Secure Data Sharing at Scale
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.
What is data sharing?
Data sharing refers to the provision of information from one party to another, and can be done both internally and externally.
What are the three types of data sharing?
The three most common types of data sharing are internal data sharing, wherein teams collaborate and share data within their organization, external or third-party data sharing, in which data is shared outside an organization’s network and may be subject to data use agreements or transfer regulations, and data exchange platforms, which are environments where data can be shared among multiple organizations and stakeholders on a broad scale.
What are the benefits of data sharing?
In an increasingly interconnected world, the benefits of data sharing are becoming more pronounced. Sharing data internally can benefit organizations by accelerating data-backed decision making, which can save time and money in the short- and long-term. External data sharing similarly allows teams to mine insights more quickly, efficiently, and holistically. This can reduce the risk of human error and subjectivity, leading to everything from more informed public health decisions to better budgeting and resource allocation.
What is a clean room for data?
A clean room for data refers to an environment in which distributed data can be shared securely, whether with other teams in the same organization or with third parties. A common use case for data clean rooms is in the advertising industry because they allow companies to share aggregated customer data with advertisers, without risk of breaching customer privacy standards.
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