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Do not put confidential information into a Chinese AI service until you have assessed the exact model, application or API route, account type, hosting location and current terms. A hosted chatbot can retain prompts, uploads, feedback and chat history, use those inputs to improve its technology, and process personal data in another jurisdiction. Those risks are different from running open-weight software entirely inside infrastructure you control.
DeepSeek’s privacy policy, last updated February 14, 2025, says it may collect text inputs, prompts, uploaded files, feedback, chat history and other content; monitor interactions; use information to train and improve technology; and process or store personal data on servers in the People’s Republic of China. The practical starting point is data minimization: evaluate with synthetic or public material, and require security, privacy, legal and procurement approval before using sensitive data.
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What you are actually assessing
“A Chinese AI model” is not one technical or legal category. Your risk depends on the complete service path:
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- Client: distinguish a web or mobile chatbot, a desktop application, an API, an enterprise tenant and open-weight code.
- Hosting: record where inference runs, where prompts and logs are stored, and which subprocessors can access them.
- Account and controls: note the subscription tier, administrator settings, plugins, browsing tools, connectors, retention options and identity integration.
- Date: policies, model behavior, app permissions and government restrictions can change, so record when you checked them.
An open-weight model run offline on a controlled server has a different data path from a hosted chatbot. A local deployment can still have risks in the code, model files, dependencies and update process, but cloud-service findings should not automatically be applied to every self-hosted installation. Conversely, an enterprise label does not by itself prove that prompts are excluded from training or that storage is outside China.
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What the provider’s policy tells you
Data categories
Start by listing every item the service can receive, not only the text typed into a message box. DeepSeek’s policy states: “When you use our Services, we may collect your text input, prompt, uploaded files, feedback, chat history, or other content that you provide to our model and Services.” Treat that as a broad collection scope that can include documents, images, code, conversation metadata and feedback associated with an account.
Training, monitoring and reuse
The policy describes monitoring interactions and using information to train and improve technology. Unless a current contract clearly excludes your tenant and defines enforceable handling, assume that submitted content may be retained and reused for operational or improvement purposes. An account setting that disables one form of history may not change backups, abuse monitoring, support records or legally required retention; verify each purpose separately.
Retention and deletion
Do not infer a universal deletion deadline. DeepSeek says retention varies with the type and sensitivity of information, the purpose for which it was collected and legal requirements. Ask for the retention period for prompts, attachments, logs, backups and support tickets, then ask how deletion propagates to replicas and subprocessors. If the answer is a general statement rather than a measurable period and procedure, record deletion as an unresolved risk.
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The policy identifies Hangzhou DeepSeek Artificial Intelligence Co., Ltd. as the controller and says personal data may be stored on a server outside the country where the user lives, including servers in the People’s Republic of China. Storage location is only one part of jurisdiction: the provider’s incorporation, administrators, subprocessors, support teams and applicable disclosure laws also matter. You cannot conclude from encryption in transit that a provider cannot read or disclose prompts.
Classify information before you test
Use a written data-classification rule before anyone opens an account. The safest first evaluation uses fabricated records, public documents or data that has been irreversibly anonymized. Redaction that leaves a person identifiable through combinations of names, dates, locations or unique facts is not sufficient.
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| Information type | Use in an initial test? | Required condition before production use |
|---|---|---|
| Public text and synthetic records | Yes, if they contain no hidden credentials or personal data. | Document the test purpose and remove unnecessary fields. |
| Personal identifiers, health or financial records | No. | Privacy review, lawful-processing basis, contractual limits, retention and deletion evidence, and an approved deployment. |
| Passwords, API keys, tokens and private certificates | Never. | Keep outside prompts and attachments; rotate immediately if exposed. |
| Proprietary source code, unreleased designs and confidential strategy | No. | Security and legal approval, an explicit no-training commitment where required, access controls and an incident plan. |
| Customer, employee or government information | No. | Follow sector, contract, export-control and public-sector rules; obtain written owner acceptance. |
| Export-controlled or national-security material | No. | Use an authorized environment whose jurisdiction, personnel access and transfer controls have been verified. |
The U.S. Department of Justice’s Data Security Program provides a useful sensitivity lens for organizations handling sensitive personal or government-related information. It does not approve a particular model; it helps you decide which data must stay out of a service until legal and security owners accept the transfer.
