At the LlamaIndex RAG-A-THON event at DataStax HQ, the focus was on implementing solutions using Retrieval Augmented Generation (RAG) while addressing cybersecurity concerns, especially regarding unsanitized data that may contain Personally Identifiable Information (PII). The importance of handling PII is underscored by privacy, identity theft risks, legal compliance, trust, financial security, and national security. The discussion highlighted how RAG models and vector databases must handle PII carefully, suggesting methods like filtering, anonymizing, encryption, and hashing to prevent leaks. The open-source library Presidio, maintained by Microsoft, was introduced as a tool for identifying and anonymizing PII in text and images, leveraging methods like Named Entity Recognition (NER), regular expressions, and rule-based logic. The text describes the integration of Presidio with LlamaIndex, enhancing PII detection and anonymization capabilities, which resulted in a successful project that won third place at the event. The next steps involve expanding customization and anonymization options within this integrated solution.