Can I review the redacted document before it's sent to an LLM?

Yes, Masquerade allows you to preview the redacted PDF document, ensuring you have full control and visibility over the anonymized content before it's sent to any LLM.

What types of sensitive data can Masquerade redact?

Masquerade is designed to automatically detect and redact various types of sensitive data, including names, email addresses, dates, and other identifiable entities within your PDF files.

Which large language models (LLMs) does Masquerade work with?

Masquerade integrates seamlessly with Claude desktop via the Model Context Protocol (MCP), allowing you to securely prepare PDF content before interacting with Claude.

Masquerade

Name: Masquerade
Author: postralai

bypostralai

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Data Science & ML

Productivity & Workflow

Security & Testing

Provides a privacy firewall for large language models, automatically detecting and redacting sensitive information from PDF documents before processing.

Large Language Models (LLMs) like Claude or GPT offer immense power but necessitate raw input, posing significant risks when handling sensitive documents such as contracts, medical records, or internal files. Masquerade addresses this by acting as a privacy firewall, intelligently detecting and redacting personal or confidential data (like names, emails, and entities) from PDFs. It allows users to preview the redacted document before interaction with an LLM, ensuring data privacy and compliance while leveraging AI capabilities. The system processes PDFs, uses an isolated AI for sensitive data identification, and then generates a summary including masked data and redaction details for the user and the LLM.

Key Features

01Automatically detects sensitive data (names, emails, dates, entities)

02Redacts sensitive data from PDF files

03Allows preview of redacted documents before sending to LLMs

0443 GitHub stars

05Integrates seamlessly with Claude desktop via Model Context Protocol (MCP)

06Generates summaries with masked sensitive data and redaction counts

Use Cases

01Anonymizing medical records for AI analysis

02Handling internal company documents securely with external LLM services

03Safely processing confidential contracts with LLMs