Filter sensitive information from text

Philter finds, identifies, and removes sensitive information, such as PHI and PII, from natural language text. Run it in the cloud or in containers.



Philter finds, identifies, and removes sensitive information, such as Personally Identifiable Information (PII) and Protected Health Information (PHI), from text. Philter can help you with text deidentification, anonymization, masking, and redaction.

Philter is available for AWS, Azure, Google Cloud, and from DockerHub.

AWS Marketplace

Redact and replace with realistic values

Keep your documents useful! Philter can replace sensitive information with similar but random values so documents can remain useful for secondary purposes. Philter can generate random names, phone numbers, and more.

Customizable redaction logic

You can apply different redaction logic based on conditions. Philter can redact sensitive information based on conditions such as the population of a zip code or the content and type of the sensitive information.

Many types of sensitive information

Philter can currently identify:  Ages, Bitcoin Addresses, Cities, Counties, Credit Cards, Custom Dictionaries, Custom Identifiers (medical record numbers, financial transaction numbers), Dates, Drivers License Numbers, Email Addresses, IBAN Codes, IP Addresses, MAC Addresses, Passport Numbers, Persons' Names, Phone/Fax Numbers, SSNs and TINs, Shipping Tracking Numbers, States, URLs, VINs, Zip Codes

Tailored for different domains

Philter’s NLP model can be changed based on your domain and use-case to offer increased performance. We offer specialized models for working with healthcare and COVID-19 text. We are constantly improving our models to provide the best possible performance.


Use Philter’s API to filter text. Use our open-source SDKs, curl, or any other tool of your choosing.

Quick start guide

Follow the quick start guide to get up and running and filtering text in as little as 15 minutes.