How do you find PII data in an SQL server?

 To find Personally Identifiable Information (PII) in an SQL Server, you'll need to perform a series of steps that involve both automated tools and manual queries. Identifying PII is not a one-size-fits-all task because it depends on the context of the database and the specific data you have. PII can include sensitive information such as names, social security numbers, email addresses, phone numbers, addresses, and more.

Here are a few methods to help you find PII in SQL Server:

1. Manual Data Inspection:

If you already have an idea of what tables or columns might contain PII, you can manually inspect them. Here are some steps you can take:

• Examine Column Names and Data Types: PII-related columns often include personal identifiers, such as name, address, email, phone, ssn, etc. You can search for these by querying the INFORMATION_SCHEMA.COLUMNS or sys.columns system views.

SELECT table_name, column_name, data_type
FROM information_schema.columns
WHERE column_name LIKE '%name%' 
   OR column_name LIKE '%email%' 
   OR column_name LIKE '%phone%'
   OR column_name LIKE '%ssn%'
   OR column_name LIKE '%address%';

• Look for known patterns in data: For certain PII types like phone numbers, social security numbers (SSNs), and credit card numbers, there are standard formats that you can search for using LIKE or PATINDEX.

-- Example for finding Social Security Numbers (SSN)
SELECT *
FROM your_table
WHERE column_name LIKE '___-__-____';  -- SSN format

-- Example for finding phone numbers
SELECT *
FROM your_table
WHERE column_name LIKE '(%) %-%'  -- Typical phone number format

2. Using Regular Expressions (Pattern Matching):

SQL Server supports pattern matching using PATINDEX and LIKE functions, but it doesn't have full support for regular expressions like other databases. However, you can still use basic patterns to find certain types of PII data.

• Social Security Number (SSN) pattern:

  SELECT *
  FROM your_table
  WHERE PATINDEX('%[0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9][0-9][0-9]%', column_name) > 0;
  

• Email address pattern:

  SELECT *
  FROM your_table
  WHERE column_name LIKE '%@%.%';
  

• Phone number pattern:

  SELECT *
  FROM your_table
  WHERE column_name LIKE '(___) ___-____';
  

3. Automated Tools for Data Discovery:

Several third-party tools can help automate the identification of PII data within SQL Server databases. These tools typically scan the database schema and data for patterns indicative of PII. Some popular tools include:

• Microsoft Information Protection and Compliance (MIP): A part of Microsoft Purview, MIP allows you to scan data in SQL Server and other sources for sensitive information.
• Varonis Data Security Platform: This tool can scan databases for PII and other sensitive data.
• SQL Compliance Manager: A tool for auditing and discovering sensitive data.
• Data Discovery & Classification (DLP): SQL Server Management Studio (SSMS) also has data classification capabilities, allowing you to classify columns as "PII," "PII - sensitive," or "non-sensitive."

4. Using SQL Server’s Data Classification Features:

SQL Server 2016 and later versions support Data Discovery & Classification for automatically classifying and labeling sensitive data. You can use this feature to classify columns in your tables that contain PII.

• How to classify data:
  1. Open SQL Server Management Studio (SSMS).
  2. Right-click on a database > Tasks > Manage Data Classification.
  3. This will open a wizard where you can classify columns by type (e.g., PII, Credit Card Information, etc.).

Once the data is classified, you can search for classified columns and review them for PII.

5. Using Data Masking for Sensitive Data:

Another step in identifying PII is to apply Dynamic Data Masking or Static Data Masking. While this won't directly help you find PII, it can help protect it once identified. You can configure it to mask sensitive columns.

Example of applying dynamic data masking:

ALTER TABLE your_table
ALTER COLUMN column_name ADD MASKED WITH (FUNCTION = 'default()');

6. Using SQL Server Extended Properties:

Extended properties allow you to add descriptions to your tables and columns. By standardizing column names or adding descriptions, you can make it easier to identify PII columns. Though this method requires prior setup, it can be useful for maintaining a more organized database.

Example:

EXEC sys.sp_addextendedproperty 
  @name = N'PII', 
  @value = N'This column contains personal information', 
  @level0type = N'SCHEMA', @level0name = 'dbo', 
  @level1type = N'TABLE', @level1name = 'Customer', 
  @level2type = N'COLUMN', @level2name = 'Email';

7. Data Mining and Machine Learning:

If you have a very large database and are unsure what qualifies as PII, you could consider building machine learning models to help classify data. This approach would require additional tools like Azure Machine Learning or Python to analyze your data and find patterns that are likely to be PII.

8. Use SQL Server’s sys.dm_exec_query_stats and sys.dm_exec_requests:

You can query these views to find recent queries being executed in your database. By analyzing query patterns, you can potentially identify queries that access PII data.

9. SQL Server Full-Text Indexing:

Full-text indexing can help in identifying text patterns or phrases that may indicate PII data. You can create a full-text index and use CONTAINS or FREETEXT to search for patterns that might represent PII (like "John Doe" or "johndoe@example.com").

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Conclusion

Identifying PII in SQL Server involves a combination of inspecting the database schema, querying the data for known patterns, and utilizing available tools for data classification and discovery. Whether you use manual querying methods, SQL Server's built-in tools, or third-party solutions, it’s important to follow best practices and ensure that any PII discovered is handled securely and in compliance with privacy regulations (e.g., GDPR, CCPA).