๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐๐ฃ๐ฅ-๐๐ผ๐บ๐ฝ๐น๐ถ๐ฎ๐ป๐ ๐ฅ๐๐ ๐ช๐ถ๐๐ต ๐ก๐ฒ๐ ๐ผ ๐๐ด๐ฒ๐ป๐ ๐ง๐ผ๐ผ๐น๐ธ๐ถ๐
Companies often build RAG systems faster than they perform security audits.
A common mistake happens in HR departments. You upload employee files, medical disclaimers, and salary FAQs into a vector database. Six months later, your LLM starts leaking names, phone numbers, and salary ranges.
The data is in your retrieval context. This violates GDPR and CCPA rules. Privacy must be part of your design, not an afterthought.
I built a solution using the NeMo Agent Toolkit to create a PII-aware RAG pipeline.
๐๐ผ๐ ๐ถ๐ ๐๐ผ๐ฟ๐ธ๐: The pipeline cleans data before it ever reaches your database.
- Original Document โ Piiranha PII Detection โ Redact โ Vector Database.
- User Query โ NAT ReAct Agent โ RAG Retrieval โ LLM Response.
The Piiranha model runs on a GPU. I tested it on an RTX 3090.
๐๐ฒ๐ฟ๐ฒ ๐ฎ๐ฟ๐ฒ ๐๐ต๐ฒ ๐ฟ๐ฒ๐๐๐น๐๐ ๐๐. ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ฃ๐ฟ๐ฒ๐๐ถ๐ฑ๐ถ๐ผ (๐๐ฃ๐จ): โข Overall F1 Score: 0.9866 (Piiranha) vs 0.7116 (Presidio). โข Speed: 10,643 tokens/s (Piiranha) vs ~2,000 tokens/s (Presidio). โข Latency: 6.6 ms per sample (Piiranha) vs ~9.9 ms per sample (Presidio).
Piiranha is 5x faster and significantly more accurate. It covers 17 entity types including emails, passwords, and social security numbers.
๐ช๐ต๐ ๐๐ต๐ถ๐ ๐ฎ๐ฝ๐ฝ๐ฟ๐ผ๐ฎ๐ฐ๐ต ๐๐ถ๐ป๐: โข Data Security: The vector database stays clean. Even if your DB leaks, it contains no private info. โข Low Latency: Redaction happens during ingestion. It does not slow down user queries. โข Compliance: It follows the GDPR principle of data minimization. โข Observability: Using NVIDIA NeMo Agent Toolkit, you can track every PII detection and tool call through OpenTelemetry.
You can even turn this into an MCP server to let tools like Claude Desktop call your PII detector directly.
Stop risking your data. Build RAG systems that protect privacy by default.
Optional learning community: https://t.me/GyaanSetuAi