Tags in Immuta
Tags have several uses; they can be used to drive policies. Data Owners can use tags on the local level for subscription or data policies directly in a data source. But Immuta recommends tags to be utilized on the Global level with Subscription and Data Policies. In doing this company wide data security restrictions can be controlled by the administrators and governors, while the users and data owners need only to worry about tagging the data correctly.
Tags can also be used to generate Immuta Reports for anything from insider threat surveillance and data access monitoring. Lastly, tags can be used to drive search results in the Immuta UI.
Governors can create tags or import tags from external catalogs in the Governance UI. Data Owners and Governors can then apply these tags to or remove them from projects, data sources, and/or specific columns within the data sources.
Best Practice: Managing Tags
Use the minimum number of tags possible to achieve the data privacy needed.
Tags include the following UI components:
Tag Tooltips: When you hover over a tag on the data source page, you will see information about the tag, including where it was created (i.e., Immuta or an external catalog), whether the tag was discovered by SDD, and the full name of the tag.
Consistent Tag Visuals: Tags are presented as
Parent . Child . Childwith
.between each level.
Search Experience: When searching the UI for tags to add to a data source, you will see all the parent tags first. Then you can scroll or type the exact tag to select a specific child tag.
Sensitive Data Discovery
Best Practice: Use Sensitive Data Discovery
Sensitive Data Discovery can improve your ability to secure your data by automatically tagging sensitive entities, enabling the scalable implementation of Global Policies. Immuta highly recommends the use of this feature in tandem with verification of tags on all data sources.
Sensitive Data Discovery (SDD) helps to ensure sensitive data is properly managed and governed, providing fast identification for entities in columns such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data, and more.