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Row-level security

  • Artikkeli

Applies to: SQL Server Azure SQL Database Azure SQL Managed Instance Azure Synapse Analytics SQL analytics endpoint in Microsoft Fabric Warehouse in Microsoft Fabric SQL database in Microsoft Fabric

Decorative graphic of row level security.

Row-level security (RLS) enables you to use group membership or execution context to control access to rows in a database table.

Row-level security simplifies the design and coding of security in your application. RLS helps you implement restrictions on data row access. For example, you can ensure that workers access only those data rows that are pertinent to their department. Another example is to restrict customers' data access to only the data relevant to their company.

The access restriction logic is located in the database tier rather than away from the data in another application tier. The database system applies the access restrictions every time that data access is attempted from any tier. This makes your security system more reliable and robust by reducing the surface area of your security system.

Implement RLS by using the CREATE SECURITY POLICY Transact-SQL statement, and predicates created as inline table-valued functions.

Row-level security was first introduced to SQL Server 2016 (13.x).

Note

This article is focused on SQL Server and Azure SQL platforms. For Microsoft Fabric, see Row-level security in Microsoft Fabric.

Description

Row-level security (RLS) supports two types of security predicates:

  • Filter predicates silently filter the rows available to read operations (SELECT, UPDATE, and DELETE).

  • Block predicates explicitly block write operations (AFTER INSERT, AFTER UPDATE, BEFORE UPDATE, BEFORE DELETE) that violate the predicate.

Access to row-level data in a table is restricted by a security predicate defined as an inline table-valued function. The function is then invoked and enforced by a security policy. For filter predicates, the application is unaware of rows that are filtered from the result set. If all rows are filtered, then a null set is returned. For block predicates, any operations that violate the predicate will fail with an error.

Filter predicates are applied while reading data from the base table. They affect all get operations: SELECT, DELETE, and UPDATE. The users can't select or delete rows that are filtered. The user can't update rows that are filtered. But, it's possible to update rows in such a way that they'll be filtered afterward. Block predicates affect all write operations.

  • AFTER INSERT and AFTER UPDATE predicates can prevent users from updating rows to values that violate the predicate.

  • BEFORE UPDATE predicates can prevent users from updating rows that currently violate the predicate.

  • BEFORE DELETE predicates can block delete operations.

Both filter and block predicates and security policies have the following behavior:

  • You might define a predicate function that joins with another table and/or invokes a function. If the security policy is created with SCHEMABINDING = ON (the default), then the join or function is accessible from the query and works as expected without any extra permission checks. If the security policy is created with SCHEMABINDING = OFF, then users will need SELECT permissions on these additional tables and functions to query the target table. If the predicate function invokes a CLR scalar-valued function, the EXECUTE permission is needed in addition.

  • You might issue a query against a table that has a security predicate defined but disabled. Any rows that are filtered or blocked aren't affected.

  • If a dbo user, a member of the db_owner role, or the table owner queries a table that has a security policy defined and enabled, the rows are filtered or blocked as defined by the security policy.

  • Attempts to alter the schema of a table bound by a schema bound security policy results in an error. However, columns not referenced by the predicate can be altered.

  • Attempts to add a predicate on a table that already has one defined for the specified operation results in an error. This happens whether the predicate is enabled or not.

  • Attempts to modify a function that is used as a predicate on a table within a schema bound security policy results in an error.

  • Defining multiple active security policies that contain nonoverlapping predicates, succeeds.

Filter predicates have the following behavior:

  • Define a security policy that filters the rows of a table. The application is unaware of any rows that are filtered for SELECT, UPDATE, and DELETE operations. Including situations where all the rows are filtered out. The application can INSERT rows, even if they'll be filtered during any other operation.

Block predicates have the following behavior:

  • Block predicates for UPDATE are split into separate operations for BEFORE and AFTER. You can't, for example, block users from updating a row to have a value higher than the current one. If this kind of logic is required, you must use triggers with the DELETED and INSERTED intermediate tables to reference the old and new values together.

