Dilemma in LLM Adoption for Secure Environments

An AI professional expresses concern regarding the choice of large language models (LLMs) for clients with high national security needs. The inability to use cloud services to prevent sensitive data leaks pushes towards the adoption of open source models in closed environments.

US vs. Chinese Models: A Security Problem

However, customers reject Chinese models due to perceived national security risks. The US alternative, gpt-oss-120b, is less performant than more recent models like GLM and MiniMax. This creates a strong constraint: use less capable models and fall behind, or adopt potentially risky solutions.

Searching for Alternatives and Future Implications

The user speculates about pressure on Anthropic to provide offline models to the US Department of Defense. They also wonder about the possibility of asking OpenAI to release another open source model, or whether Cohere (Canada) could represent a viable alternative. The situation highlights a growing difficulty in finding AI models suitable for contexts with stringent data sovereignty and security requirements.

For those evaluating on-premise deployments, there are complex trade-offs between performance, security, and data control. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.