Privacy-aware Correlation, In Addition to Classification and Cataloging

Data Privacy

Software-based data discovery typically falls into one of two camps depending on whether the tool is aimed at security or data governance professionals. Security professionals typically avail themselves of classification-centric products that emerged post PCI to find specific types of sensitive data like credit card numbers or national IDs. Data governance professionals, conversely, are most familiar with metadata catalogs that surface technical metadata in structured data sources that can provide a cursory view of what kind of data resides where.

Neither approach is adequate for privacy. Both lack the ability to identify contextually personal information (PI); they lack the data coverage to give a broad-based view of what data resides where, and perhaps most importantly – they have no identity context and so cannot map or correlate data back to a person.

Privacy is about people.

Without identity context, it’s impossible to identify what data belongs to what individual. Individual data rights are the primary purpose of privacy regulations like GDPR and CCPA. BigID therefore rethought data discovery for the privacy era and patented a first-of-its-kind approach to data discovery and intelligence that puts identity at the center. With BigID’s privacy-aware correlation based approach to discovery, organizations can both find “what” data and “whose” data.

Providing data accountability to consumers requires data accounting down to an identity level.

With BigID, organizations no longer have to choose. They get market leading classification and catalog capabilities, in combination with correlation so that they can get the privacy-centric detail required for meeting data right regulations – all without compromising the views and insight that security and data governance professionals need.

One source of data truth across three disciplines.