Real-Time Data Protection with Dynamic Data Masking in Sybase

Dynamic data masking (DDM) is a cutting-edge technique for real-time obfuscation ofsensitive data. As organizations manage more private information, tools for dynamic data masking in Sybase are essential. They help keep data secure while allowing normal workflows to continue. This article explores the concept of dynamic data masking, its implementation in Sybase environments, and practical applications.

What Is Dynamic Data Masking?

Dynamic data masking hides sensitive data in real time by altering its presentation to unauthorized users. Unlike static masking, which creates a separate dataset, DDM applies transformations dynamically at query runtime, ensuring that the original data remains intact in the database.

Key Features of Dynamic Data Masking

1. Real-Time Obfuscation: Masks data as it is queried, without modifying the underlying data.
2. Role-Based Access: Grants varying levels of access based on user roles.
3. Non-Disruptive: Requires minimal changes to application code or database architecture.

Dynamic Data Masking in Sybase

Sybase does not have built-in DDM support like some newer databases. However, it can get similar results. This can be done using views, stored procedures, and reverse proxy tools like DataSunrise. Let’s explore these approaches in detail.

Implementing Dynamic Data Masking with Views

Views are a straightforward way to mask data dynamically based on user roles. Below is an example demonstrating email masking:

CREATE VIEW MaskedUsers1 AS
SELECT
   user_id,
   LEFT(email, 3) + REPLICATE('*', CHAR_LENGTH(email) - 7) + RIGHT(email, 4) AS masked_email
FROM Users2
WHERE CHAR_LENGTH(email) >= 7;


SELECT * FROM MaskedUsers1

Example Output:

In this approach, the SYSTEM_USER function determines the querying user’s identity and dynamically adjusts the returned data.

Stored Procedures for Advanced Masking Logic

For more granular control, stored procedures allow for conditional masking logic based on specific user attributes:

CREATE PROCEDURE GetMaskedEmails (@username VARCHAR(50))
AS
BEGIN
   IF @username = 'admin'
   BEGIN
       -- If the user is 'admin', show the full email address
       SELECT user_id, email FROM Users1
   END
   ELSE
   BEGIN
       -- For all other users, mask the email except the first 3 characters and the last 4 characters
       SELECT user_id,
              LEFT(email, 3) + REPLICATE('*', CHAR_LENGTH(email) - 7) + RIGHT(email, 4) AS masked_email
       FROM Users2
   END
END

This technique offers flexibility, such as incorporating session attributes or custom logic, making it ideal for complex masking requirements.

Dynamic Masking with DataSunrise

DataSunrise is a powerful third-party tool that simplifies dynamic data masking for Sybase. By acting as a proxy between users and the database, DataSunrise ensures that only masked data is visible to unauthorized users.

Setting Up Dynamic Masking in DataSunrise

1. Create a Instance:
   
   
• Configure DataSunrise to connect to your Sybase database.
• Define proxy settings for redirecting user queries through the DataSunrise engine.

2. Define Dynamic Masking Rules:
   
   
• Navigate to the Masking > Dynamic Masking Rules tab and click Add Rule.
• Specify the target database, table, and columns.

3. Test Masking Rules:
   
   
• Use a client application to query masked tables via the DataSunrise proxy.
• Validate that unauthorized users see obfuscated data, while authorized users access unmasked data.

4. Fine-Tune Filters:
   
   
• Apply session-based filters to restrict masking based on criteria such as user roles, client IPs, or applications.

Advantages of Proxy-Based Masking

• Transparency: No need to alter database schema or application code.
• Granularity: Define column-level masking rules with fine-tuned filters.
• Scalability: Supports high-query environments with minimal performance impact.

Use Cases for Dynamic Data Masking in Sybase

Dynamic masking is ideal for scenarios requiring secure real-time data sharing:

1. Application Development:
   Developers access realistic yet obfuscated data, ensuring security in non-production environments.
2. Regulatory Compliance:
   Organizations meet privacy requirements by masking PII and sensitive information for auditors or external vendors.
3. Data Analytics:
   Analysts work with masked data, maintaining confidentiality while deriving insights.
4. Multi-Tenant Environments:
   Mask data dynamically to isolate tenant-specific information in shared databases.

Challenges and Considerations

While dynamic data masking is effective, it comes with certain challenges:

• Performance Overhead: Masking logic, especially complex patterns, can increase query execution time.
• Role Management: Requires robust role definitions and access policies.
• Audit Requirements: Ensure proper logging of masked data access for compliance.

Conclusion

Dynamic data masking is a vital tool for securing sensitive information in Sybase environments. Organizations can use views, stored procedures, and tools like DataSunrise. This helps them create strong masking solutions that fit their needs. By adopting dynamic masking, businesses not only enhance data security but also streamline compliance and operational efficiency.

To explore how DataSunrise can revolutionize your Sybase security setup with advanced dynamic masking capabilities, visit our website and schedule an online demo.