A while back, DeepSeek was named during the UN Security Council's discussion on AI security, and this spread quickly in tech circles. My first reaction wasn't "domestic models have made it big," but another more practical question: when large models are placed on the international security agenda, how should companies calculate the compliance costs of connecting to large model APIs? The signal is clear—AI capability is no longer just a technical selection issue; it is beginning to carry diplomatic and regulatory attributes.
1. Where the real signal of this matter lies
The core of the Security Council's discussion on AI security is not judging which model is stronger, but that countries are beginning to set rules for the "cross-border flow of AI capabilities." For Chinese companies, the direct impact is this: where the model you call is deployed, where data flows, and how long logs are retained—things no one used to scrutinize—will now be watched by compliance departments. In IDC's 2025 enterprise AI survey, more than 60% of surveyed companies listed "data compliance" as their primary concern for adopting generative AI, ahead of cost.
2. The three types of compliance issues companies cannot avoid when connecting to large model APIs
The first is cross-border data transfer. When you call an overseas model, customer information and contract text in the prompt are transferred abroad. Classified protection and the Measures for Security Assessment of Data Exports impose strict controls on this, especially in finance, healthcare, and government scenarios.
The second is log retention. Regulators require traceability, but logs themselves also contain sensitive information. Retention and desensitization are a pair of contradictions. Many teams directly store full requests in plaintext, and if something goes wrong, it is a major incident.
The third is content safety filtering. Generative AI services have clear content review obligations. You have to take responsibility for what the model outputs; you cannot push it all upstream.
3. How to address these three types of issues at the technical layer
When we build AI API aggregation in our projects, the most energy-consuming part is actually not model access, but the compliance layer. Simply put, the aggregation layer needs to do three things: data isolation, request desensitization, and audit logs.
Data isolation means that requests from different tenants and different business lines are separated at the gateway layer and not mixed into a single log stream. When we do isolation in SiCore TokenWorks' aggregation layer, we split by two dimensions: tenant + business tag. Cross-tenant data is physically isolated at the storage layer, not left to the application layer's self-discipline.
Request desensitization means performing a cleanup before the request leaves the gateway. Fields recognizable by regex, such as mobile phone numbers, ID numbers, and bank card numbers, are replaced before forwarding. Audit logs record only metadata: who, when, which model was called, and how many tokens were consumed—not plaintext content. This satisfies traceability without exposing sensitive data in logs.
By the way, one point about cost. After unified access to multiple models, usage-based billing becomes much clearer. We compared it: scheduling the same batch of tasks through the aggregation layer not only saves money compared with connecting to official SDKs one by one, but also saves the manpower of maintaining five sets of authentication logic. token8341 does automatic model selection by task in this area, prioritizing domestic models and using overseas models as a fallback, with a unified log format as well.
4. Several practical suggestions for developers and enterprises
Don't connect directly to official APIs right away. First use an aggregation layer to consolidate calls. Compliance policies, rate limiting, and desensitization can all be done once at the gateway. Changing one line of base_url allows model switching, with low migration cost.
Define the logging strategy in advance. Which fields are recorded, how long they are stored, and who can query them should be written into the access specification. Don't wait until an audit arrives to fix it.
Prioritize domestic models for scenarios they can cover. Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark are sufficient for Chinese-language tasks, and data stays within the country, reducing compliance pressure by a notch.
5. Looking ahead
AI security entering the Security Council is only the beginning of tighter regulation. Gartner predicts that by 2027, a significant proportion of enterprise generative AI applications will be required to rectify compliance issues. My judgment is: model capabilities will become increasingly similar, and what truly creates differentiation is who can keep both compliance and cost stable at the same time. The competition in large model APIs will next be about the access layer, not the model layer. Whoever builds data isolation, desensitization, and auditing solidly will be able to capture the next wave of enterprise demand.