion works, and what happens if the provider changes its terms. Assess re-identification, cross-border transfer, unauthorised disclosure, inaccurate output and secondary use. Revisit the assessment when a provider adds a connector, changes a model, alters a policy or introduces a new data region.
Use contracts and technical controls together
Procurement should verify data-residency commitments, subprocessors, breach notification, deletion certificates, audit rights, support access, encryption, identity integration, retention configuration and whether customer content is excluded from training. A contract cannot prevent an employee from copying a secret into the wrong account, so pair it with single sign-on, role-based access, data-loss-prevention rules, browser controls, logging and periodic access reviews.
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Training should include realistic examples of unsafe prompts, the approved-tool list, escalation contacts and the difference between anonymisation and simple name replacement. Monitor usage for prohibited data patterns where lawful, review alerts with minimal content exposure, and apply a consistent response for violations. Keep a written exception process with an owner, expiry date and documented compensating controls.
Australia’s Office of the Australian Information Commissioner updated its guidance on 17 January 2025 and recommends that organisations do not enter personal information, particularly sensitive information, into publicly available generative-AI tools. That is a practical baseline for policy design, not a claim that every private or enterprise deployment is automatically compliant.
How should I compare DeepSeek with another cloud AI chatbot?
Compare the complete data lifecycle, not model quality alone. The following questions expose differences that a marketing page may omit.
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| Decision area | Questions to ask the provider | Evidence to retain |
|---|---|---|
| Inputs and metadata | Are prompts, files, images, voice, device data, IP addresses and usage logs collected? Are administrators able to inspect content? | Current privacy notice, product documentation and admin settings |
| Training and optimisation | Is customer content used for training, evaluation, abuse detection or human review? Is opt-out available, and what scope does it cover? | Terms, training disclosure, configuration screenshot and contract clause |
| Processing location | Which countries process and store content? Can the customer select a region? Which subprocessors have access? | Data-residency statement, subprocessor list and transfer terms |
| Retention and deletion | What is the default retention period? What happens to backups, logs, deleted chats and legal holds? Can an administrator set a shorter period? | Retention schedule, deletion procedure and test result |
| Identity and administration | Are single sign-on, multifactor authentication, role-based permissions, guest restrictions and session revocation available? | Security configuration and access-control policy |
| Auditability | Can the organisation export access logs, prompt activity, connector use and administrative changes? | Sample audit report and log-retention terms |
| Legal and regulatory commitments | Does the provider support required data-processing agreements, breach notice, audit rights and assistance with data-subject requests? | Signed agreement and compliance documentation |
| Deployment control | Is there an approved enterprise tenant, private endpoint or locally controlled option, and what functionality is lost when isolation is enabled? | Architecture diagram, threat model and change record |
Score each option against the sensitivity of the intended use. A provider that cannot answer a question clearly should be treated as unverified rather than assumed safe.
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An application can expose more data than a user sees in the prompt. Secure the surrounding system as carefully as the model connection.
- Minimise at the boundary: Select and redact fields before sending a request. Do not forward an entire database row when a single attribute is needed.
- Keep secrets out of prompts: Store API keys in a secrets manager, inject them at runtime, and prevent them from appearing in source code, logs, traces or error messages.
- Apply least privilege: Give the model and its tools only the records and actions required for the current task. Separate read, write and administrative capabilities.
- Control retrieval: Filter document search by user authorisation before context reaches the model. Treat retrieved text as untrusted input that may contain prompt-injection instructions.
- Constrain outputs: Use schemas, validation and allow-lists for commands, database queries, file paths and external actions. Require human approval for irreversible or high-impact operations.
- Log safely: Record request IDs, policy decisions and outcomes without copying raw personal data into general-purpose logs. Set access and retention limits for debugging traces.
- Test abuse cases: Test prompt injection, data exfiltration, cross-tenant access, malicious files, model hallucination, excessive permissions and provider outages.
- Monitor changes: Track model versions, system prompts, connector scopes, provider policy changes and unusual volume. Re-run tests after material changes.
- Provide a kill switch: Be able to disable a connector, revoke credentials and route requests away from a provider without waiting for a full application release.
The UK National Cyber Security Centre’s secure-AI guidance for hosted models and external APIs emphasises designs that continue to protect sensitive data from unauthorised parties. That principle applies whether the model is called from a web app, a workflow tool or an internal script.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I do if I already pasted sensitive information?
Act quickly, but preserve enough evidence to understand what was exposed. Do not paste additional material while trying to “clarify” the original conversation.
