Knowledge Extraction System - A Template for Security Architect's Workflow Augmentation Leveraging LLM.
In the most basic sense, LLMs are a useful piece of technology for encoding information. Trained on vast datasets to understand and generate human-like text, they excel in extracting and synthesizing knowledge from diverse sources, making them powerful for information retrieval. They are a useful tool for augmenting day-to-day tasks or workflows when used right. However, LLMs can struggle with providing precise and relevant information within user prompts due to potential biases, overfitting, and lack of specific context. Privacy concerns arise as LLMs may inadvertently retain and expose sensitive data from their training sets, risking user confidentiality and data security. When organizations use third-party LLM API, there is also a risk of providing proprietary information to the model that can later be leaked.
While LLMs offer broad applicability across domains, their adopt