Which security feature masks sensitive data before sending prompts to external LLMs?

The correct choice highlights the importance of safeguarding sensitive information when interfacing with external Large Language Models (LLMs). Data Masking is the process of obscuring specific data within a database so that it remains usable without compromising privacy or confidential details. This technique transforms sensitive information into a format that is accessible for processing while preventing exposure of the actual data.

When interacting with external systems, such as LLMs, data masking is critical. It ensures that any prompts sent do not reveal personally identifiable information (PII) or other sensitive data, thus protecting against potential data breaches or misuse. By employing data masking, organizations can adhere to compliance requirements and foster a secure environment for data handling, which is essential in today’s data-driven landscape.

The other choices involve relevant concepts but do not specifically address the prevention of sensitive data exposure. For instance, dynamic grounding relates to improving the relevance and reliability of responses from LLMs, while secure data retrieval focuses on accessing information safely. Toxicity detection, on the other hand, is about identifying harmful or inappropriate content. None of these options directly accomplish the critical task of obscuring sensitive data as effectively as data masking does.