Data Masking Best Practices: Protecting Your Sensitive Information

In a recent blog post, we talked about how legacy privacy techniques like data masking are starting to take a back seat to more advanced privacy-enhancing technologies (PETs) like AI-generated synthetic data, secure multi-party computation, and homomorphic encryption.

Which raises the question: is data masking still relevant? Does it adequately safeguard sensitive data and maintain privacy?

The short answer is yes, data making remains a valuable technique for protecting sensitive data. It’s still the privacy solution of choice for software development and testing, intra-organizational data sharing, and analytics, as it allows data to retain its format, relationships, and usability. So, while some advanced applications benefit more from more recent PET methods, data masking remains a trusted and effective option for scenarios where usability and privacy are equally critical.

This article defines what data masking is, explores data masking techniques, and explains the data masking best practices that ensure you get the most out of this proven technique.

What is a Data Masking?

You’re likely familiar with court records or government documents that, when released to the public, have various sections blacked out, including names, social security numbers, and other private or confidential information that needs to be obscured to protect an individual or entity’s sensitive data.

This redaction process is analogous to data masking, where sensitive data elements are replaced or obscured to ensure only authorized users can access the original, unmasked information. Just as redaction ensures confidentiality in legal matters, data masking guarantees data privacy and security in digital environments.

The most popular types of data masking include:

1. Static data masking (SDM) applies a fixed set of masking rules to the original data, creating a masked version that’s consistent across all instances. It’s often used for non-production environments like testing, development, or training where you don’t want to reveal any actual data.
2. Dynamic data masking (DMD) occurs during actual run time, with data streamed directly from the production system. Unlike static masking, no masked data is saved in another location. DMD allows various users to view different levels of data based on defined access privileges, enhancing security while maintaining data usability.
3. Deterministic data masking replaces column data with an identical value that can be applied to all instances of the original data. For example, if the name Mary is changed to Linda in one place, all instances of the original Mary that appear in other locations are replaced with Linda.
4. On-the-fly masking is a great option for organizations with complex data integrations and who continuously deploy new software versions. Data is simply transferred from one environment to another—for instance, from production to testing—masking only specific subsets of set data when needed.
5. Tokenization is commonly used to secure credit card data and other confidential information by replacing sensitive data with non-sensitive placeholder values or tokens generated by encryption algorithms. The original data’s stored in a separate secure repository.
6. Pseudonymization replaces or encrypts sensitive information with codes or pseudonyms, rendering personal data less identifiable while still allowing it to be used for data analysis, research, or processing.
7. Anonymization irreversibly transforms data so it can no longer be linked to an identifiable individual or entity. All identifiers are removed or modified, ensuring the data becomes completely anonymous.
8. Data encryption transforms sensitive data into ciphertext using cryptographic algorithms. Only authorized users with the appropriate decryption keys can access the original data.
9. Shuffling randomly rearranges data records within a dataset, making it challenging for cyber attackers to link masked data to specific individuals or entities.
10. Data substitution replaces sensitive data with fictitious but realistic data, ensuring data relationships and formats are preserved for testing and analytics while sensitive information remains protected.
11. Format preserving encryption (FPE) encrypts data while preserving its original format. It’s typically used where data format needs to be maintained, such as with credit card or social security numbers.

Each of these techniques addresses different privacy concerns and can be employed in complementary ways to enhance data protection and privacy in various contexts.

Data Masking Use Cases

Data masking is effective across diverse industries and scenarios.

• In the financial sector, it secures customer financial records and enables secure application testing.
• Healthcare organizations use data masking to protect patients’ confidential medical information during research or software development.
• In eCommerce, it secures customer details while facilitating business intelligence and analytics.
• Government agencies find data masking helpful in ensuring privacy and compliance when sharing data for research or public use.

While data masking has it share of challenges, such as format preservation, referential integrity, and data uniqueness, they can be addressed through careful planning, choosing appropriate masking techniques, and conducting rigorous testing.

By adopting a comprehensive data masking strategy and continuously refining it based on evolving requirements, your organization can confidently protect sensitive data, comply with data privacy regulations, and mitigate the risks associated with unauthorized data exposure.

Data Masking Best Practices

A dynamic data landscape demands innovative solutions to secure sensitive information and maintain regulatory compliance. Establishing data masking standards that incorporate industry-leading best practices helps your organization shield sensitive information from unauthorized access and potential breaches.

These data masking best practices fortify data security and confidentiality across diverse business scenarios.

Data Discovery

The first step in implementing data masking is performing a comprehensive organization-wide data audit to determine data flow, storage, and access points. AI-based automation tools make it easy to scan databases and files for sensitive elements like personally identifiable information or financial data. Classification tags further enhance accuracy, and collaboration between data owners, security teams, and compliance officers ensures a unified understanding of sensitive data and aids in defining appropriate masking techniques.

Develop a Data Masking Strategy

Defining clear objectives and goals that consider account regulatory requirements and privacy standards helps you apply the appropriate masking technique, be it tokenization, encryption, or some other method. Data with higher risk exposure should be prioritized, and factors like data relationships should be considered to maintain referential integrity. Strict access controls are needed to limit disclosure, and routine testing of each masking strategy ensures data remains adequately protected.

Select Appropriate Masking Techniques

The aim is to strike a balance between security and data utility. Begin by identifying the data’s sensitivity level and compliance requirements, as different techniques offer different protections levels. For instance:

• Encryption is typically best for high-security needs, as it renders data unreadable.
• Tokenization is ideal where data must retain its form and structure but needs to be protected from unauthorized access.
• Data substitution is useful for ensuring safe data sharing without compromising its integrity or usability.
• Whichever technique you choose, be sure to conduct thorough testing to ensure masked data is functionally equivalent, reducing the risk of unintended consequences.

Testing

Data masking testing validates the initiative’s effectiveness and integrity and ensures sensitive information remains obscured while maintaining its usability for testing purposes. Various tests are performed to verify the accuracy and consistency of masked data against the original data and identify any potential vulnerabilities or loopholes that could compromise the data masking’s effectiveness.

Monitoring

Monitoring and auditing ensure ongoing data protection and compliance. Robust monitoring tools should be put in place to track data access, usage, and attempted breaches. And the regular review of access logs should be conducted to identify any unauthorized access attempts or suspicious activities.

Periodic audits assess your data masking policy’s effectiveness and validate the accuracy of masked data against compliance standards. They should include all relevant data repositories and systems so you can promptly address any security gaps, improve data governance, and maintain data privacy standards.

Data Masking With Velotix

Adhering to data masking best practices is paramount for safeguarding sensitive information. By effectively obscuring or encrypting sensitive information, personal details, financial records, and proprietary data remain protected from unauthorized access and potential cyber threats. It also fosters trust among customers and stakeholders and helps your organization meet stringent data privacy regulations like the GDPR and CCP while avoiding hefty fines and reputational damage.

AI-driven Velotix offers fine-grained control over masking and redaction policies as well as instant audit and compliance reporting. It’s designed to allow businesses to advance at a pace that’s comfortable for them while making data more available for users, applications, and algorithms.

With data breaches becoming increasingly prevalent, implementing robust data masking strategies not only fortifies the security posture of your organization but also demonstrates a commitment to preserving individuals’ privacy rights and fostering a secure and thriving digital ecosystem. That’s a win-win for everyone, creating a safe and more trustworthy digital landscape that benefits individuals and businesses alike.