Data discovery & classification
Bearer discovers and classifies data flows by scanning your code repositories so you can:
  1. 1.
    Avoid shadow IT and data: maintain a complete and accurate picture of your application architecture and data flows.
  2. 2.
    Stop wasting time gathering information manually.
  3. 3.
    Stop bothering your developers with manual questionnaires.

How does data discovery work?

Bearer primarily discovers data (e.g., lastname) by scanning OpenAPI, SQL, GraphQL and Protobuf files.
Bearer also scans the rest of the codebase in a complementary way to discover and classify data. The general mechanics is that it looks for Objects (e.g., User) and their properties or attributes (e.g., lastname).
For this, Bearer supports:
  • C#: 98,3% true positive, 1.7% false positive.
  • Golang: 93,6% true positive, 5,1% false positive.
  • Java: 80.6% true positive, 16% false positive.
  • Javascript/Typescript: 99% true positive,1% false positive.
  • PHP: 78% true positive,22% false positive
  • Python: 72% true positive, 28% false positive.
  • Ruby : 77.5% true positive, 22.5% false positive.

How does data classification work?

Data types are then classified by a built-in machine learning model.
Data types include: Passwords, PIN, Mother's Maiden Name, Browsing Behavior, Telephone Recordings, Voice Mail, Emails, IP address, Mac address, Device identifier, Browser Fingerprint, Email Address, Physical Address, Telephone Number, Credit Records, Credit Worthiness, Credit Standing, Credit Capacity, Convictions, Charges, Pardons, Age Range, Physical Traits, Income Brackets, Geographic, Biometric Data, Race, National origin, Ethnic Origin, Spoken Languages, Accents, Family Structure, Siblings, Offspring, Marriages, Divorces, Relationships, Credit Card Number, Bank Account, Firstname, Lastname, Fullname, Username, Unique Identifier, Passport Number, ID Number, Call Logs, Links clicked, Demeanor, Attitude, Religious Beliefs, Philosophical beliefs, Thoughts, Knowledge, Country, GPS Coordinate, Room Number, Physical and mental health, Drugs test results, Disabilities, Family health history, Personal health history, Health Records, Blood Type, DNA code, Prescriptions, Cars, Houses, Apartments, Personal Possessions, Height, Weight, Age, Hair Color, Skin Tone, Tattoos, Gender, Piercings, Opinions, Intentions, Interests, Favorite Foods, Colors, Likes, Dislikes, Music, Job Titles, Salary, Work History, School attended, Employee Files, Employment History, Evaluations, References, Interviews, Certifications, Disciplinary Actions, Character, General Reputation, Social Status, Martial Status, Religion, Political Affiliation, Interactions, Gender identity, Sexual Preferences, Sexual History, Friends, Connections, Acquaintances, Associations, Group Membership, Purchases, Sales, Credit, Income, Loan Records, Transactions, Taxes, Purchases and Spending Habits, Image, Conversation.