Philter finds, identifies, and removes sensitive information, such as Personally Identifiable Information (PII) and Protected Health Information (PHI), from text.

Using state of the art natural language processing, Philter analyzes text for many types of sensitive information that when found can be redacted, masked, encrypted, hashed, replaced by static values, or anonymized by realistic random values. You can apply conditional logic based on attributes such as the type or content of sensitive information to have fine-grained control over how sensitive information is identified and manipulated and to generate alerts.

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

Philter's API provides an endpoint for submitting raw text and receiving back the filtered text. Philter can scale horizontally behind a load balancer for increased throughput. Open source API clients are available or use the free Philter Studio desktop application to interact with Philter.