  • The optimizer won't check an AFTER UPDATE block predicate if the columns used by the predicate function weren't changed. For example: Alice shouldn't be able to change a salary to be greater than 100,000. Alice can change the address of an employee whose salary is already greater than 100,000 as long as the columns referenced in the predicate weren't changed.

  • No changes have been made to the bulk APIs, including BULK INSERT. This means that block predicates AFTER INSERT applies to bulk insert operations just as they would regular insert operations.

Use cases

Here are design examples of how row-level security (RLS) can be used:

  • A hospital can create a security policy that allows nurses to view data rows for their patients only.

  • A bank can create a policy to restrict access to financial data rows based on an employee's business division or role in the company.

  • A multitenant application can create a policy to enforce a logical separation of each tenant's data rows from every other tenant's rows. Efficiencies are achieved by the storage of data for many tenants in a single table. Each tenant can see only its data rows.

RLS filter predicates are functionally equivalent to appending a WHERE clause. The predicate can be as sophisticated as business practices dictate, or the clause can be as simple as WHERE TenantId = 42.

In more formal terms, RLS introduces predicate based access control. It features a flexible, centralized, predicate-based evaluation. The predicate can be based on metadata or any other criteria the administrator determines as appropriate. The predicate is used as a criterion to determine if the user has the appropriate access to the data based on user attributes. Label-based access control can be implemented by using predicate-based access control.

Permissions

Creating, altering, or dropping security policies requires the ALTER ANY SECURITY POLICY permission. Creating or dropping a security policy requires ALTER permission on the schema.

Additionally the following permissions are required for each predicate that is added:

  • SELECT and REFERENCES permissions on the function being used as a predicate.

  • REFERENCES permission on the target table being bound to the policy.

  • REFERENCES permission on every column from the target table used as arguments.

Security policies apply to all users, including dbo users in the database. Dbo users can alter or drop security policies however their changes to security policies can be audited. If high privileged users, such as sysadmin or db_owner, need to see all rows to troubleshoot or validate data, the security policy must be written to allow that.

If a security policy is created with SCHEMABINDING = OFF, then to query the target table, users must have the SELECT or EXECUTE permission on the predicate function and any additional tables, views, or functions used within the predicate function. If a security policy is created with SCHEMABINDING = ON (the default), then these permission checks are bypassed when users query the target table.

Best practices

  • It's highly recommended to create a separate schema for the RLS objects: predicate functions, and security policies. This helps to separate the permissions that are required on these special objects from the target tables. Additional separation for different policies and predicate functions might be needed in multitenant databases, but not as a standard for every case.

  • The ALTER ANY SECURITY POLICY permission is intended for highly privileged users (such as a security policy manager). The security policy manager doesn't require SELECT permission on the tables they protect.

  • Avoid type conversions in predicate functions to avoid potential runtime errors.

  • Avoid recursion in predicate functions wherever possible to avoid performance degradation. The query optimizer will try to detect direct recursions, but isn't guaranteed to find indirect recursions. An indirect recursion is where a second function calls the predicate function.

  • Avoid using excessive table joins in predicate functions to maximize performance.

Avoid predicate logic that depends on session-specific SET options: While unlikely to be used in practical applications, predicate functions whose logic depends on certain session-specific SET options can leak information if users are able to execute arbitrary queries. For example, a predicate function that implicitly converts a string to datetime could filter different rows based on the SET DATEFORMAT option for the current session. In general, predicate functions should abide by the following rules:

Security note: side-channel attacks

Malicious security policy manager

It's important to observe that a malicious security policy manager, with sufficient permissions to create a security policy on top of a sensitive column and having permission to create or alter inline table-valued functions, can collude with another user who has select permissions on a table to perform data exfiltration by maliciously creating inline table-valued functions designed to use side channel attacks to infer data. Such attacks would require collusion (or excessive permissions granted to a malicious user) and would likely require several iterations of modifying the policy (requiring permission to remove the predicate in order to break the schema binding), modifying the inline table-valued functions, and repeatedly running select statements on the target table. We recommend you limit permissions as necessary and monitor for any suspicious activity. Activity such as constantly changing policies and inline table-valued functions related to row-level security should be monitored.

