Privitar and BigID have partnered to provide organizations with an integrated, automated approach to tackling some of the biggest challenges associated with deriving valuable insights from sensitive data.
The integration of Privitar’s privacy engineering platform and policies with BigID’s data discovery and classification ensures that analytics teams can make use of well-defined, high resolution, de-identified datasets for their programs, and remove manual steps for privacy-aware data pipeline provisioning.
“Timely data access and minimizing privacy risk are critical success factors for today’s data leaders,” said Jason McFall, CTO of Privitar. “The partnership between Privitar and BigID makes both of these possible, enabling enterprises to leverage their data safely and at great speed.“
Today, data engineering and data science teams depend on data derived from multiple sources to drive new insights. Combining data from multiple sources amplifies the risks that individual data subjects are inappropriately profiled or re-identified.
Also, privacy compliance mandates such as the EU GDPR and the California Consumer Privacy Act limit the use of data to the assigned purpose of processing. Organizations must be able to reliably classify and provision personal data for use in a way that respects and adheres to current privacy requirements, including consistent understanding of who the data belongs to.
The integration of BigID’s machine learning-driven discovery, classification, and labeling for sensitive and personal data, combined with Privitar’s comprehensive de-identification, policy management and data provisioning enables data-driven enterprises to both streamline and de-risk their data pipelines.
API-level integration of sensitive data discovery and policy-driven de-identification allows for automated metadata exchange, and enables fast, efficient and standardized data protection and provisioning at scale.
Organizations can maximize the value of their sensitive data by realizing seamless data discovery, programmatic tagging of categories, automated data provisioning, and consistent privacy preservation.
With Privitar and BigID, organizations can leverage their most sensitive data whether it is in the cloud, on-prem or hybrid, and fuel their digital transformation across analytics, data lake, machine learning and artificial intelligence initiatives.
Through the integrated solution, customers can:
- Accelerate time from data ingestion to data usage by building privacy into their data pipelines
- Identify and classify sensitive data to automatically apply privacy protections
- Quickly find and prepare data for analyses and models without coding or scripting
- Eliminate slow, error-prone manual data scrubbing processes and automate Safe Data provisioning at scale
- Systematically adhere to privacy policies and procedures, with the ability to audit actions
- Consistently apply privacy protections across all data sources and environments
“The integration of Privitar and BigID provides a powerful and scalable approach to sensitive data discovery and analytics across the entire enterprise,” said Nimrod Vax, CPO of BigID.
“Our customers can draw on the combined discovery and performance at scale of BigID and Privitar technologies to make data available while preserving privacy for a wide range of use cases in a secure, seamless, and orchestrated process.”
Article originally published here.