What Is Data Masking?

Data is the lifeblood of the successful modern enterprise, allowing businesses to accelerate innovation, optimize decision-making, and drive revenue growth. Accurate and copious data is also essential fuel for the transformative AI technologies blazing their way across every industry.

Yet, data is also a magnet for bad actors.  Today’s cybercriminals are not only continuously escalating attacks on business networks large and small, they’re employing increasingly sophisticated methods to help their operations, such as weaponizing popular AI bots like ChatGPT to generate compelling phishing campaigns, exploit vulnerabilities, and even code advanced malware to order. If successful, the data breaches that follow can devastate your operations, cost millions or billions in losses, trigger substantial fines for regulatory violations, and shatter customer trust.

So, how can you supply your core enterprise software tools and AI systems with all the data they need without leaving it accessible to hackers, leakers, or other malicious agents?

What Is Data Masking? Core Concepts

Data masking is an information security technique that replaces original sensitive data with structurally similar but inauthentic versions, so that the information can be safely employed in enterprise software applications or AI systems. For example, masked healthcare data can be used to train machine learning models, enabling AI agents to develop disease pattern recognition without needing to access or store confidential health records.

Organizations can use data masking to comply with privacy regulations like the GDPR, CCPA, and HIPAA, by applying to any data classified as:

• Personally Identifiable Information (PII): Names, addresses, social security numbers, driver’s license numbers.
• Protected Health Information (PHI): Medical records, insurance information, diagnosis codes.
• Financial Data: Credit card numbers, bank account details, transaction records
• Credentials and Authentication Data: Usernames, passwords, access tokens, and security questions
• Biometric Data: Fingerprints, facial recognition data, and other biological identifiers
• Intellectual Property: Trade secrets, proprietary algorithms, confidential business data

Data masking is one of many methods of securing information. Two other top data protection methods to be aware of are data tokenization, and data anonymization. In data tokenization, sensitive data values are replaced by unique but random strings, enhancing security while minimizing the data companies need to store. It is particularly used to safeguard credit card information, but can be applied to various types of sensitive data beyond financial transactions.

Meanwhile, data anonymization is a set of processes businesses use to completely and permanently remove personal information before sharing data publicly. Masking contrasts with anonymization as it keeps the initial information intact and is more often used for securing internal data transfers.

Why Data Masking Matters for Your Business

As regulatory scrutiny intensifies and cyber threats multiply, data defense has become a top priority for businesses in all industries. Laws such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA) require businesses to protect private information and include severe financial penalties for non-compliance. Even as California and EU regulators expand enforcement efforts and increase fines for both the CCPA and the GDPR, other jurisdictions are working on tightening their own data protection regimes.

At the same time, cybercriminals are taking advantage of advanced AI to step up the frequency and complexity of their attacks, breach outdated network defenses and expose critical company information. For companies to confidently embrace digital transformation, they must be able to share data across development, testing, and analytics environments without increasing cyber risk. Data masking provides the solution to these challenges by allowing data to be used in operational environments while keeping private information securely protected from bad actors.

Common Types of Data Masking

The three most standard types of data masking are static, dynamic, and on-the-fly. The ideal choice for your organization depends on the sensitivity and compliance requirements of each intended use case. Let’s break down these approaches:

1. Static Data Masking: Substitutes genuine production data with synthetic information based on specific, pre-defined rules that are consistently applied to all copies.
2. Dynamic Data Masking: Conceals sensitive data as users access it. Commonly used for role-based access control, where unique masking rules can be applied based on each user’s access permissions.
3. On-The-Fly Data Masking: Transforms data in real time during the transfer process between systems. A subset of dynamic masking, this process applies masking rules as data moves through integration points without requiring modifications to the client application or database.

Key Data Masking Techniques

The effectiveness of any masking strategy hinges on the specific techniques used to transform the original information. Companies use a wide range of methods, including:

• Substitution: Replaces original values with realistic alternatives from predefined lookup tables. For example, real customer names might be replaced with fictional values while conserving demographic distributions. This retains data realism while shielding original values.
• Shuffling: Randomly rearranges data within a column, sustaining the overall data distribution but breaking connections to specific individuals. Salary figures might be shuffled among employee records, preserving the statistical properties of the dataset while protecting individual compensation details.
• Redaction: Partially or completely obscures sensitive information with generic characters (like asterisks or Xs). This is most commonly seen when credit card numbers are displayed as “XXXX-XXXX-XXXX-1234,” revealing only the last four digits. It’s appropriate for use cases that don’t require realism.
• Range-Based Masking: Converts exact values to ranges that correspond approximately to the original data distribution. For instance, specific ages might be replaced with ranges (25-34, 35-44), ensuring analytical value while hiding individual details.
• Date Aging: Shifts date values by a consistent amount forward or backward in time to keep timing patterns while obscuring actual dates. Specifically, you might shift all dates in a medical record table back by six months, maintaining the intervals between appointments while protecting actual treatment timelines.

