Detection confidential information by large language models

Abstract

In today's digital age, the protection of personal and confidential customer data is paramount. With the increasing volume of data being generated and processed, organizations face significant challenges in ensuring that sensitive information is adequately protected. One of the critical steps in safeguarding this data is the detection and classification of personal and confidential information within text documents. This process involves identifying sensitive data, classifying it appropriately, and storing the results in a semi-structured format such for further analysis and action. The need for detecting and classifying sensitive data is driven by regulatory compliance, data security, risk management, and operational efficiency. Various methodologies, including rule-based systems, machine learning models, natural language processing (NLP), and hybrid approaches, are employed to detect and classify sensitive data. Large Language Models (LLMs) like GPT-3 and BERT, trained on extensive text data, are transforming data management and governance, areas crucial for SOC 2 Type 2 compliance. LLMs respond to prompts, guiding their output generation, and can automate tasks like data cataloging, enhancing data quality, ensuring data privacy, and assisting in data integration. These capabilities can support a robust data classification policy, a key requirement for SOC 2 Type 2.