Crafting timely, truthful LLMs for top security operations

TLDR:

• Building your own LLMs using retrieval-augmented generation is recommended for better security operations.
• RAG helps eliminate problems like lack of timely data and visibility on training data in commercial LLMs.

In the article “Building Timely and Truthful LLMs for Security Operations,” Brennan Lodge, a professor at New York University, emphasizes the importance of organizations building their own Large Language Models (LLMs) using retrieval-augmented generation (RAG) for better security operations. Commercial LLMs face issues like hallucinations, lack of timely data, and lack of transparency on how they were trained. RAG continuously updates and categorizes data to combat these issues, similar to a card catalog system in a library. This makes it easier for security analysts to sift through threat intelligence feeds, vulnerability alerts, and new regulations. The prototype RAG vector database by Lodge includes supporting information links with every response to provide transparency into the system. By building custom LLMs, organizations can own the data and utilize it for cybersecurity purposes.

Lodge highlights that traditional LLMs have strengths and weaknesses, and RAG can offer various use cases and implementation options. The role of AI tools in cybersecurity operations is also discussed in the interview. Lodge, with extensive experience in financial services and cybersecurity roles at major organizations, advocates for the adoption of RAG for improved security operations and compliance strategies.