- Stop further sharing. Close the chat, disable connectors and prevent colleagues from forwarding the conversation or public link.
- Record the facts. Note the account, time, data categories, files, recipients, model or service, visible sharing settings and any screenshots required by your incident process. Store the record in an approved incident system.
- Revoke secrets. Rotate exposed passwords, API keys, tokens, certificates and recovery codes. Invalidate sessions and review authentication logs for suspicious use.
- Delete what you can. Remove chats, files, shared links and connected-app permissions according to the provider’s documented process. Do not delete evidence your organisation is required to preserve.
- Notify the owner. Contact your privacy officer, security team, legal counsel, data owner or incident-response channel. They can determine whether customers, employees, regulators or contractual partners must be informed.
- Ask the provider targeted questions. Request confirmation of receipt, access, retention, deletion scope, subprocessor involvement and whether the content entered training or review systems. Keep the request factual and avoid sending the data again.
- Contain recurrence. Add the service to browser or network controls where appropriate, update the approved-tool list and brief the people who may repeat the mistake.
Deletion is a containment step, not proof that disclosure did not occur. Your incident team should decide the legal and contractual response based on the type of information and the provider’s answers.
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Privacy obligations depend on the people, organisations and jurisdictions involved. Guidance from one regulator is a strong risk signal but is not automatically a universal ban.
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- Australia: The OAIC guidance updated 17 January 2025 recommends that organisations avoid entering personal, especially sensitive, information into publicly available generative-AI tools. Use it as a conservative baseline for public-chatbot controls.
- Canada: The Office of the Privacy Commissioner advises users to limit personal information, avoid sensitive or identifiable information, alter details, delete chat history where possible, review privacy settings and use strong passwords.
- Victoria, Australia: An administrative guideline updated 14 February 2025 directs public bodies to prevent installation and remove DeepSeek products unless a legitimate business use is approved, and requires monitoring and user training. It is a government-specific rule, not a consumer ban everywhere.
- United Kingdom: The National Cyber Security Centre’s secure-AI material focuses on providers and developers using hosted models or external APIs, with emphasis on preventing unauthorised disclosure through secure design.
Before using a chatbot for regulated work, identify the relevant privacy, secrecy, employment, health, financial-services, records-management and cross-border-transfer rules. Ask your organisation’s privacy or legal function to interpret them for the actual data and country rather than relying on a generic “AI-safe” label.
What are the most common mistakes when using cloud AI?
- Assuming “free” means private: Price does not determine retention, training use or processing location.
- Removing only names: Rare dates, job titles, addresses, screenshots and narrative details can re-identify a person.
- Using a personal account for work: Personal accounts generally lack organisational retention, audit, identity and contractual controls.
- Uploading the whole file: Excess context increases exposure without necessarily improving the answer.
- Trusting a setting without testing it: Confirm what a toggle covers, when it takes effect and whether existing chats are included.
- Leaving connectors enabled: A drive, mailbox or browser extension can transfer content outside the prompt you intended to send.
- Logging raw prompts in your own system: Your application can create a second, less protected copy of the same sensitive data.
- Treating deletion as erasure everywhere: Backups, legal holds, security logs and provider subprocessors may follow different schedules.
- Allowing model output to trigger actions automatically: A plausible response can still contain an injection, disclosure or unsafe command.
A short pre-send pause—What data is this? Who could identify it? Where will it be processed? How long might it remain?—catches many failures before they become incidents.
Frequently Asked Questions
Can I use a cloud chatbot with anonymised customer data?
Only after checking that the remaining details cannot reasonably identify a person and that the use is permitted by your notice, contract and internal policy. Keep the re-identification key outside the service, document the transformation and obtain approval for higher-risk datasets.
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Does turning off chat history stop a provider from retaining my prompt?
Not necessarily. History controls may change what appears in your account while separate security, abuse-prevention, backup or legal-retention systems continue to hold data. Read the provider’s retention language and request clarification for the exact account type.
Should I use a browser extension that sends webpages to an AI chatbot?
Treat it as a data connector with its own permissions. Review what page content, cookies, metadata and account data it can read, restrict it to approved sites, and disable it before opening confidential material.
Are local AI models automatically private?
They can reduce provider-side exposure, but prompts may still enter application logs, shared storage, telemetry or a third-party model-download service. Secure the host, restrict access, manage model files and test the full data path.
Who should approve an AI chatbot for sensitive work?
Approval should involve the data owner plus privacy, security and procurement or legal specialists appropriate to the use. They should record the permitted data classes, region, retention, access controls, monitoring and a review date.
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