Hi,
I’m looking for guidance on implementing data access control in Power BI.
Specifically, I need to restrict access to certain attributes (columns) based on user roles, ensuring that sensitive data is appropriately masked. These attributes are spread across multiple dimension tables.
This requirement is similar to Row-Level Security (RLS), but instead of controlling access to specific rows (horizontal security), it involves Column-Level Security (CLS) (vertical security).
Do you have any recommendations or best practices for implementing CLS directly within Power BI? Note: The source data is stored in AWS Redshift.
Using EDNA Mentor chat. I copied your posting into the EDNA chat and this what comes up.
Implementing Column-Level Security (CLS) in Power BI is not as straightforward as Row-Level Security (RLS), as Power BI does not provide built-in CLS functionality. However, you can achieve a form of column-level security through some techniques. Here are recommendations and best practices for implementing CLS in Power BI:
1. Using Role-Based Measures
Create measures that return values based on the user’s role. For instance, you can create measures that return a blank or masked value when the user doesn’t have access to certain columns.
SensitiveMeasure =
IF(
USERNAME() IN {"role1@example.com", "role2@example.com"},
SUM(SensitiveData[Value]),
BLANK() // Or use "N/A" for masking
)
2. Separate Reports for Different User Groups
Design separate report files or dashboards for different user roles, each tailored to the data access levels required. This provides a clear separation of sensitive information.
3. Hide Columns in the Model
Hide sensitive columns from the report view in Power BI Desktop. This can be done by right-clicking the column in the data model and selecting Hide in Report View. However, this does not prevent users with access to the model from seeing these columns in the data view.
4. Using Power BI Embedded or API for Additional Security
If using Power BI Embedded, consider using the API to customize user access and control data visibility. This can include programming controls to restrict access to certain columns based on user roles.
5. Publish to Web with caution
Avoid using the “Publish to Web” feature for sensitive data. Instead, use internal sharing and governance to control access.
6. Implementing Dataflows
Use Power Query in dataflows to filter out sensitive columns before the data is loaded into Power BI. This ensures that sensitive data doesn’t even reach the report.
7. Power BI Service Security Settings
Leverage the security settings in the Power BI service. Set up workspace permissions and access controls to allow only authorized users to access reports containing sensitive data.
8. Data Masking Techniques
Use data masking techniques through DAX to obfuscate data. For example, show only partial values of sensitive fields:
MaskedColumn =
IF(
USERNAME() IN {"role1@example.com"},
SensitiveData[OriginalColumn],
"****"
)
Best Practices:
Document Access Controls: Clearly document who has access to what data.
Test Thoroughly: Make sure to test your implementation among different user roles.
Educate Users: Make sure end-users know how to access and interpret the data based on their respective roles.
Regularly Review Permissions: Regular audits of user access to ensure compliance with data governance policies.
By utilizing these methods, you can create a more controlled and secure environment in Power BI to manage access to sensitive attributes across your reports.