Carefully crafted queries

It's possible to cause information leakage by using carefully crafted queries that use errors to exfiltrate data. For example, SELECT 1/(SALARY-100000) FROM PAYROLL WHERE NAME='John Doe'; would let a malicious user know that John Doe's salary is exactly $100,000. Even though there's a security predicate in place to prevent a malicious user from directly querying other people's salary, the user can determine when the query returns a divide-by-zero exception.

Cross-feature compatibility

In general, row-level security will work as expected across features. However, there are a few exceptions. This section documents several notes and caveats for using row-level security with certain other features of SQL Server.

  • DBCC SHOW_STATISTICS reports statistics on unfiltered data, and can leak information otherwise protected by a security policy. For this reason, access to view a statistics object for a table with a row-level security policy is restricted. The user must own the table or the user must be a member of the sysadmin fixed server role, the db_owner fixed database role, or the db_ddladmin fixed database role.

  • Filestream: RLS is incompatible with Filestream.

  • PolyBase: RLS is supported with external tables in Azure Synapse and SQL Server 2019 CU7 or higher versions.

  • Memory-Optimized Tables: The inline table-valued function used as a security predicate on a memory-optimized table must be defined using the WITH NATIVE_COMPILATION option. With this option, language features not supported by memory-optimized tables will be banned and the appropriate error will be issued at creation time. For more information, see Row-level security in Memory-Optimized Tables.

  • Indexed views: In general, security policies can be created on top of views, and views can be created on top of tables that are bound by security policies. However, indexed views can't be created on top of tables that have a security policy, because row lookups via the index would bypass the policy.

  • Change Data Capture: Change Data Capture (CDC) can leak entire rows that should be filtered to members of db_owner or users who are members of the "gating" role specified when CDC is enabled for a table. You can explicitly set this function to NULL to enable all users to access the change data. In effect, db_owner and members of this gating role can see all data changes on a table, even if there's a security policy on the table.

  • Change Tracking: Change Tracking can leak the primary key of rows that should be filtered to users with both SELECT and VIEW CHANGE TRACKING permissions. Actual data values aren't leaked; only the fact that column A was updated/inserted/deleted for the row with a certain primary key. This is problematic if the primary key contains a confidential element, such as a Social Security Number. However, in practice, this CHANGETABLE is almost always joined with the original table in order to get the latest data.

  • Full-Text Search: A performance hit is expected for queries using the following Full-Text Search and Semantic Search functions, because of an extra join introduced to apply row-level security and avoid leaking the primary keys of rows that should be filtered: CONTAINSTABLE, FREETEXTTABLE, semantickeyphrasetable, semanticsimilaritydetailstable, semanticsimilaritytable.

  • Columnstore Indexes: RLS is compatible with both clustered and nonclustered columnstore indexes. However, because row-level security applies a function, it's possible that the optimizer might modify the query plan so that it doesn't use batch mode.

  • Partitioned Views: Block predicates can't be defined on partitioned views, and partitioned views can't be created on top of tables that use block predicates. Filter predicates are compatible with partitioned views.

  • Temporal tables: Temporal tables are compatible with RLS. However, security predicates on the current table aren't automatically replicated to the history table. To apply a security policy to both the current and the history tables, you must individually add a security predicate on each table.

Other limitations:

  • Microsoft Fabric and Azure Synapse Analytics support filter predicates only. Block predicates aren't currently supported on Microsoft Fabric and Azure Synapse Analytics.

Examples

A. Scenario for users who authenticate to the database

This example creates three users and creates and populates a table with six rows. It then creates an inline table-valued function and a security policy for the table. The example then shows how select statements are filtered for the various users.

Create three user accounts that demonstrate different access capabilities.

CREATE USER Manager WITHOUT LOGIN;
CREATE USER SalesRep1 WITHOUT LOGIN;
CREATE USER SalesRep2 WITHOUT LOGIN;
GO

Create a table to hold data.

CREATE SCHEMA Sales
GO
CREATE TABLE Sales.Orders
    (
    OrderID int,
    SalesRep nvarchar(50),
    Product nvarchar(50),
    Quantity smallint
    );

Populate the table with six rows of data, showing three orders for each sales representative.

