ML Driven Data Classification
Intelligently Classify Sensitive Data & Files Everywhere
Data Discovery and Classification
Comprehensive discovery of sensitive data combined with identifier classification and file categorization remain fundamental information security objectives.
With the emergence of both data privacy protection compliance mandates and growing consumer concerns, the challenge is compounded: enterprises now not only have to discover and classify sensitive data across complex data landscapes, but also account for whose data they are collecting and processing, and how.
In contrast to approaches that rely on pattern matching to find only specific identifiers, BigID’s leverages machine learning algorithms to understand how data values relate and map to an identity for greater coverage, accuracy, and automated privacy insights.
Scale and Coverage for the Modern Enterprise
Discover, index and inventory personal data at scale for legacy data sources, big data repositories, file shares, cloud services, and SaaS applications
ML-Driven Classification
Understand the relationship between data values first, and then apply ML models with confidence scoring and business glossary integration to enhance classification accuracy
Correlation plus Classification
Correlation-based approach leverages context to uncover “dark data” and infer via correlation what constitutes personal data attributes
Cataloging plus Classification
Integrate metadata enrichment and classification to find and catalog additional personal data, and associate sensitivity risk
Intelligent Labeling and Tagging
Assign tags to files and labels to objects across all data findings through policies written in an intuitive query-based language for policy enforcement bu Azure Information Protection, or other enforcement mechanisms