The arrival on the market of extremely fast and economical language models developed in China has shaken the artificial intelligence landscape. Services like Kimi (Moonshot AI), DeepSeek, and the Qwen series have seen a surge in popularity among developers and tech experts, offering a low-cost alternative to major Western labs.
However, before integrating these APIs into business workflows, especially if they are embedded in applications that process contracts, customer data, or patents, it is essential to carefully assess the regulatory, confidentiality, and technological stability risks.
Dossier Summary
- The lure of cost: Chinese models like DeepSeek and Kimi cost up to 95% less than OpenAI or Anthropic.
- External jurisdiction: Data sent to external cloud APIs fall under data security laws that allow state controls without GDPR guarantees.
- Continuity risk: Server saturation due to traffic spikes and potential preventive blocks by the Privacy Authority.
- The solution: Download open weights of models and run them locally offline via internal servers or dedicated local computers.
The Journey of Data Beyond European Borders
When documents or source code are forwarded via APIs based on servers located outside the European Economic Area, the information travels and enters a different jurisdiction. In the case of providers based in China, processing falls under local data security laws (Data Security Law), which allow local authorities access to servers for national security purposes.
Moreover, on the front of the European General Data Protection Regulation (GDPR), transfer to countries that do not offer adequate guarantees and lack specific bilateral agreements constitutes a structural violation. Many of these foreign providers do not offer standard contractual clauses suitable for European Union companies.
Strategic Comparison
Data Management: Cloud vs Local Execution
Using external cloud APIs exposes contracts and proprietary sources to foreign jurisdictions. The secure alternative is to use the same open-weight models by installing them locally (e.g., via Ollama or internal offline servers), eliminating any information leakage.
Service Stability and Interruption Risks
Alongside legal issues is the topic of business continuity. The sudden global traffic spikes that characterize emerging models often cause remote server saturations, resulting in errors and response blocks.
There is also an immediate regulatory factor: Privacy Authorities in Europe strictly monitor data flows abroad. In the event of confirmed anomalies in transfers, they can order the immediate blocking of connections to the service from a nation or the entire EU, jeopardizing the activities of those who have built their automations on that single channel.
The Way Out: Cloud Models vs Local Execution
Protecting corporate intellectual property does not require giving up these technologies, but it does require changing the way they are executed:
- Direct Cloud APIs (High Risk): Send sensitive data or proprietary code to external servers. It is advisable to avoid using them with confidential information.
- Open-weight models locally (Secure): Many of these models are distributed with open licenses. Downloading the models and running them directly on internal servers or dedicated computers within the corporate network ensures that no data can ever leak outside.
A Long-Term Strategy
For the ordinary management of sensitive data, it is preferable to opt for providers that offer explicit GDPR compliance guarantees and host information within European data centers. If different external models are to be tested, local removal of personal data (preemptive redaction) and transitioning to the use of locally installed models are the only concrete guarantees to protect corporate assets.