INSERT INTO Sales.Orders  VALUES (1, 'SalesRep1', 'Valve', 5);
INSERT INTO Sales.Orders  VALUES (2, 'SalesRep1', 'Wheel', 2);
INSERT INTO Sales.Orders  VALUES (3, 'SalesRep1', 'Valve', 4);
INSERT INTO Sales.Orders  VALUES (4, 'SalesRep2', 'Bracket', 2);
INSERT INTO Sales.Orders  VALUES (5, 'SalesRep2', 'Wheel', 5);
INSERT INTO Sales.Orders  VALUES (6, 'SalesRep2', 'Seat', 5);
-- View the 6 rows in the table
SELECT * FROM Sales.Orders;

Grant read access on the table to each of the users.

GRANT SELECT ON Sales.Orders TO Manager;
GRANT SELECT ON Sales.Orders TO SalesRep1;
GRANT SELECT ON Sales.Orders TO SalesRep2;
GO

Create a new schema, and an inline table-valued function. The function returns 1 when a row in the SalesRep column is the same as the user executing the query (@SalesRep = USER_NAME()) or if the user executing the query is the Manager user (USER_NAME() = 'Manager'). This example of a user-defined, table-valued function is useful to serve as a filter for the security policy created in the next step.

CREATE SCHEMA Security;
GO

CREATE FUNCTION Security.tvf_securitypredicate(@SalesRep AS nvarchar(50))
    RETURNS TABLE
WITH SCHEMABINDING
AS
    RETURN SELECT 1 AS tvf_securitypredicate_result
WHERE @SalesRep = USER_NAME() OR USER_NAME() = 'Manager';
GO

Create a security policy adding the function as a filter predicate. The STATE must be set to ON to enable the policy.

CREATE SECURITY POLICY SalesFilter
ADD FILTER PREDICATE Security.tvf_securitypredicate(SalesRep)
ON Sales.Orders
WITH (STATE = ON);
GO

Allow SELECT permissions to the tvf_securitypredicate function:

GRANT SELECT ON Security.tvf_securitypredicate TO Manager;
GRANT SELECT ON Security.tvf_securitypredicate TO SalesRep1;
GRANT SELECT ON Security.tvf_securitypredicate TO SalesRep2;

Now test the filtering predicate, by selected from the Sales.Orders table as each user.

EXECUTE AS USER = 'SalesRep1';
SELECT * FROM Sales.Orders;
REVERT;

EXECUTE AS USER = 'SalesRep2';
SELECT * FROM Sales.Orders;
REVERT;

EXECUTE AS USER = 'Manager';
SELECT * FROM Sales.Orders;
REVERT;

The manager should see all six rows. The Sales1 and Sales2 users should only see their own sales.

Alter the security policy to disable the policy.

ALTER SECURITY POLICY SalesFilter
WITH (STATE = OFF);

Now Sales1 and Sales2 users can see all six rows.

Connect to the SQL database to clean up resources from this sample exercise:

DROP USER SalesRep1;
DROP USER SalesRep2;
DROP USER Manager;

DROP SECURITY POLICY SalesFilter;
DROP TABLE Sales.Orders;
DROP FUNCTION Security.tvf_securitypredicate;
DROP SCHEMA Security;
DROP SCHEMA Sales;

B. Scenarios for using Row Level Security on an Azure Synapse external table

This short example creates three users and an external table with six rows. It then creates an inline table-valued function and a security policy for the external table. The example shows how select statements are filtered for the various users.

Prerequisites

  1. You must have a dedicated SQL pool. See Create a dedicated SQL pool
  2. The server hosting your dedicated SQL pool must be registered with Microsoft Entra ID (formerly Azure Active Directory) and you must have an Azure storage account with Storage Blog Data Contributor permissions. Follow the steps to Use virtual network service endpoints and rules for servers in Azure SQL Database.
  3. Create a file system for your Azure Storage account. Use Azure Storage Explorer to view your storage account. Right-click on containers and select Create file system.

Once you have the prerequisites in place, create three user accounts that demonstrate different access capabilities.

