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Definition: Identify PII spans within a sample (both input and output). The current model detects the following precisely defined categories:
  • Account Information: Bank account numbers, Bank Identification Code (BIC) and International Bank Account Number (IBAN).
  • Address: A physical address. Must contain at least a street name and number, and may contain extra elements such as city, zip code, state, etc.
  • Credit Card: Credit card number (can be full or last 4 digits), Card Verification Value (CVV) and expiration date.
  • Date of Birth: This represents the day, month and year a person was born. The context should make it clear that it’s someone’s birthdate.
  • Email: An email address.
  • Name: A person’s full name. It must consist of at least a first and last name to be considered PII.
  • Network Information: IPv4, IPv6 and MAC addresses.
  • Password: A password.
  • Phone Number: A phone number.
  • Social Security Number (SSN): A US Social Security Number.
  • Username: A username.
Calculation: We leverage a Small Language Model (SLM) trained on proprietary datasets. Usefulness: Automatically identify PII occurrences in any part of the workflow (user input, chains, model output, etc), and respond accordingly by implementing guardrails or other preventative measures. Explainability: To highlight which parts of the text were detected as PII, click on the icon next to the PII metric value. The type of PII detected along with the model’s confidence will be shown on the input or output text.
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