BigID & Privitar: End-to-End Safe Data Provisioning

Best-of-Breed Integration from Discovery through Provisioning

The integration of BigID and Privitar efficiently delivers well-defined, high resolution, de-identified datasets to users so they can accelerate insights. This powerful combination enables organizations to effectively leverage sensitive data in decision making while respecting and maintaining customer privacy.

BigID’s machine learning-driven data discovery, classification and labeling and Privitar’s comprehensive de-identification, policy management and provisioning together enable data-driven enterprises to streamline and automate their data pipelines.

Rapidly Build and Scale Safe Data Pipelines

Combining BigID and Privitar enables data engineers to rapidly build Safe Data pipelines. These pipelines can discover what data needs protection before it is accessed, automate classification and apply privacy protections directly to data regardless of source or environment. The reliability and performance of BigID and Privitar means enterprises can safely scale-up Safe Data for a wide range of secondary use cases throughout their organizations.

Minimizing Manual Processes and Decision-Making Maximizes Compliance and Control

By tagging and indexing sensitive data, BigID’s data discovery and classification system provides Privitar with rich context that informs the definition of Privacy Policies and data access. This integrated solution automates provisioning of de-identified data and enables auditing on origin, lineage and authorized users for each dataset via Privitar Watermarks™.

Solution Benefits at a Glance

With BigID and Privitar, organizations can leverage even their most sensitive data whether it is in the cloud, on-premise or hybrid environments. Enterprises can harness this data to fuel their analytics, data lake, machine learning and artificial intelligence initiatives and the insights they provide.

• Understand your sensitive data and optimize privacy protections accordingly
• Quickly find and prepare data for analyses and models without coding or scripting
• Eliminate slow, error-prone manual processes and automate Safe Data provisioning at scale
• Systematically adhere to policies and procedures
• Consistently apply privacy protections across all data sources and environments