--run in master
CREATE LOGIN Manager WITH PASSWORD = '<user_password>'
GO
CREATE LOGIN Sales1 WITH PASSWORD = '<user_password>'
GO
CREATE LOGIN Sales2 WITH PASSWORD = '<user_password>'
GO

--run in both the master database and in your dedicated SQL pool database
CREATE USER Manager FOR LOGIN Manager;
CREATE USER Sales1  FOR LOGIN Sales1;
CREATE USER Sales2  FOR LOGIN Sales2 ;

Create a table to hold data.

CREATE TABLE Sales
    (
    OrderID int,
    SalesRep sysname,
    Product varchar(10),
    Qty int
    );

Populate the table with six rows of data, showing three orders for each sales representative.

INSERT INTO Sales VALUES (1, 'Sales1', 'Valve', 5);
INSERT INTO Sales VALUES (2, 'Sales1', 'Wheel', 2);
INSERT INTO Sales VALUES (3, 'Sales1', 'Valve', 4);
INSERT INTO Sales VALUES (4, 'Sales2', 'Bracket', 2);
INSERT INTO Sales VALUES (5, 'Sales2', 'Wheel', 5);
INSERT INTO Sales VALUES (6, 'Sales2', 'Seat', 5);
-- View the 6 rows in the table
SELECT * FROM Sales;

Create an Azure Synapse external table from the Sales table you created.

CREATE MASTER KEY ENCRYPTION BY PASSWORD = '<user_password>';

CREATE DATABASE SCOPED CREDENTIAL msi_cred WITH IDENTITY = 'Managed Service Identity';

CREATE EXTERNAL DATA SOURCE ext_datasource_with_abfss WITH (TYPE = hadoop, LOCATION = 'abfss://<file_system_name@storage_account>.dfs.core.windows.net', CREDENTIAL = msi_cred);

CREATE EXTERNAL FILE FORMAT MSIFormat  WITH (FORMAT_TYPE=DELIMITEDTEXT);

CREATE EXTERNAL TABLE Sales_ext WITH (LOCATION='<your_table_name>', DATA_SOURCE=ext_datasource_with_abfss, FILE_FORMAT=MSIFormat, REJECT_TYPE=Percentage, REJECT_SAMPLE_VALUE=100, REJECT_VALUE=100)
AS SELECT * FROM sales;

Grant SELECT for the three users on the external table Sales_ext that you created.

GRANT SELECT ON Sales_ext TO Sales1;
GRANT SELECT ON Sales_ext TO Sales2;
GRANT SELECT ON Sales_ext TO Manager;

Create a new schema, and an inline table-valued function, you might have completed this in example A. The function returns 1 when a row in the SalesRep column is the same as the user executing the query (@SalesRep = USER_NAME()) or if the user executing the query is the Manager user (USER_NAME() = 'Manager').

CREATE SCHEMA Security;
GO

CREATE FUNCTION Security.fn_securitypredicate(@SalesRep AS sysname)
    RETURNS TABLE
WITH SCHEMABINDING
AS
    RETURN SELECT 1 AS fn_securitypredicate_result
WHERE @SalesRep = USER_NAME() OR USER_NAME() = 'Manager';

Create a security policy on your external table using the inline table-valued function as a filter predicate. The STATE must be set to ON to enable the policy.

CREATE SECURITY POLICY SalesFilter_ext
ADD FILTER PREDICATE Security.fn_securitypredicate(SalesRep)
ON dbo.Sales_ext
WITH (STATE = ON);

Now test the filtering predicate, by selecting from the Sales_ext external table. Sign in as each user, Sales1, Sales2, and Manager. Run the following command as each user.

SELECT * FROM Sales_ext;

The Manager should see all six rows. The Sales1 and Sales2 users should only see their sales.

Alter the security policy to disable the policy.

ALTER SECURITY POLICY SalesFilter_ext
WITH (STATE = OFF);

Now the Sales1 and Sales2 users can see all six rows.

