Immuta, a data security leader, is making key enhancements to its Data Security Platform for Databricks, enabling data teams to leverage Immuta's full platform capabilities to unlock value from data, reduce costs, and speed up innovation while maintaining strong data security posture.
“We continue to work closely with Databricks to ensure that our technology platforms integrate seamlessly with every product release. With this update, Immuta provides a native, seamless integration with Unity Catalog’s latest release, helping Databricks to simplify and scale data security in the cloud,” said Steve Touw, CTO, Immuta. “As an industry, it’s time we tackle the next frontier for the cloud: data security. We are thrilled to be accomplishing this with Databricks in our latest integration update and enhancements as we continue our track record of innovation.”
These updates include a new native integration with Databricks Unity Catalog that connects customers with Immuta’s latest platform capabilities, providing localized Sensitive Data Discovery, enhanced security and access control for artificial intelligence (AI) workloads, and enhanced Data Security Posture Management, according to the company.
Through this new integration with Databricks, Immuta abstracts and orchestrates the complexities of managing data security so that joint customers can focus on getting value from their data faster.
Immuta leverages and expands on new features added to Databricks Unity Catalog, such as row filtering and column masking capabilities to provide data discovery, security and access control, and comprehensive data monitoring.
With Immuta’s new localized Sensitive Data Discovery, all sensitive data processing remains within Databricks so that it is easy to conform to data localization and data security requirements.
Key capabilities available to Databricks customers include:
- Providing auto-tagging capabilities across a lakehouse environment
- Applying 60-plus prebuilt identifiers and easily define data
- Ability to build your own classifiers with acceptable confidence levels
- Exploring how tags are applied across a data lakehouse
The push toward building machine learning (ML) models and AI applications continues to accelerate, yet there remains a clear gap in addressing (and automating) data security and governance with regard to AI workloads on the modern data lakehouse.
By leveraging Immuta on the Databricks Lakehouse Platform, organizations can enhance access controls, monitor security, and maintain compliance to safeguard their AI workloads effectively.
Today’s data teams struggle with over-provisioning access, detecting insider threats, and assessing impact in a quantifiable manner. Immuta Detect provides timely insights into risky user data access behavior, enabling data security posture management and risk remediation above policy thresholds.
For more information about this news, visit www.immuta.com.