DeepSeek has emerged as a phenomenon since its announcement in late December 2024 by hedge fund company High-Flyer. The AI industry and general public have been captivated by both its capabilities and potential implications.
Security has been at the forefront of recent conversation due to reports from Wiz that the DeepSeek database is leaking sensitive information, including chat history as well as geopolitical concerns. Even RedMonk analyst Stephen O’Grady discussed DeepSeek and the Enterprise focusing on considerations for business adoption.
At AuthZed, we recognize that trust and security fundamentally shape how organizations evaluate AI models, which is why we're sharing our perspective on this crucial discussion.
The DeepSeek Phenomenon
What makes DeepSeek particularly noteworthy is its unique combination of features. As an open-source model, it demonstrates performance comparable to frontier models from industry leaders like OpenAI and Anthropic, yet achieves this with (reportedly) significantly lower training costs. The R1 version exhibits impressive reasoning capabilities, further challenging conventional assumptions about the infrastructure investments required for advancing LLM performance.
Balancing Potential and Precaution
While these factors drive DeepSeek’s popularity, they’ve also drawn skepticism alongside geopolitical considerations based on DeepSeek’s origin. The uncertainty surrounding the source of training data and potential biases in responses warrants careful consideration. A recent data breach of the hosted service has heightened privacy concerns, particularly given the official hosted service’s terms of service permit user data retention for future model training.
Despite the concerns, users and companies increasingly express interest in exploring its capabilities. Organizations seeking to leverage DeepSeek's capabilities while maintaining data security can adopt permissions systems to define data access controls. This strategy is especially relevant for applications built on DeepSeek's large language models, where protecting sensitive information is paramount.
SpiceDB: A Solution for Secure AI Integration
SpiceDB offers a robust framework for organizations integrating AI capabilities. Its fine-grained permissions help avoid oversharing by letting you precisely define which data the model can and cannot access. This granular control extends beyond data access - you can prevent excessive agency by explicitly defining the scope of actions a DeepSeek-based agent is permitted to take. This dual approach to security - controlling both data exposure and action boundaries - makes SpiceDB particularly valuable for organizations that want to leverage DeepSeek’s capabilities but in a controlled environment.
Practical Implementation
To help organizations get started, we've created a demo notebook showcasing SpiceDB integration with a DeepSeek-based RAG system: https://github.com/authzed/workshops/tree/deepseek/secure-rag-pipelines
For further exploration and community support, join our SpiceDB Discord community to connect with other developers implementing secure AI applications.