Connect to the Azure Synapse database to clean up resources from this sample exercise:

DROP USER Sales1;
DROP USER Sales2;
DROP USER Manager;

DROP SECURITY POLICY SalesFilter_ext;
DROP TABLE Sales;
DROP EXTERNAL TABLE Sales_ext;
DROP EXTERNAL DATA SOURCE ext_datasource_with_abfss ;
DROP EXTERNAL FILE FORMAT MSIFormat;
DROP DATABASE SCOPED CREDENTIAL msi_cred;
DROP MASTER KEY;

Connect to logical server's master database to clean up resources:

DROP LOGIN Sales1;
DROP LOGIN Sales2;
DROP LOGIN Manager;

C. Scenario for users who connect to the database through a middle-tier application

Note

In this example block predicates functionality isn't currently supported for Microsoft Fabric and Azure Synapse, hence inserting rows for the wrong user ID isn't blocked.

This example shows how a middle-tier application can implement connection filtering, where application users (or tenants) share the same SQL Server user (the application). The application sets the current application user ID in SESSION_CONTEXT after connecting to the database, and then security policies transparently filter rows that shouldn't be visible to this ID, and also block the user from inserting rows for the wrong user ID. No other app changes are necessary.

Create a table to hold data.

CREATE TABLE Sales (
    OrderId int,
    AppUserId int,
    Product varchar(10),
    Qty int
);

Populate the table with six rows of data, showing three orders for each application user.

INSERT Sales VALUES
    (1, 1, 'Valve', 5),
    (2, 1, 'Wheel', 2),
    (3, 1, 'Valve', 4),
    (4, 2, 'Bracket', 2),
    (5, 2, 'Wheel', 5),
    (6, 2, 'Seat', 5);

Create a low-privileged user that the application will use to connect.

-- Without login only for demo
CREATE USER AppUser WITHOUT LOGIN;
GRANT SELECT, INSERT, UPDATE, DELETE ON Sales TO AppUser;

-- Never allow updates on this column
DENY UPDATE ON Sales(AppUserId) TO AppUser;

Create a new schema and predicate function, which will use the application user ID stored in SESSION_CONTEXT() to filter rows.

CREATE SCHEMA Security;
GO

CREATE FUNCTION Security.fn_securitypredicate(@AppUserId int)
    RETURNS TABLE
    WITH SCHEMABINDING
AS
    RETURN SELECT 1 AS fn_securitypredicate_result
    WHERE
        DATABASE_PRINCIPAL_ID() = DATABASE_PRINCIPAL_ID('AppUser')
        AND CAST(SESSION_CONTEXT(N'UserId') AS int) = @AppUserId;
GO

Create a security policy that adds this function as a filter predicate and a block predicate on Sales. The block predicate only needs AFTER INSERT, because BEFORE UPDATE and BEFORE DELETE are already filtered, and AFTER UPDATE is unnecessary because the AppUserId column can't be updated to other values, due to the column permission set earlier.

CREATE SECURITY POLICY Security.SalesFilter
    ADD FILTER PREDICATE Security.fn_securitypredicate(AppUserId)
        ON dbo.Sales,
    ADD BLOCK PREDICATE Security.fn_securitypredicate(AppUserId)
        ON dbo.Sales AFTER INSERT
    WITH (STATE = ON);

Now we can simulate the connection filtering by selecting from the Sales table after setting different user IDs in SESSION_CONTEXT(). In practice, the application is responsible for setting the current user ID in SESSION_CONTEXT() after opening a connection. Setting the @read_only parameter to 1 prevents the value from changing again until the connection is closed (returned to the connection pool).

EXECUTE AS USER = 'AppUser';
EXEC sp_set_session_context @key=N'UserId', @value=1;
SELECT * FROM Sales;
GO

/* Note: @read_only prevents the value from changing again until the connection is closed (returned to the connection pool)*/
EXEC sp_set_session_context @key=N'UserId', @value=2, @read_only=1;

SELECT * FROM Sales;
GO

INSERT INTO Sales VALUES (7, 1, 'Seat', 12); -- error: blocked from inserting row for the wrong user ID
GO

REVERT;
GO

Clean up database resources.

