Graham Thompson

 ·  7 min read

Data Masking vs. Data Redaction: Key Differences and Uses

Businesses are constantly fighting to balance the pressing need for data utility with stringent requirements for assuring data privacy. Masking and redaction offer two proven ways to bridge that problematic divide. Here's how to tell which is best for your specific needs.

Data Anonymization

In the ever-evolving IT security landscape, digitally dependent businesses need effective and dynamic data protection methods to safeguard their most sensitive assets. Two techniques in particular, Data Masking and Data Redaction, offer practical ways to protect sensitive information and ensure organizational compliance with the ever-expanding corpus of data privacy regulations.

Choosing between masking and redaction depends on your organization's specific needs, operational framework, security requirements, and the nature of the data it handles. If your organization frequently needs to share documents containing sensitive data, data redaction might be the better choice. However, data masking would be the preferred option if your organization needs to use actual data for testing or training without risking privacy.

Evaluating your data privacy requirements and working with a data security professional can provide further guidance tailored to your organization's needs. Let's understand these two crucial data protection methodologies better and see how and where they can be best used.

Data Masking Defined

Data Masking, also known as data obfuscation or data anonymization, is a method that involves disguising original data. The primary aim of data masking is to ensure that sensitive information remains confidential, even when accessed by unauthorized users. It creates a structurally similar but inauthentic version of the data, which can help in diverse areas, such as software testing and customer analytics, without risking compromise of the actual sensitive data.

For example: Suppose you have a customer information database with sensitive data like social security numbers. These social security numbers might be replaced in a masked environment with fictitious yet structurally similar numbers. So, a social security number like 123-45-6789 might be masked as 987-65-4321.

At a high level, data masking is most helpful when an organization needs to use sensitive data for non-production purposes such as testing, training, and development. It helps keep the data realistic but not directly identifiable while maintaining its usefulness for operational tasks.

Data Redaction Defined

On the other hand, data redaction removes sensitive information or content from a document or digital medium. It ensures that the sensitive information is not visible or accessible to unauthorized users. Unlike data masking, redaction does not replace sensitive data with similar, non-sensitive data. Instead, it removes the data, often replacing it with blacked-out or blank spaces.

For example: Consider a legal document containing sensitive information such as a client's name, address, and bank details. It would be redacted if this document needs to be used in a public court hearing. The original information, let's say—"Mary Jones, 123 Main Street, Bank Account No. 456789"—would be redacted and replaced with blacked-out lines or blank spaces, effectively removing the sensitive data from any public versions of the document. This preserves the confidentiality of the subject's private and personally identifiable information.

Again, from a 10,000-foot perspective, data redaction is preferred when orgs need to share documents externally but want to ensure any sensitive information contained therein remains confidential. Redaction permanently removes or obscures specific data within a document, making it safe for distribution outside the company.

Data Masking and Data Redaction: A Matter of Approach

At a more granular level, while they both aim to protect sensitive information, data masking and data redaction differ significantly in their approach and application. A few key distinctions:

  1. Nature of the Affected Data. Data masking replaces sensitive data with contextually similar, non-sensitive data. It retains both the structure and the original data type, making it particularly useful for software testing environments. Data redaction removes the sensitive data entirely, making it more suitable for situations where the original data will never be used or reaccessed.
  2. Reversibility. In most cases, data masking is functionally irreversible, making this obfuscation very secure. There are ways, however, that masking transforms (data pseudonymization, for example) can be made reversible at the beginning of the project, ultimately allowing an authorized user to revert masked data to its original form. Data redaction is nearly always 100% irreversible when done correctly. Once the data has been redacted, it can never be recovered or viewed again.
  3. Best Use Cases. Data masking is often used in non-production environments, for example, during software testing or development, where data structure needs to be maintained without exposing sensitive information. Data redaction is commonly used before sharing documents outside the organization, where specific details must be hidden.

