Comprehensive Data Masking Solutions for Sybase Security

Ensuring the confidentiality of sensitive information is paramount. Data masking for Sybase is an effective approach to safeguarding sensitive data against unauthorized access while maintaining usability for non-production environments.

This article explores Sybase’s native masking techniques. It gives practical examples using Python. It also highlights the benefits of third-party solutions like DataSunrise for both dynamic and static data masking. 

Why Data Masking Matters

Data masking is essential for organizations handling sensitive information, such as personal identifiable information (PII), financial data, or proprietary business details. By obfuscating real data, it protects against breaches and ensures compliance with data privacy regulations like GDPR and HIPAA.

Key Benefits of Data Masking

• Enhanced Security: Reduces risk of sensitive data exposure.
• Compliance: Supports adherence to legal and regulatory requirements.
• Improved Testing: Allows developers to work with realistic data without compromising security.

Native Data Masking in Sybase

Sybase does not have built-in dynamic masking. However, it has features like views, stored procedures, and roles that allow for strong masking. Below, we explore these options in detail.

Using Views for Masking

Views provide a simple way to mask sensitive data by displaying only the obfuscated version. For example, to mask email addresses in a table:

-- Create the Users table
CREATE TABLE Users (
user_id INT PRIMARY KEY,
email VARCHAR(255) NOT NULL
);
-- Insert sample data for demonstration
INSERT INTO Users (user_id, email) VALUES
(1, 'user1@mail.com'),
(2, 'admin@corp.org');
CREATE VIEW MaskedUsers AS
SELECT
user_id,
LEFT(email, 3) + REPLICATE('*', CHAR_LENGTH(email) - 7) + RIGHT(email, 4) AS masked_email
FROM Users;
-- Query the masked data
SELECT * FROM MaskedUsers;

Example Result:

Stored Procedures for Conditional Masking

Stored procedures enable more complex logic for data masking. For instance, you can restrict access based on user roles:

CREATE PROCEDURE GetMaskedData(@role VARCHAR(50))
AS
BEGIN
IF @role = 'admin'
SELECT * FROM Users;
ELSE
SELECT user_id, '*****' AS sensitive_column FROM Users;
END;
-- Example usage
EXEC GetMaskedData 'developer';

Example Result (for non-admin):

Role-Based Access Control (RBAC)

Sybase’s RBAC capabilities allow you to restrict access to specific columns or tables entirely. Define roles and grant access selectively:

GRANT SELECT ON Users(user_id) TO DeveloperRole;
DENY SELECT ON Users(email) TO DeveloperRole;

This ensures that only authorized users can view sensitive fields.

Python Integration for Data Masking

Python is a powerful tool for implementing data masking in Sybase. Below is an example of connecting to Sybase, creating a masked copy of data, and obfuscating email addresses.

Connecting to Sybase and Masking Data

import pyodbc
# Connect to Sybase
def connect_to_sybase():
conn = pyodbc.connect(
'DRIVER={Adaptive Server Enterprise};SERVER=your_server;PORT=5000;DATABASE=your_db;UID=your_user;PWD=your_password'
)
return conn
# Mask email addresses
def mask_emails():
conn = connect_to_sybase()
cursor = conn.cursor()
# Create a masked copy of the data
cursor.execute("CREATE TABLE MaskedUsers AS SELECT user_id, '****@****.com' AS email FROM Users")
conn.commit()
# Verify the masked data
cursor.execute("SELECT * FROM MaskedUsers")
for row in cursor.fetchall():
print(row)
conn.close()
mask_emails()

Dynamic and Static Masking with DataSunrise

While Sybase’s native tools offer flexibility, third-party solutions like DataSunrise provide advanced capabilities for dynamic data masking and static data masking.

Setting Up Dynamic Masking in DataSunrise

Follow these steps to create a dynamic masking rule:

1. Create an Instance: Use the supported database type.
2. Add a Dynamic Masking Rule:
• Go to Masking > Dynamic Masking Rules.
• Click Add Rule and set the following:
• General Settings: Define the rule name and database type.


• Action Settings: Enable logging and other options.


• Filter Sessions: Optionally define conditions based on user roles or applications.


• Specify masking conditions in the “Hide Rows” tab.


3. Test the Rule: Query the database via the DataSunrise proxy and validate the masked data.

Static Masking in DataSunrise

Static masking creates a copy of your database with obfuscated data for non-production use.

1. Create a New Task:
• Go to Static Masking and click New.
• Define source and target instances, databases, and schemas.

2. Set Masking Methods:
• Select tables and define column-specific masking techniques.

3. Schedule the Task:
• Configure execution frequency.
• Save and start the task.


4. Verify Results:
• Connect to the target database and check masked data.

Benefits of DataSunrise

• Centralized Control: Uniform masking rules across databases.
• Compliance: Meets industry standards for security and privacy.
• Flexibility: Supports dynamic and static masking with granular settings.

Conclusion

Implementing data masking for Sybase is crucial for protecting sensitive information and ensuring compliance. Native features such as views, stored procedures, and Python integration offer good solutions.

However, third-party tools like DataSunrise go further. They provide advanced dynamic and static masking capabilities. DataSunrise offers centralized control and a strong security suite. DataSunrise is a great choice for organizations that want to protect data. For more information, visit our website. You can also request an online demo to explore our advanced tools.