Ensuring Security with Static Data Masking Sybase

Static data masking (SDM) is an important process. It helps create a secure and working copy of a production database. Static masking is different from dynamic masking.

Dynamic masking hides data while a query runs. In contrast, static masking permanently changes sensitive data. This happens in a non-production environment.

Sybase does not have built-in support for static data masking. However, you can use SQL scripts, Python, or third-party tools like DataSunrise to implement it effectively.

Understanding Static Data Masking

Static masking transforms sensitive data into anonymized or obfuscated values within a database copy, ensuring the original production data remains untouched. This makes SDM particularly valuable for creating secure development, testing, and training environments.

Key Benefits of Static Data Masking

1. Permanent Data Anonymization: Unlike dynamic masking, SDM creates a permanent copy of the database with obfuscated data.
2. Regulatory Compliance: Meets data privacy requirements by ensuring that non-production environments do not expose sensitive information.
3. Secure Testing and Development: Enables teams to work with realistic data without compromising security.

Implementing Static Masking in Sybase with SQL

Although Sybase lacks built-in SDM features, SQL can be leveraged to manually mask sensitive data. Below is an example of a simple masking process:

Example: Masking Email Addresses in SQL

-- Step 1: Create a duplicate of the original table
CREATE TABLE MaskedUsers AS
SELECT * FROM Users;

-- Step 2: Apply masking to sensitive columns
UPDATE MaskedUsers
SET email = LEFT(email, 3) + REPLICATE('*', CHAR_LENGTH(email) - 7) + RIGHT(email, 4);

-- Step 3: Verify the masked data
SELECT * FROM MaskedUsers;

This approach requires custom scripts for each sensitive column and must be carefully tailored to the database schema.

Implementing Static Masking in Sybase with Python

Python provides a flexible alternative for automating static masking tasks. Below is a Python script example for masking sensitive columns in a Sybase database:

Example: Masking Data with Python

import pyodbc

# Connect to Sybase
def connect_to_sybase():
    return pyodbc.connect(
        'DRIVER={Adaptive Server Enterprise};SERVER=your_server;PORT=5000;DATABASE=your_db;UID=your_user;PWD=your_password'
    )

# Mask sensitive data
def mask_data():
    conn = connect_to_sybase()
    cursor = conn.cursor()

    # Create a masked copy of the Users table
    cursor.execute("CREATE TABLE MaskedUsers AS SELECT * FROM Users")
    conn.commit()

    # Apply masking logic
    cursor.execute(
        "UPDATE MaskedUsers SET email = LEFT(email, 3) + REPLICATE('*', CHAR_LENGTH(email) - 7) + RIGHT(email, 4)"
    )
    conn.commit()

    # Verify masked data
    cursor.execute("SELECT * FROM MaskedUsers")
    for row in cursor.fetchall():
        print(row)

    conn.close()

mask_data()

This script automates the creation of a masked database copy and ensures consistent application of masking rules.

Static Masking with DataSunrise

DataSunrise offers a comprehensive solution for implementing static data masking in Sybase. Here’s how you can use DataSunrise to perform SDM:

Key Features of DataSunrise Static Masking

1. Automated Masking Tasks: Enables periodic execution of masking tasks to maintain secure database copies.
2. Flexible Masking Methods: Provides a wide range of masking techniques tailored to different data types.
3. Constraint Management: Preserves database integrity by respecting primary keys, foreign keys, and other constraints during masking.

Steps for Creating a Static Masking Task in DataSunrise

1. Access the Static Masking Tab:
   
   
• Navigate to the Static Masking section and click New to start creating a masking task.
2. Configure Task Settings:
   
   
• Set the task name and select the server where it will run.
• Specify the source and target instances by selecting or creating new database connections.

3. Select Tables and Columns:
   
   
• In the Transferred Tables section, choose tables for masking.
• Define masking methods for sensitive columns (e.g., randomization, hashing, or nulling).

4. Set Masking Options:
   
   
• Configure additional settings, such as loader options and proxy usage, in the Static Masking Settings section.

5. Schedule the Task:
   
   
• Define the execution frequency (e.g., every 2-3 minutes) in the Startup Frequency section.

6. Run the Task:
   
   
• Save the configuration and start the task.
• Monitor task progress and results in the execution log.
7. Validate the Masked Database:
   
   
• Connect to the target database and verify that sensitive data has been successfully masked.

Example Masking Scenario

Imagine a database with customer information, including names, emails, and credit card details. Using DataSunrise, you can:

• Randomize names to anonymize identities.
• Hash email addresses to maintain uniqueness while masking details.
• Replace credit card numbers with format-preserving dummy values.

Differences Between Dynamic and Static Masking

Conclusion

Static data masking is a critical technique for organizations using Sybase to ensure secure non-production environments. While native support is lacking, SQL and Python offer powerful tools for manual implementation. For more advanced capabilities, DataSunrise provides a robust solution, enabling automated, constraint-aware masking processes. By leveraging these techniques, organizations can safeguard sensitive data and comply with regulatory standards effectively.

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