Protect LLMs with CipherTrust Transparent Encryption - White Paper

The exponential growth of data in digital environments has brought about an urgent need for robust protection strategies, particularly regarding sensitive Large Language Models (LLM) use cases. How can Thales help your organization protect its private data in LLM use cases? Thales, the leader in data security solutions, offers an efficient approach to safeguarding private data in LLM use cases. Thales proposes two scenarios: Scenario 1 focuses on data protection at rest and in transit; Scenario 2 protects data at rest, in transit and during execution. 

The primary purpose is to safeguard the backend framework, which stores all data queried by users to the LLM, along with user credentials, logs, metadata, and more. Any prompt and response used and stored can contain sensitive data that requires protection. An approach to improve the efficiency of the data retrieval and accuracy of the responses using domain specific domain, is known as RAG (Retrieval-Augmented Generation). Thales provides additional protection for LLMs using RAG with Thales’ CipherTrust Transparent Encryption to provide a seamless and robust security framework for LLM use cases.

By addressing these risks and implementing the proposed solution, organizations can effectively fortify their data protection strategies and mitigate the risks associated with LLM use cases, thereby ensuring the confidentiality and integrity of sensitive information.