Masking and Redaction Examples and Use Cases

Understanding the differences and strengths of data masking and data redaction is easier when we examine how the two methods work in practice. Some real-world examples:

Scenario 1: Data Masking in Web Development for Financial Services

Consider a large financial institution developing a new customer portal. The dev team needs realistic customer information to test portal functionality and performance. Still, they can't use actual data like account numbers, social security numbers, or addresses due to regulatory requirements and basic common sense regarding privacy. Here, masking can create a "sanitized" version of the database, replacing all sensitive fields with fictional but structurally similar data. This 'masked' database can then be safely used for testing. It does not risk exposing sensitive customer information while providing a realistic and practical testing environment.

Now, think about a law firm that needs to submit court documents in a public case. These documents contain sensitive client information such as names, addresses, social security numbers, and financial records, all of which must remain confidential. The legal team must share these documents with the court and opposing counsel but can't risk divulging personally identifiable information. Redaction is the obvious choice here; it can irreversibly conceal specific parts of the document while ensuring that documented facts pertinent to the case are clear and available to the judge, jury, counsel, and the court record.

Spotlight on Data Masking

As we've noted, data masking and data redaction play crucial roles in data security. However, data masking is emerging as a preferred method in core data protection strategies for a host of reasons, including:

  1. Preserved Data Structure. Data masking retains the original structure of the data, ensuring that the masked data maintains all of the important parameters— strings, numerals, floats, character length limits, etc.—of the original. This is crucial when testing software applications where data structure is of the essence.
  2. Trustworthy security. As mentioned earlier, except in very specific cases, data masking is generally considered a non-reversible process, with the original data being replaced by fictitious yet realistic data. As the actual data is never exposed in a masked dataset, the risk of data leakage or misuse is significantly reduced compared to redaction. This can offer a higher level of security than even redaction, which, while theoretically irreversible, is susceptible to workarounds that can "unhide" hidden information.
  3. Data Utility Maintenance. Unlike redaction, which removes information, data masking modifies information, preserving its utility for analytical or developmental purposes.
  4. Regulatory Compliance. If done correctly, data masking can aid in governance and compliance with various data protection and privacy laws, such as GDPR, HIPAA, and PCI DSS, prohibiting the use of personal data for specific purposes.
  5. Ease of Implementation. With a relatively straightforward implementation process, businesses can swiftly integrate data masking into their data protection arsenal, bolstered by the expertise and solutions provided by vendors like Privacy Dynamics.
  6. Scalability. The in-place operation of data masking makes it highly scalable, adeptly handling large datasets and high-velocity data environments.

Conclusion: Unmasking Best Practices for Robust Privacy Protection

Choosing the appropriate data security method still depends mainly on the specific requirements of the use case. As businesses navigate the complexities of safeguarding sensitive data, understanding these techniques—including encryption, tokenization, data anonymization, etc.—can aid organizations in building robust data protection strategies.

Data masking remains a strong candidate for businesses seeking a balanced approach to data protection, compliance, and operational efficiency. The alignment of data masking with the offerings of Privacy Dynamics further accentuates its viability as a robust data security strategy for businesses treading the path of digital resilience.

With its simplicity, cost-efficiency, and irreversible anonymization, data masking aligns seamlessly with overall strategies for robust data protection, making it a preferable choice for many businesses. Moreover, the expertise and solutions offered by Privacy Dynamics further bolster the compelling case for data masking as a robust data security strategy.

We encourage security professionals to examine their current data protection strategies, weigh the merits of masking versus redaction, and consider Privacy Dynamics's comprehensive solutions to improve their data security frameworks.

To explore these ideas further, check out the pages linked below. Gain insights on data anonymization for a test environment, learn how to de-risk data sharing, and understand why businesses need to anonymize data.

To learn more about Privacy Dynamics and how we can help you unlock production data while reducing privacy risk, give us a shout or sign up for a free trial at - We look forward to helping you!