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.

Fast time to valueScale 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

Reduced False PositivesML-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

BigData Scale

Correlation plus Classification

Correlation-based approach leverages context to uncover “dark data” and infer via correlation what constitutes personal data attributes

Structured and UnstructuredCataloging plus Classification

Integrate metadata enrichment and classification to find and catalog additional personal data, and associate sensitivity risk

Satisfy Privacy RegulationsIntelligent 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