DeepSeek and Open-Weight Models: Risks, Limits and Governance Rules

An open-weight model is one whose trained parameters are published so anyone can run it. DeepSeek, a Chinese AI company, has released models such as DeepSeek-V3 and the R1 reasoning family this way. Other well-known families include Meta’s Llama, Mistral’s open models, Alibaba’s Qwen, Google’s Gemma and OpenAI’s open-weight releases.

Open weights are not always open source in the strict sense. Training data and code are often not published, and licences range from permissive, such as MIT or Apache 2.0, to custom licences with usage restrictions. Read the licence for each model and version you use.

The risks fall into a few groups. Each needs its own rule, and none are solved simply by picking a well-known model.

Data and hosting risk

Using a model through its maker’s hosted app or API sends your data to that company’s servers under its terms and local law. DeepSeek’s own app and API are run from China, and several governments and regulators have restricted or investigated its use over data protection concerns. Running the same open weights on your own servers or through a cloud provider such as AWS Bedrock, Azure or Google Vertex AI is a very different data situation.

Content and bias risk

Models reflect their training. Some open models, including DeepSeek’s, have been reported to give censored or one-sided answers on politically sensitive topics. All models can produce false statements with confidence. For marketing content, that means claims must be checked and sensitive topics handled by people.

Security risk

Self-hosted models need the same security care as any server: access control, patching and logging. Researchers have shown that safety training in open models can be weak or removed by fine-tuning, so you cannot rely on the model to refuse harmful requests. Model files downloaded from unofficial sources may also be tampered with.

Licence and legal risk

Licences may restrict commercial use above a size, require attribution, or forbid certain applications. Copyright questions about training data and generated output remain unsettled in many countries. Keep a record of which model and licence each project uses.

Open-weight models can match closed models on many benchmarks, but running them well takes work. Large models need powerful GPUs; smaller distilled versions run on modest hardware but are less capable. Your team has to handle updates, scaling and monitoring that an API provider would otherwise do.

Features like web browsing, file handling and tool use come from the surrounding software, not the model. A raw open model is an engine without a car around it.

Write these rules before the first project, and keep them short enough that people read them.

• Approved hosting: list which ways of running models are allowed, such as self-hosted inside your cloud account or named cloud providers, and which are not, such as consumer apps for company data.
• Data classes: define what may be sent to each setup. Public content may go anywhere approved; client confidential and personal data only to self-hosted or contracted environments.
• Approved models: keep a register of models, versions, sources and licences cleared for use, with an owner for each.
• Output review: require human review for anything published, sent to clients or used in decisions.
• Sensitive topics: name topics where model output may not be used without expert review, such as health, legal, finance and politics.
• Security baseline: download weights only from official repositories, verify checksums, restrict access and log usage.
• New use approval: one named person or small group approves any new use case against these rules.

A marketing team might allow a self-hosted open model for tagging product feeds, summarising public competitor pages and drafting social variants, because the data is public and the output is reviewed. The same team would not paste client contracts into a consumer chatbot app from any provider.

For client-facing work, disclose AI use when the client’s policy asks for it. Some clients ban specific providers by name; your model register should make it easy to show which model touched which project.