DeepSeek Compliance: Approve the Route, Data, and Evidence Together

DeepSeek compliance starts with a route decision. A consumer account, DeepSeek's hosted API, an open-weight model on enterprise GPUs, and a third-party inference service create different contracts, data locations, control owners, and evidence. The model family name tells a reviewer very little about the actual processing path.

For the hosted service, the public documents also draw a line around enterprise responsibility. A deployment owner needs to read the current policy and terms, approve the data allowed on the route, and prove that the application follows those conditions in operation.

TL;DR

• Record the actual DeepSeek deployment path and provider hostname. A model name alone leaves contract, location, and retention unanswered.
• DeepSeek's public privacy policy says downstream applications built on its open platform have their own controller duties.
• Current terms address input responsibility, service improvement, opt-out, output review, and high-impact decisions.
• Compliance evidence needs an approved use case, data rules, identity, route policy, human review, change records, and repeatable operating tests.

Start with the exact DeepSeek service

The DeepSeek API documentation identifies separate OpenAI-compatible and Anthropic-compatible routes under its API hostname. It also shows bearer-token authentication and a model field in the request. Those details give the hosted API path a concrete hostname and credential boundary.

Record that path separately from a self-hosted model or a third-party host serving DeepSeek weights. For each deployment, name the legal counterparty and contract, hosting location, account owner, API endpoint, model version, approved application, data classes, retention rule, and evidence source. A service can move between hosts through a one-line base URL change, which changes more than the model identifier reveals.

The AI data residency controls can guide the location record. Make the route an approved configuration item. The change process should trigger when the hostname, provider, model, region plus account terms and retention statement changes.

I think "we use DeepSeek" is an unusable answer in a compliance review. It collapses several possible processors and deployment boundaries into one brand name.

Read the privacy policy against your application role

DeepSeek's Privacy Policy, updated February 10, 2026, applies to services that link to it. It identifies Hangzhou DeepSeek Artificial Intelligence Co., Ltd. as controller for those services. It also states that processing rules for personal data collected from end users of downstream applications built by developers on open platform services fall outside that policy. The developer operating the downstream application should provide the relevant disclosures.

That statement belongs in the enterprise record. If your customer-support application calls the DeepSeek API, define your organization's role and the disclosure shown to users. Identify the purpose and legal basis with counsel. Record the data classes that may enter prompts and the rights process for relevant application records.

The policy says the service may collect inputs such as prompts together with uploaded files and chat history. It describes service improvement and model training among processing purposes, offers an opt-out right for training use, and says data may be processed and stored in the People's Republic of China. It also warns users against providing sensitive personal data to the services. Apply those statements to the exact account and service your team uses, then capture any negotiated terms that alter the public baseline.

A screenshot of an opt-out toggle needs an owner, account scope and capture date, alongside retest schedule. Preserve the setting beside the application route so a reviewer can connect policy text to a configured control.

Terms create approval and review obligations

The DeepSeek Terms of Use, updated March 27, 2026, include APIs within the defined services. The terms place responsibility for submitted inputs and corresponding outputs on the user and require the user to hold necessary rights and permissions for the input.

The terms say DeepSeek may use inputs and outputs to provide and improve the service under stated encryption and de-identification conditions, with an opt-out through "Improve the model for everyone." They also say outputs may contain errors. For outputs used in decisions with legal or material effects on people, including credit, employment, housing, insurance, legal and medical contexts, the terms call for human review and assign responsibility to the user.

Convert those statements into controls. Add an input-rights check to the use-case approval. Define prohibited data in the same record. Record the training-use setting and who verifies it. For a decision-support use case, document the human checkpoint, reviewer qualification plus escalation route and sample evidence. Preserve the model and prompt version tested for that workflow.

The AI governance framework provides the institution-wide structure. The DeepSeek file should supply the provider-specific facts together with configuration and operating evidence that sit beneath it.

Build one evidence file per route

A hosted API route should have a compact evidence index. Start with the use-case ID and business owner. Add the application identity and originating user context, provider account, hostname, model, approved prompt data, training-use choice, retention treatment, contract review and human-review requirement, alongside risk approval.

Then add operating evidence:

• One permitted request using an approved identity and data class.
• One denied request carrying a prohibited class or disallowed route.
• The policy version and reason associated with each decision.
• A correlation ID joining the application transaction to provider and gateway records.
• A quarterly review showing the public terms, with model route and configuration were checked again.

Avoid copying raw sensitive prompts into the compliance folder. Store a protected reference or suitable digest and retain the classifications plus decision facts and authorized retrieval path. The signed audit logs for AI requests explains why evidence written outside the calling application's write path carries more weight during investigation.

For a self-hosted model, replace provider-account evidence with infrastructure and model-supply-chain records. Include weights and license provenance, inference-server configuration, network access, logging behavior, retention, patching together with change approval and workload identity. The compliance job remains route-specific even when the model family stays the same.

Test the control instead of describing it

Run a production-like test with a compliance reviewer and application owner present. At 16:15 UTC, put the approved route and policy version on one screen. Submit a permitted prompt through the application and retrieve the decision event by request ID. Confirm the originating identity, relay service, destination, model, classification and outcome, alongside timestamp.

Submit a prohibited data class through the same route. Confirm that the model endpoint never receives a forwarded request when the policy denies it. Preserve the rule beside the resulting decision record. If redaction is permitted, test that path separately and retain the transformed-request reference under access controls.

Now change the base URL in a staging configuration to an unapproved host. The change should fail deployment policy or create an exception requiring review. This test matters because an OpenAI-compatible interface makes provider substitution technically small while its compliance effect can be large.

Document defects with owners and due dates. Re-run the exact failed test after remediation. The evidence file should show the original result and closure, rather than replacing the failure with a cleaner screenshot.

Keep the HTTP boundary precise

DeepInspect can govern authenticated HTTP requests deliberately routed between users or agents and an LLM endpoint, including the hosted DeepSeek API and a self-hosted OpenAI-compatible server. It needs identity context from the application and visibility into the routed request. The control can classify prompt content, apply a route policy, with make a decision and write a per-decision record.

Local model execution outside that HTTP route remains an infrastructure responsibility. STDIO traffic, endpoint compromise, stolen credentials, provider-side operations, model training, model weights plus contract negotiation and direct bypass calls also sit elsewhere. The provider's internal handling requires contractual and assurance review. DeepInspect makes no claim about traffic it never receives.

Provider due diligence answers questions about the service. Application governance answers questions about purpose and users. Route enforcement answers which request proceeded under which policy. Each evidence source has its own owner.

DeepInspect

DeepInspect sits as a stateless proxy on approved HTTP LLM routes. For a routed DeepSeek request, it evaluates application-supplied identity and prompt classification against the permitted endpoint together with use-case policy and current rule version before the request proceeds.

Each decision can preserve the originating principal, relay service, DeepSeek route, model, classification, policy version, outcome and timestamp, alongside correlation ID. That record supports the deployment file alongside the current privacy terms, account configuration, human-review evidence, with retention decision and provider assessment. It covers the request boundary rather than the provider's internal systems.

Use one permitted event and one denied event as the first operating test. Book a demo today.