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 through the model.
Using an LLM or Pattern-based Rules for PII/PHI Redaction
In our data-driven world, being able to protect Personally Identifiable Information (PII) and Protected Health Information (PHI) is imperative. Whether you’re securing customer data, complying with regulations like GDPR or HIPAA, or simply aiming for responsible data handling, the need to effectively redact sensitive information is crucial. Today, there are two primary approaches: leveraging the…
Why Using an LLM to Redact PII and PHI is a Bad Idea
We have seen a lot – and you probably have to – posts on various social media and blogging platforms showing how you can redact text using a large language model (LLM). They present a fairly simple solution to the complex problem of redaction. Can we really just let an LLM handle our text redaction…
Philter as an AI Policy Layer
A policy layer is an important part of every source of AI-generated text. An AI policy layer is an important part of every source of AI-generated text because it inspects the AI-generated text to prevent sensitive information from being exposed. A policy layer can help remove information such as names, addresses, and telephone numbers from…