Filter sensitive information from text

Philter identifies and removes sensitive information such as PHI and PII from natural language text. Philter can be deployed in less than 10 minutes in AWS, Azure, and Google Cloud Compute Engine.

 

Philter

Philter® Solution Gallery

This is a collection of example solutions built with Philter to find and remove sensitive information from text. You are welcome to copy these solutions and tailor them to your use-cases and challenges.

Featured Solution: Apache NiFi and Philter

Topics: Data in motion, streaming text, data flow, Apache NiFi
Required Coding: None

Use Philter with Apache NiFi to remove sensitive information from text in your data flow. Philter can process the content of flowfiles to redact sensitive information without requiring any custom NiFi processors or code.

View the Solution

Solution: Using AWS Kinesis Firehose Transformations to Filter Sensitive Information from Streaming Text

Topics: Data in motion, streaming text, data flow, AWS, Kinesis, Lambda
Required Coding: Python

Use Philter to find and remove sensitive information from text inside an AWS Kinesis Firehose stream. Use a Transformation Lambda function to execute Philter on the streaming text.

View the Solution

Solution: Using Philter with Microsoft Power Automate (Flow)

Topics: Data in motion, streaming text, data flow, Microsoft Power Automate
Required Coding: None

Use Philter to find and remove sensitive information from text inside a Microsoft Power Automate flow. Using an HTTP step, we can integrate Philter with our flow to filter text.

View the Solution