video1.0<iframe src="https://www.loom.com/embed/848661f6acec4384bc4ad4a8ac797860" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/848661f6acec4384bc4ad4a8ac797860-3e8e65f257761ec9.gif297.388This Loom demonstrates a multi-layer security guardrail to prevent data leakage from LLM workflows, addressing OWASP LLM 06. It shows standard P and secret detection using Microsoft Presidio NER models plus custom Regex, including a case where an SSN was caught with 100% confidence and an internal API key was fully redacted in the sanitized output. It then covers proprietary code detection by comparing sentence-transformer vector embeddings against a forbidden internal code vault, detecting 78% semantic similarity and blocking the response when internal code is likely present. The tool uses adjustable thresholds for tuning sensitivity, is built in Python, and is designed to run as a containerized sidecar with source code and documentation on GitHub.