Prompt Engineering for Privacy: Practical Patterns for Not Leaking PII
Every prompt sent to an LLM is a data egress point. Six concrete patterns for structuring prompts, redacting inputs, and scanning outputs so PII doesn't leak.
PII in Vector Embeddings: A Defense Guide
Embeddings look like just numbers, but research shows they are partially invertible. A practical defense guide for vector stores against PII recovery attacks.
Building a HIPAA-Compliant Medical Chatbot
Why generic RAG chatbots fail HIPAA, and a blueprint for building a medical chatbot that satisfies Safe Harbor at ingestion, retrieval, and inference.
Building a Privacy-Aware RAG System
RAG pipelines have two distinct PII leak vectors: ingestion and inference. A defense-in-depth blueprint with code, using Philter and the Philter AI Proxy.
Beyond Regex: Why General LLMs Fail at PII Discovery
Regex misses context, general LLMs over-redact and burn GPUs. The right answer is hybrid: pattern matching for the deterministic, specialized AI for the rest.
Why Using an LLM to Redact PII and PHI is a Bad Idea
Lots of posts show how to redact PII and PHI text with a large language model (LLM). Can we really just let an LLM handle it? Here is why that is a bad idea.