Redaction vs. Masking: What’s the Difference?

Redaction and masking are two types of data protection tactics. While they are similar, there are differences. In this guide on redaction vs. masking, you’ll learn what each is, its use cases, and more.

Data redaction involves obscuring sensitive elements from data while preserving its usability. The structure of the information remains the same, which ensures the context remains.

There are several examples of data that you’d want to redact, including:

• PII (personally identifiable information): Social Security numbers, driver’s license numbers, names, addresses
• Financial information: Credit card numbers, bank account numbers
• PHI (protected health information): Medical account numbers, health plan numbers, medical diagnoses
• IP (intellectual property): Trade secrets, contracts

You can use redaction to satisfy compliance requirements, such as GDPR, CCPA, HIPAA, and PCI.

Data masking replaces sensitive alphanumeric characters with random characters, using the same format. With these “masks,” the data is no longer sensitive and usable for business purposes.

Organizations typically need to mask data across many platforms, from Oracle to Salesforce to Hadoop and more.

Masking is most effective when you centralize your policies, leveraging technology to discover data continuously with automated remediation. Any time you create or move data, masking is automatic.

You can use masking to support the mandates around data security included in PCI, HIPAA, and GDPR.

Data redaction and masking do sound similar when defined, but there are differences. The major distinction between data masking and redaction is format preservation versus total concealment.

Format Preserving vs. Total Concealment

Masking keeps the data functional. The data needs to retain its original structure. By masking this, you minimize your risk footprint. Random characters take the place of the actual characters.

Redaction totally conceals the sensitive elements within the data. It can also retain the structure. So, a Social Security number then looks like this XXX-XX-6789. Should a data breach occur, the leaked information has no value. It’s helpful when users need the data but don’t have the authorization to view it unredacted.

Use Cases

Redaction and masking have unique use cases.

A typical application for redaction is data sharing. Many different groups can use redacted data for insights safely.

An example of this is redacting credit card numbers by a financial organization. These companies need to send this information to merchants and must adhere to PCI standards. With automatic detection and redaction from a data protection platform, you streamline this process and ensure compliance.

Redaction is often part of legal and classification document releases. This information no longer needs to be usable, but you must obscure it for confidentiality reasons.

Masking’s most common use cases are for development, analytics, and testing. With the growth of AI models, masking has become an essential treatment occurring before the data feeds into these.

Lower-level environment masking has become crucial for development activities. Databases often have a collection of sensitive datasets that may require customizable masking. Examples include:

• