PostgreSQL Data Masking: Key Techniques and Best Practices

Data security is a top priority for any organization that handles sensitive information. You must carefully safeguard customer records, financial data, personally identifiable information (PII), and other confidential data from both internal and external threats. Data masking is a strong way to protect data. PostgreSQL has good tools to hide sensitive information and keep it safe.

What is Data Masking?

Data masking is when you hide important data while keeping the original structure and format. The goal is to create a substitute for actual data. Users can utilize this substitute for tasks like software testing, user training, and analytics. It ensures that it does not disclose any sensitive information.

Some common data masking techniques include:

• Substitution – replacing a sensitive data element with a random value of the same type and format
• Shuffling – mixing up sensitive values within a column so they are no longer associated with the original records
• Encryption – transforming data into an encrypted version that requires a key for decryption
• Nulling – replacing sensitive data with null values
• Truncation – partially displaying data, like showing only the last 4 digits of an ID number

Organizations choose how to hide data based on the type of data and how they plan to use it. The aim is to hide important details while still keeping the data useful.

Benefits of Data Masking

Implementing data masking delivers several key benefits:

• Enhanced Security – Masking sensitive data is one of the most effective ways to mitigate the risk of data breaches. If someone gets into a database without permission, they will only see hidden data, not the actual sensitive information.
• Many industries must follow strict data privacy regulations such as HIPAA, PCI DSS, and GDPR to protect sensitive data. Data masking helps achieve and maintain compliance with these standards.
• Improve DevOps and Analytics with masked production data to enhance building, testing, and optimizing applications and models. They can work with data that looks and acts like the real thing, without the security concerns.
• Easily share masked data with vendors, partners, and offshore teams to work together. The original sensitive data never leaves the organization.

When implemented properly, data masking significantly reduces data security risks without hindering data utility and collaboration.

Data Masking in PostgreSQL

PostgreSQL provides several features and extensions that enable effective data masking. Here are some of the key methods:

Dynamic Data Masking

As of version 16, PostgreSQL supports dynamic data masking via the CREATE MASK statement. You can set up masking rules that automatically apply to query results from tables with sensitive columns.

For example, to partially mask an email column to show only the first 2 and last 2 characters, you can define a mask like:

CREATE MASK email_mask WITH (aaa.bbb@ccc.ddd) RETURNS
TEXT AS $$
CASE
WHEN current_user = 'admin' THEN aaa.bbb@ccc.ddd
ELSE substring(aaa.bbb from 1 for 2) || '****' ||
substring(aaa.bbb from '@.*$')
END
$$

This checks the current user and applies the email masking function to the email column (aaa.bbb@ccc.ddd) for non-admin users. The result might look like:

jo****@company.com

na****@gmail.com

Dynamic data masking is a great option when you need to customize masking based on the user or session context.

Data Masking Extensions

PostgreSQL has several extensions that provide data masking capabilities:

• pgMask – an open-source extension that makes it easy to hide columns by replacing them with random or null values.
• pgAnonymizer – a utility that generates an anonymized dump of a PostgreSQL database by replacing column values with pseudonymized data using customizable masking rules.
• Permamask – a procedural masking extension that allows you to permanently replace sensitive data with masked values. Original values cannot be recovered.

These extensions make it easy to implement masking for common use cases without a lot of custom development.

Custom Masking Functions

For complex data masking, PostgreSQL lets you make custom masking functions with PL/pgSQL or other server-side languages. You can define masking rules using complex logic based on data types, patterns, and conditional replacement.

For instance, to randomly shuffle the values in an last_name column, you can define a function like:

CREATE FUNCTION mask_last_name() RETURNS TRIGGER AS $$
BEGIN
UPDATE employees
SET last_name = (SELECT last_name
FROM employees
ORDER BY random()
LIMIT 1)
WHERE employee_id = NEW.employee_id;
RETURN NEW;
END;
$$ LANGUAGE plpgsql;

This function automatically rearranges last names in the employees table. It uses an AFTER INSERT trigger when new employees are added.

Data Masking Best Practices

To get the most value out of PostgreSQL data masking, follow these best practices:

• Classify Data – Determine which data elements are sensitive and need to be masked. Consider regulatory requirements and business needs.
• Define Masking Rules – Establish clear rules for how each type of sensitive data should be masked. Document the methods and mappings.
• Use the Right Masking Techniques – Select context-appropriate techniques that balance data protection and utility. Avoid reversible methods for highly sensitive data.
• Implement Role-Based Access – Use PostgreSQL roles and permissions to limit access to masked and unmasked data based on user profiles.
• Monitor and Audit – Regularly review masking configurations and user activity logs to detect unauthorized changes and access attempts.

By following these guidelines, organizations can reduce data security risks and encourage responsible data sharing and collaboration. PostgreSQL’s data masking features can help with this process.

Conclusion

Safeguarding sensitive information is a critical responsibility for any organization. PostgreSQL has strong data masking features to protect sensitive data while keeping its usefulness intact.

You can create secure masked data sets by using dynamic masking, extensions, and custom functions. This is useful for application development, analytics, and data sharing.

Improve data security and management by investing in a robust PostgreSQL data masking strategy.

Data Masking in PostgreSQL With DataSunrise

DataSunrise offers a secure and reliable way of static and dynamic data masking in PostgreSQL. Contact our team to schedule a demo and discover the possibilities of DataSunrise now.