On-Device Input Analysis to Proactively Enhance Data Security in Large Language Models

Large Language Models (LLMs) have become essential in many industries, helping with productivity gains and automation. However its utilisation creates concerns regarding the unintentional disclosure of sensitive information, which puts security and privacy in jeopardy. In order to reduce these risks, this qualitative study suggests a technique called “Input Refining” that involves methodically identifying and removing personal data from LLM inputs. The process entails a careful examination of the input data, which is then paraphrased to exclude sensitive material while maintaining the input's semantic integrity. Through the incorporation of Input Refining into AI workflows, entities can enhance data security and privacy procedures, cultivating conscientious implementation of LLMs. By providing a workable solution to the problem of unintentional information security breaches in the age of large language models, our study adds to the ongoing discourse on AI ethics and data privacy.