DROP USER AppUser;

DROP SECURITY POLICY Security.SalesFilter;
DROP TABLE Sales;
DROP FUNCTION Security.fn_securitypredicate;
DROP SCHEMA Security;

D. Scenario for using a lookup table for the security predicate

This example uses a lookup table for the link between the user identifier and the value being filtered, rather than having to specify the user identifier in the fact table. It creates three users and creates and populates a fact table, Sample.Sales, with six rows and a lookup table with two rows. It then creates an inline table-valued function that joins the fact table to the lookup to get the user identifier, and a security policy for the table. The example then shows how select statements are filtered for the various users.

Create three user accounts that demonstrate different access capabilities.

CREATE USER Manager WITHOUT LOGIN;
CREATE USER Sales1 WITHOUT LOGIN;
CREATE USER Sales2 WITHOUT LOGIN;

Create a Sample schema and a fact table, Sample.Sales, to hold data.

CREATE SCHEMA Sample;
GO
CREATE TABLE Sample.Sales
    (
    OrderID int,
    Product varchar(10),
    Qty int
    );

Populate Sample.Sales with six rows of data.

INSERT INTO Sample.Sales VALUES (1, 'Valve', 5);
INSERT INTO Sample.Sales VALUES (2, 'Wheel', 2);
INSERT INTO Sample.Sales VALUES (3, 'Valve', 4);
INSERT INTO Sample.Sales VALUES (4, 'Bracket', 2);
INSERT INTO Sample.Sales VALUES (5, 'Wheel', 5);
INSERT INTO Sample.Sales VALUES (6, 'Seat', 5);
-- View the 6 rows in the table
SELECT * FROM Sample.Sales;

Create a table to hold the lookup data – in this case a relationship between Salesrep and Product.

CREATE TABLE Sample.Lk_Salesman_Product
  ( Salesrep sysname,
    Product varchar(10)
  ) ;

Populate the lookup table with sample data, linking one Product to each sales representative.

INSERT INTO Sample.Lk_Salesman_Product VALUES ('Sales1', 'Valve');
INSERT INTO Sample.Lk_Salesman_Product VALUES ('Sales2', 'Wheel');
-- View the 2 rows in the table
SELECT * FROM Sample.Lk_Salesman_Product;

Grant read access on the fact table to each of the users.

GRANT SELECT ON Sample.Sales TO Manager;
GRANT SELECT ON Sample.Sales TO Sales1;
GRANT SELECT ON Sample.Sales TO Sales2;

Create a new schema and an inline table-valued function. The function returns 1 when a user queries the fact table Sample.Sales and the SalesRep column of the table Lk_Salesman_Product is the same as the user executing the query (@SalesRep = USER_NAME()) when joined to the fact table on the Product column, or if the user executing the query is the Manager user (USER_NAME() = 'Manager').

CREATE SCHEMA Security ;
GO
CREATE FUNCTION Security.fn_securitypredicate
         (@Product AS varchar(10))
RETURNS TABLE
WITH SCHEMABINDING
AS
           RETURN ( SELECT 1 as Result
                     FROM Sample.Sales f
            INNER JOIN Sample.Lk_Salesman_Product s
                     ON s.Product = f.Product
            WHERE ( f.product = @Product
                    AND s.SalesRep = USER_NAME() )
                 OR USER_NAME() = 'Manager'
                   ) ;

Create a security policy adding the function as a filter predicate. The STATE must be set to ON to enable the policy.

CREATE SECURITY POLICY SalesFilter
ADD FILTER PREDICATE Security.fn_securitypredicate(Product)
ON Sample.Sales
WITH (STATE = ON) ;

Allow SELECT permissions to the fn_securitypredicate function:

GRANT SELECT ON Security.fn_securitypredicate TO Manager;
GRANT SELECT ON Security.fn_securitypredicate TO Sales1;
GRANT SELECT ON Security.fn_securitypredicate TO Sales2;

Now test the filtering predicate, by selected from the Sample.Sales table as each user.

