Data Discovery
Last updated
Was this helpful?
Last updated
Was this helpful?
Sensitive data discovery (SDD) is an Immuta feature that uses data patterns to determine what type of data your column represents. Using identification frameworks and identifiers, Immuta evaluates your data and can assign the appropriate tags to your data dictionary based on what it finds. This saves the time of identifying your data manually and provides the benefit of a standard taxonomy across all your data sources in Immuta.
Sensitive data discovery is supported for from the following technologies:
or
: Sensitive data discovery for Starburst (Trino) is currently in public preview and available to all accounts. .
: Sensitive data discovery for Redshift is currently in private preview and available to all accounts. Reach out to your Immuta representative to enable it on your tenant.
To evaluate your data, SDD generates a SQL query using the identification framework's identifiers; the Immuta system account then executes that query in the remote technology. Immuta receives the query result, containing the column name and the matching identifiers but no raw data values. These results are then used to apply the resulting tags to the appropriate columns.
This evaluating and tagging process occurs when identification runs, which happens automatically from the following events:
A new data source is created.
Schema monitoring is enabled, and a new data source is detected.
Column detection is enabled, and new columns are detected. Here, SDD will only run on new columns, and no existing tags will be removed or changed.
A user manually triggers it from the data source health check menu.
A user manually triggers it from the identification frameworks page.
A user manually triggers it through the API.
An identification framework is a group of identifiers that will look for particular criteria and tag any columns where those conditions are met.
An identifier is a criteria and the tags to apply to data that matches the criteria. When Immuta recognizes that criteria, it can tag the data to describe the type.
Criteria are the conditions that need to be met for resulting tags to be applied to data.
SDD only supports regular expressions (regex) written in RE2 syntax.
Regex: This criteria contains a case-insensitive regular expression that searches for matches against column values.
Dictionary: This criteria contains a list of words and phrases to match against column values.
Column name: This criteria includes a case-insensitive regular expression matched against column names, not against the values in the column. The identifier's tags will be applied to the column where the name is found. Multiple column name identifiers can match a column and be applied.
The amount of time it takes to run identification on a data source depends on several factors:
Columns: The time to run identification grows nearly linearly with the number of text columns in the data source.
Row count: Performance of identification may vary depending on the sampling method used by each technology. For Snowflake, the number of rows has little impact on the time because data sampling has near-constant performance.
Views: Performance on views is limited by the performance of the query that defines the view.
Deprecation notice
Support for this feature has been deprecated.
The data inventory dashboard visualizes information about your organization's data. It presents your entire data corpus within the context of the frameworks you have actively tagging your data with details like when your data was scanned last or how much of the scanned data is relevant to your active frameworks.
Data regex
Text string columns
Case-sensitive
Column name regex
Any column
Not case-sensitive
Dictionary
Text string columns
Can be toggled in the identifier definition
Immuta compiles dictionary patterns into a regex that is sent in the body of a query.
Redshift Spectrum is not supported with SDD.
The Redshift cluster must be up and running for SDD to successfully run.
The username and password auth method is fully supported with SDD.
AWS access key is supported with limitations with SDD:
redshift-data:BatchExecuteStatement
redshift-data:CancelStatement
redshift-data:DescribeStatement
redshift-data:ExecuteStatement
redshift-data:GetStatementResult
redshift-data:ListStatements
The AWS access key used to register the data source must have redshift:GetClusterCredentials for the cluster, user, and database that they onboard their data sources with.
Redshift Serverless data sources are not supported for native SDD with the AWS access key authentication method.
These limitations are only relevant to users who have previously enabled and run Immuta SDD.
Immuta has improved the performance and behavior of sensitive data discovery (SDD), so references to two types of SDD can be found in the product:
Legacy SDD was available before October 2023. It is no longer available, but some users may still see the term "legacy SDD" in the context of their data tags.
Native SDD was released in the 2024.2 LTS release. Native SDD is the only type of SDD available with a standard install. It is often just referred to as SDD.
If you had legacy SDD enabled, running native SDD can result in different tags being applied because native SDD is more accurate and has fewer false positives than legacy SDD. Running a new SDD scan against a table will change the context of the resulting tags, but no Discovered tags previously applied by legacy SDD will be removed.
Users can or the .
Sensitive data discovery (SDD) runs to discover data. These frameworks are a collection of . These identifiers contain a single and the tags that will be applied when the criteria's conditions have been met. See the sections below for more information on each component.
While organizations can have multiple frameworks, only one may be applied to each data source. Immuta has the built-in "Default Framework," which contains all the and assigns the .
For a how-to on the framework actions users can take, see the .
Each organization has a single global framework that will apply to all the data sources in Immuta by default unless they have a different framework assigned. It is labeled on the frameworks page with a globe icon. Users can bypass this global framework by .
Immuta comes with to discover common categories of data. These identifiers cannot be modified or deleted. to find their specific data.
For a how-to on the identifier actions users can take, see the .
Competitive criteria analysis: This criteria is a process that will review all the regex and dictionary criteria within the identifiers of the framework and search for the identifier with the best fit. In this review, each competitive criteria analysis identifier in the framework competes against each other to find the best and most specific identifier that fits the data. The resulting tags for the best identifier are then applied to the column. Only one competitive criteria analysis identifier will apply per column. To learn more about the competitive nature, see the .
Create a new identifier in the or with the .
Only application admins can on the Immuta app settings page. Then, data source creators can disable SDD on a data-source-by-data-source basis.
When SDD is manually triggered by a data owner, all column tags previously applied by SDD are removed and the tags prescribed by the latest run are applied. However, if SDD is triggered because a new column is detected by schema monitoring, tags will only be applied to the new column, and no tags will be modified on existing columns. Additionally, governors, data source owners, and data source experts can to prevent them from being used and auto-tagged on that data source in the future.
Identifiers: The number of identifiers being used the time to run identification.
The time it takes to run identification for all newly onboarded data sources in Immuta is not limited by SDD performance but by the execution of background jobs in Immuta. when onboarding a large number of data sources to ensure the advanced settings are set appropriately for your organization.
For users interested in testing SDD, note that the built-in identifiers by Immuta require a 90% match to data to be assigned to a column. This means that with synthetic data, there may be situations where the data is not real enough to fit the confidence needed to match identifiers. To test SDD, use a dev environment, create copies of your tables, or and see the tags that would be applied to your data by SDD.
In the data inventory dashboard, you will see tiles for scanned coverage and the percentage of data scanned within a specific time frame. These tiles reference data scanned by an identification framework with SDD. To increase the number of your data sources that have been scanned, .
The next section of the dashboard shows tiles for the compliance frameworks. Within each graph is the separation of columns containing or not containing the data important to the compliance framework. These graphs update every time classification runs, which will happen from .
For information on the frameworks visualized in the dashboard, see the .
Deleting the built-in Discovered tags is not recommended: If you do delete built-in Discovered tags and use the Default Framework, then when the identifier is matched the column will not be tagged. As an alternative, tags can be disabled on a , or SDD can be turned off on a data-source-by-data-source basis when creating a data source.
For Snowflake, the size of the dictionary is limited by the .
For Databricks, Immuta will start up a Databricks cluster to complete the SDD job if one is not already running. This can cause unnecessary costs if the cluster becomes idle. Follow to automatically terminate inactive clusters after a set period of time.
SDD will only work on Starburst (Trino) data sources authenticated with username and password. is not supported with SDD.
is not supported with SDD.
The AWS access key used to register the data source can do a minimum of the following :
If using a custom URL, then the data source registered with the AWS access key must have the region and clusterid included in the formatted like the following:
See the page for more information.