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Enabling Data Sharing without Risking Compliance

Data analytics rarely occur in a vacuum. Analysts collaborate and share scripts, dashboards, and models as they attempt to extract value from their organization’s most critical business asset: data. However, most controls protect only the raw data, and fail to consider the security implications of production-level data.

Common Data Sharing Scenarios:

  • Collaborate and Share Data Securely Inside the Organization
  • Securely Share Data with Third Parties
  • Implement a Secure Data Exchange Platform
  • Assure Lawful Data Use Agreements
Immuta Projects

Immuta projects are virtual walled gardens for teamwork, protected through a variety of policy enforcement options. When users work under a project, they can share data outputs, models, and dashboards freely and without worry.

Permission Equalization

Collaboration can be difficult and risky when users have different data permissions. With Immuta, you can equalize the permissions of everyone in a project with a click of a button, saving weeks or months of data cleansing time.

Masking Techniques

Add re-identification techniques such as format-preserving encryption and reversible masking that re-identify data during collaboration and sharing events.

Data Fingerprints

Take fingerprints — or snapshots — of your data over time to understand how policies on data sources have impacted your downstream data, analytics, and dashboards.

Immuta provides a well-controlled workflow for de-anonymized investigation that fully conforms with health industry data privacy guidelines.

Halim Abbas Chief AI Officer, COGNOA

Additional materials

June 13, 2022
How to Manage Databricks Data Clean Rooms
May 26, 2021
Enabling Third Party Data Sharing with Databricks Delta Sharing
January 8, 2019
Data Governance Anti-Patterns: The Copy & Paste Data Sharing Method

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