Inspect the client and the network path
Web and mobile applications
Before installing or signing in, record the publisher, package source, version, requested permissions, update mechanism and domains contacted. Check whether the application can read the clipboard, files, contacts, microphone, camera, notifications or browser data. Review local caches, crash reports and diagnostic settings. Use endpoint protection and network monitoring to identify unexpected executables, domains, certificates or data flows.
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These controls have a strict limit: they can reveal local malware, excessive permissions or suspicious traffic, but they cannot stop a provider from retaining a prompt on its servers or answering a lawful access request. A clean antivirus result is therefore not evidence that the service is private.
APIs and enterprise integrations
For an API, document the endpoint region, authentication method, logging behavior, model parameter defaults, rate limits, error payloads and connected tools. Determine whether prompts appear in provider dashboards, support systems, abuse-review queues or analytics. Test with a unique harmless marker and ask the provider where it appears, how long it remains and how to delete it. Require written answers rather than relying on marketing labels such as “secure” or “private.”
Open-weight or self-hosted models
Confirm that inference, telemetry, model downloads, update checks and logging remain inside your controlled environment. Pin dependency versions, verify package provenance and signatures, restrict outbound connections and scan the host. Review the model license and any bundled code. Self-hosting reduces provider-side prompt exposure; it does not eliminate vulnerabilities, malicious updates, unsafe generated code or biased or censored outputs.
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Test information integrity and safety
Security testing and answer-quality testing are separate workstreams. A model can have acceptable transport security and still provide unsafe code, disclose training-data fragments, resist oversight poorly or suppress information on sensitive subjects.
Build a repeatable test set
Use the same prompts, locale, model version, temperature and other settings for every candidate. Record the date, account type, client, full output and any refusal or warning. Include:
- ordinary factual questions with answers you can verify independently;
- requests to extract, infer or combine personal data;
- prompt-injection attempts that tell the model to ignore system instructions or reveal hidden context;
- code-generation requests involving authentication, file handling, cryptography and network access;
- political, historical and human-rights topics likely to expose censorship or selective omission;
- the same question in multiple languages and with minor wording changes;
- repeated runs to measure refusal and answer consistency.
Evaluate the results
Look for fabricated citations, changing factual answers, unexplained omissions, unsafe code, disclosure of test secrets, and different treatment by language or locale. Do not score a refusal as a security failure automatically: refusal can be appropriate. Instead, check whether the model explains the boundary consistently and whether it will provide safe alternatives. Preserve the raw outputs so a later reviewer can reproduce your decision.
What independent assessments have reported
These findings do not prove that every model, client or deployment has identical behavior. They do show why policy review, technical testing and jurisdiction analysis must be combined.
| Source and year | Reported concern | How to use the finding |
|---|---|---|
| NIST Center for AI Standards and Innovation (CAISI), 2025 | Evaluation of DeepSeek models reported security and censorship shortcomings and said the models lagged U.S. models in security on the tests performed. | Test the specific model and client for prompt injection, unsafe code and information suppression; do not treat a benchmark as a certification. |
| Taiwan National Institute of Cyber Security (NICS), 2025 | Testing reported inadequate security and protection mechanisms and recommended against using the service. | Use a heightened review for government, regulated or high-impact environments and require written risk acceptance for exceptions. |
| South Korea’s National Intelligence Service (NIS), 2025 | Reported excessive personal-data collection, use of all inputs as training data, unrestricted sharing with advertisers, overseas storage and differing answers on sensitive topics. | Verify each allegation against the current contract and repeat data-flow and multilingual behavior tests before deployment. |
| 2025 independent research audit | Examined information suppression in DeepSeek. | Include politically and historically sensitive prompts in an integrity test and document omissions as well as direct errors. |
| Victoria public-sector guidance | Classified DeepSeek products as an unacceptable security risk for Commonwealth government systems. | Public-sector users should check their own authority’s prohibition or approval list instead of making an individual exception. |
| U.S. Department of Justice guidance | Emphasizes protecting sensitive personal and government-related data from countries of concern. | Use jurisdiction and data sensitivity as gating criteria, not as an after-the-fact legal check. |
None of these sources establishes one universal retention period, deletion service-level agreement or security certification for every Chinese AI model. Do not fill those gaps with assumptions.
