All You Need to Know about Sensitive Data Handling Using Large Language Models

Author(s): Hussein Jundi

Originally published on Towards AI.

A Step-by-Step Guide to Understand and Implement an LLM-based Sensitive Data Detection Workflow
Sensitive Data Detection and Masking Workflow — Image by Author

Introduction

What and who defines the sensitivity of data ?What is data anonymization and pseudonymisation?What is so special about utilizing AI for handling Sensitive Data ?

Hand-On Tutorial — Implementation of an LLM Powered Data Profiler

Local LLM Setup 1. Setting up the model server using Docker 2. Building the PromptAzure OpenAI Setup

High Level Solution ArchitectureConclusionReferences

An estimated 328.77 million terabytes of data is created daily. Much of that data flows to data-driven applications processing and enriching it every second. The increased adoption and integration of LLMs across mainstream products further intensified the use cases and benefits of utilizing text data.

Organizations processing such data on a large scale face difficulties in adhering to the requirements of sensitive data handling, whether that is regarding its security or compliance with data laws and regulations.

The direct and indirect impact of sensitive data breaches especially when sensitive data is involved can have significant financial consequences on organizations. This extends beyond immediate cost implications but can go to shake the trust and loyalty of their customer base.

Impact of Data Breach — IBM Data Breach Report

Sensitive data is a critical concept in the context of data protection and privacy. On a… Read the full blog for free on Medium.

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Published via Towards AI