EXECUTE AS USER = 'Sales1';
SELECT * FROM Sample.Sales;
-- This will return just the rows for Product 'Valve' (as specified for 'Sales1' in the Lk_Salesman_Product table above)
REVERT;

EXECUTE AS USER = 'Sales2';
SELECT * FROM Sample.Sales;
-- This will return just the rows for Product 'Wheel' (as specified for 'Sales2' in the Lk_Salesman_Product table above)
REVERT;

EXECUTE AS USER = 'Manager';
SELECT * FROM Sample.Sales;
-- This will return all rows with no restrictions
REVERT;

The Manager should see all six rows. The Sales1 and Sales2 users should only see their own sales.

Alter the security policy to disable the policy.

ALTER SECURITY POLICY SalesFilter
WITH (STATE = OFF);

Now Sales1 and Sales2 users can see all six rows.

Connect to the SQL database to clean up resources from this sample exercise:

DROP USER Sales1;
DROP USER Sales2;
DROP USER Manager;

DROP SECURITY POLICY SalesFilter;
DROP FUNCTION Security.fn_securitypredicate;
DROP TABLE Sample.Sales;
DROP TABLE Sample.Lk_Salesman_Product;
DROP SCHEMA Security;
DROP SCHEMA Sample;

E. Row-level security scenario in Microsoft Fabric

We can demonstrate row-level security Warehouse and SQL analytics endpoint in Microsoft Fabric.

The following example creates sample tables that will work with Warehouse in Microsoft Fabric, but in SQL analytics endpoint use existing tables. In the SQL analytics endpoint, you can't use CREATE TABLE, but you can use CREATE SCHEMA, CREATE FUNCTION, and CREATE SECURITY POLICY.

In this example, first create a schema sales, a table sales.Orders.

CREATE SCHEMA sales;
GO

-- Create a table to store sales data
CREATE TABLE sales.Orders (
    SaleID INT,
    SalesRep VARCHAR(100),
    ProductName VARCHAR(50),
    SaleAmount DECIMAL(10, 2),
    SaleDate DATE
);

-- Insert sample data
INSERT INTO sales.Orders (SaleID, SalesRep, ProductName, SaleAmount, SaleDate)
VALUES
    (1, 'Sales1@contoso.com', 'Smartphone', 500.00, '2023-08-01'),
    (2, 'Sales2@contoso.com', 'Laptop', 1000.00, '2023-08-02'),
    (3, 'Sales1@contoso.com', 'Headphones', 120.00, '2023-08-03'),
    (4, 'Sales2@contoso.com', 'Tablet', 800.00, '2023-08-04'),
    (5, 'Sales1@contoso.com', 'Smartwatch', 300.00, '2023-08-05'),
    (6, 'Sales2@contoso.com', 'Gaming Console', 400.00, '2023-08-06'),
    (7, 'Sales1@contoso.com', 'TV', 700.00, '2023-08-07'),
    (8, 'Sales2@contoso.com', 'Wireless Earbuds', 150.00, '2023-08-08'),
    (9, 'Sales1@contoso.com', 'Fitness Tracker', 80.00, '2023-08-09'),
    (10, 'Sales2@contoso.com', 'Camera', 600.00, '2023-08-10');

Create a Security schema, a function Security.tvf_securitypredicate, and a security policy SalesFilter.

-- Creating schema for Security
CREATE SCHEMA Security;
GO

-- Creating a function for the SalesRep evaluation
CREATE FUNCTION Security.tvf_securitypredicate(@SalesRep AS nvarchar(50))
    RETURNS TABLE
WITH SCHEMABINDING
AS
    RETURN SELECT 1 AS tvf_securitypredicate_result
WHERE @SalesRep = USER_NAME() OR USER_NAME() = 'manager@contoso.com';
GO

-- Using the function to create a Security Policy
CREATE SECURITY POLICY SalesFilter
ADD FILTER PREDICATE Security.tvf_securitypredicate(SalesRep)
ON sales.Orders
WITH (STATE = ON);
GO

After applying the security policy and creating the function, the users Sales1@contoso.com and Sales2@contoso.com will only be able to see their own data in the sales.Orders table, where the column SalesRep equals their own user name returned by the built-in function USER_NAME. The Fabric user manager@contoso.com is able to see all data in the sales.Orders table.

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