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A practical assessment workflow
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Identify the exact service
Write down the provider, model and version, app or API route, hosting region, account tier, enabled plugins or tools and the date checked. Capture screenshots or exportable settings so a later reviewer sees the same configuration.
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Read the current policy and contract
Extract the controller, collected data, training or improvement use, opt-out method, retention period, deletion process, subprocessors, international transfers, breach notice and governing law. Ask for an enterprise addendum when the public policy is silent. Treat an unanswered question as an open risk rather than as permission.
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Classify data and define a test boundary
Publish a one-page rule stating what may be submitted, what is prohibited and who can approve an exception. Begin with synthetic or public data. Keep secrets, credentials, regulated personal data, proprietary code and confidential plans out until the relevant owners sign off.
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Check technical controls
For an app, inspect permissions, package provenance, updates, TLS behavior, local logs, crash reports and outbound domains. For an API, verify authentication, tenant separation, regional routing, logging and tool permissions. For self-hosting, lock down egress, dependencies and updates. Record what endpoint controls can detect and what provider-side risks they cannot prevent.
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Run integrity and safety tests
Use the documented test set across languages and repeated runs. Log exact settings and full outputs. Have a subject-matter reviewer check factual accuracy, privacy leakage, code safety, refusal consistency and politically sensitive omissions.
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Make a written risk decision
Score the candidate on data collection and training, storage and transfer jurisdiction, retention and deletion enforceability, security evidence and incident history, output integrity, legal fit and operational controls such as single sign-on, audit logs, regional hosting and rapid disablement. A high-risk result should block production use, not merely add a warning banner.
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Reassess after changes
Recheck the policy, model card, subprocessors, hosting region, application version and government restrictions after a major model release, terms change, ownership change, security incident or new integration. For volatile facts, use a 30-day freshness window; the information summarized here was current to October 16, 2026.
Decision criteria for different users
Individual experimentation
Use only public or fabricated content, a separate account and a device without sensitive files. Disable optional permissions and connectors. Do not paste passwords, recovery codes, private messages, medical details, tax records or unreleased work. Remember that deleting a local chat does not establish deletion from provider systems.
Businesses
Route any pilot through vendor-risk management. Require an approved data classification, administrator controls, identity management, auditability, incident contacts and a documented offboarding process. Restrict access by group, block prohibited content with data-loss-prevention rules where practical, and train staff that endpoint monitoring cannot undo provider retention.
Public-sector and regulated organizations
Start with the applicable prohibition and procurement rules. Victoria’s classification illustrates a low-tolerance posture for government systems, while DOJ guidance highlights sensitive personal and government-related data. If an authority permits evaluation, require written acceptance by security, privacy and legal owners, define the authorized data set and retain the test record for audit.
Common assessment mistakes
- Generalizing from nationality: assess the specific provider, model, route and contract rather than labeling every model from a country identical.
- Confusing encryption with confidentiality: TLS protects transit; it does not prevent the service from processing, retaining or legally disclosing content.
- Relying on a “no history” switch: verify training, abuse monitoring, backups, support access and deletion separately.
- Testing with real secrets: use a canary string that has no operational value. If a real credential is exposed, revoke it immediately.
- Checking only the chatbot: review mobile permissions, desktop updates, browser extensions, plugins, connectors and API logs as separate components.
- Treating a benchmark as approval: independent evaluations identify concerns; they do not certify your version, settings or deployment.
- Ignoring behavior changes: model updates, policy revisions and regional routing can alter both data handling and answers.
When the answer should be “not approved”
Reject or pause the deployment when the provider cannot identify storage and transfer locations, will not state whether prompts train models, offers no workable deletion process, requires prohibited data for the use case, lacks an incident contact, or produces unacceptable security or integrity results. Escalate instead of silently accepting uncertainty when the data involves children, health, finance, customers, export controls, government operations or trade secrets.
Approval is conditional, not permanent. Keep the service inventory, policy capture, test prompts, outputs, technical observations and owner sign-offs together. Set an expiry date for the decision and trigger a new review whenever the model, client, terms, subprocessors, hosting region or applicable government guidance changes.
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