Machine learning engineer || Azure certified AI Engineer 🤖 || Building production grade Knowledge Graphs 🧠
🛡 Protecting Privacy in the Age of AI: Introducing LLM-Guard Are you working with large language models? Concerned about accidentally exposing sensitive data? Check out LLM-Guard, an open-source library that helps catch and redact personally identifiable information (PII) in LLM inputs and outputs! Here's a quick 4-step guide to using LLM-Guard: 1. Easy Installation: Get started with a simple pip install llm-guard 2. Import Key Components: Bring in the Vault, Anonymize scanner, and model of your choice to detect PII 3. Set Up Your Prompt: Define the text you want to analyze, potentially containing sensitive info 4. Scan and Protect: Use the Anonymize scanner to detect and redact PII, then retrieve the sanitized data from the Vault. You can remove the identified PII from the prompt by simply passing in the prompt to the 𝘴𝘤𝘢𝘯() 𝘮𝘦𝘵𝘩𝘰𝘥. 👇 𝘴𝘢𝘯𝘪𝘵𝘪𝘻𝘦𝘥_𝘱𝘳𝘰𝘮𝘱𝘵 , 𝘪𝘴_𝘷𝘢𝘭𝘪𝘥 , 𝘳𝘪𝘴𝘬_𝘴𝘤𝘰𝘳𝘦 = 𝘴𝘤𝘢𝘯𝘯𝘦𝘳.𝘴𝘤𝘢𝘯(𝘱𝘳𝘰𝘮𝘱𝘵) The result? Peace of mind knowing that sensitive details like SSNs, phone numbers, and credit card info are automatically caught and protected! 🔒 #LLM #AI #Security #LLMOps #AIEthics
ML Engineer at SpatialChat | Researcher
5moAwesome 👏