Business Use Cases for Data Masking

Data masking can be applied in critical operations across your enterprise, enabling teams to work with functional data while strengthening information security and upholding compliance standards. This process is often used for:

1. Software Development and Testing

Software development teams need realistic data to build effective solutions without exposing customer information. Masked production data enables developers to validate application logic, test edge cases, and troubleshoot issues using information that behaves like real data but contains no sensitive elements. This accelerates development cycles and reduces compliance risks.

2. Analytics and Business Intelligence

Data scientists and business analysts require comprehensive data sets to uncover meaningful insights and support accurate decision-making. Data masking allows these teams to work with information that’s statistically valid but doesn’t contain any identifying details, improving analyst efficiency.

3. Training and Documentation

Effective employee training incorporates accurate portrayals of day-to-day scenarios. Masked data can be used in a training context so that new team members can become proficient with enterprise systems using lifelike data that mirrors production environments.

4. Third-Party Collaboration

Although highly sensitive data may need to be fully anonymized before it is shared externally, data masking can be used as an alternative data protection method in low-risk scenarios. Masked data can be leveraged for outsourced development, cloud migrations, and managed service arrangements, enabling the flow of data across organizational boundaries without weakening your security posture.

Key Challenges in Data Masking Implementation

Successfully adopting data masking requires attention to some core technical and organizational concepts such as referential integrity, format preservation, and scalability. For example, if a customer’s information exists in CRM, billing, and service systems, that information must be masked identically across all three platforms to preserve relationships between tables and maintain application functionality. Format preservation is also essential: credit card numbers have to follow validation patterns, and addresses should retain their geographic logic. If masking alters these formats, system functionality will break.

Additionally, as data volumes expand and systems multiply, organizations often struggle to scale complex masking processes. Without taking steps to carefully optimize performance, bottlenecks will form when applying masking rules across different use cases.

Implementing Data Masking Best Practices

To avoid falling into a quagmire of disjointed processes, data silos, and poor accountability, you’ll need to follow a carefully crafted strategy. Maximize the value of your data masking initiatives by following these field-tested best practices:

• Automate Data Discovery and Classification: Manual discovery is slow and vulnerable to human error, leaving critical data hiding in unexpected locations. Take advantage of automated scanning tools that can automatically classify sensitive data patterns across your enterprise.
• Define Your Policies: Create a centralized policy framework that defines what data requires masking, appropriate methods for each data type, and which roles can access unmasked information under specific circumstances.
• Ensure Consistency: Deploy a unified platform rather than point solutions to maintain consistent rules across technology environments. This avoids security gaps between systems.
• Maintain Integrity: Document data relationships before implementation and test thoroughly afterward to verify that masked values preserve essential connections between related database tables.
• Test and Validate: Validate masked data with actual business processes and applications before full deployment to check that it meets both functional requirements and compliance standards.
• Continuously Monitor and Audit: Establish regular compliance monitoring processes with documented evidence that masked data remains protected according to policy as systems and data evolve.

How Boomi Enhances Data Security Through Data Masking

Boomi delivers comprehensive data masking capabilities that integrate flawlessly with broader data management functions, so that you can protect sensitive information throughout your enterprise and unlock hyperproductivity.

The Boomi Enterprise Platform offers an impressive array of data security features, including:

Boomi DataHub for Comprehensive Data Masking

Boomi offers data masking as a built-in feature of Boomi DataHub. Using DataHub’s low-code interface, you can quickly and easily set up masking rules to replace sensitive data fields. Activate partial or full masks on any column or field of data in just a few clicks, or use custom scripts to tailor masking to your unique requirements.

AI-Powered Data Discovery and Classification

The Boomi platform leverages AI to classify PII and track sensitive data as it moves through different systems and jurisdictions. With automatic data discovery, you can reduce the risk of failing to mask sensitive data and verify compliance with regional laws without exhaustive manual cataloging.

Unified Governance and Security Controls

With granular role-based access controls, Boomi ensures that only authorized users can view unmasked data based on legitimate business needs. Detailed audit and reporting capabilities track who accesses what data and when, providing the transparency and documentation required by regulators and enterprise security frameworks.

Integration with Data Management Workflows

Boomi incorporates data masking as part of its broader data integration offerings, providing consistent protection throughout the data lifecycle across hybrid and multi-cloud environments. As the leading integration as a platform service (iPaaS), Boomi offers cohesive, seamless management for diverse technology stacks, from legacy appliances to advanced, AI-powered ecosystems, eliminating the security gaps that often occur at integration points and achieving holistic data security.

Leverage Boomi To Secure Your Data

In a rapidly evolving market fraught with risk, balancing data security with business utility is indispensable. Poor information security can expose your business to data breaches, which can catastrophically derail your operations and stall revenue. On the other hand, failing to take full advantage of your data undermines your competitive advantage, hobbles your ability to grow, and stifles innovation. Data masking eliminates these issues, enabling your organization to take full advantage of production data for everything from development and testing to external collaboration, all while maintaining robust security.

With Boomi’s comprehensive data masking capabilities, you can build a foundation of trusted data to optimize your AI initiatives, improve your decision-making, and simplify integrations.

To learn more about how enterprise organizations are getting the most from their data, read the 2024 Hanover Data Liquidity Index Study.