Immuta Launches Native Offering for Databricks With Enhanced Security and Collaboration Features

Immuta for Databricks features secure data collaboration, simplified regulatory compliance, and advanced anonymization techniques

New 14-day trial makes it easy for Databricks customers to try Immuta at no cost

Immuta, the automated data governance company, announced an enhanced platform integration with Databricks, the data and AI company. Immuta for Databricks – a new, native offering for Databricks customers – enhances data engineering productivity and data security by automating fine-grained access control and privacy protection natively within Databricks and Delta Lake. Immuta’s latest release unlocks new data science opportunities and outcomes, simplifies regulatory compliance, and further enhances security and privacy controls.

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“Databricks provides unmatched scalability, flexibility, cost savings, and performance. However, strict data protection rules and regulations create compliance and technical limitations when it comes to utilizing protected data for analytics and data science,” said Steve Touw, CTO, Immuta. “Data teams are exposed to new levels of risk, making it challenging to manage and prepare sensitive data for data scientists to access in a compliant, self-service way, but also for those same analysts to securely share and publish their work. The latest Immuta for Databricks enhances automation capabilities required to overcome these challenges.”

With today’s release, Immuta for Databricks now provides secure data collaboration for analysts and data scientists; advanced anonymization techniques; and simplified compliance with data privacy regulations like the CCPA, GDPR, HIPAA, and COPPA. Immuta for Databricks will be demonstrated at this week’s virtual Spark + AI Summit 2020. The company will also host customer sessions on Wednesday, June 24th at 11:30am PDT with Artis Consulting and Thursday, June 25th at 11:30am PDT with WorldQuant Predictive.

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Key features and benefits of the latest Immuta for Databricks release include:

• Secure data collaboration: Protecting “gold” tables, ready for analytics and ML use, with fine-grained controls is only the start of protecting your data. When data analysts create new products via feature engineering or transformations (views, tables), writing those products out can cause data leaks because the analysts may be able to see more than other analysts that can see their output. Leveraging Immuta’s patented approach to equalizing permissions across users to prevent data leakage, data teams can now create projects in Immuta with appropriate WRITE access to publish derivative work for secure data collaboration on a Databricks cluster.
• Advanced privacy: Privacy-enhanced analysis of sensitive data is a notoriously complex task. It often involves the risky and time-consuming implementation of difficult-to-understand-and-debug statistical techniques, data-specific code, and complex ELT processes. The latest Immuta for Databricks can dynamically apply randomized response to achieve local differential privacy at the column level, without writing a single line of code. Local differential privacy, like its close relative differential privacy, places mathematically guaranteed limits on an attacker’s ability to exploit sensitive data in attempts to draw harmful conclusions about individual data subjects.
• Simplified compliance with data privacy regulations: While Immuta pioneered the intuitive, no-code Policy Builder to support data privacy regulations, Immuta for Databricks enhances the ability for data governance and compliance teams to automate the manual processes required for compliance with the CCPA, GDPR, HIPAA, COPPA and other regulations.

“At Databricks, we are committed to providing data teams control over sensitive data as they scale their business analytics and machine learning projects,” said Michael Hoff, SVP Business Development and Partners at Databricks. “Immuta’s new features broaden our native integration by simplifying regulatory compliance, offering advanced anonymization techniques, and allowing data scientists to publish derivative work with automated security and privacy protections.”

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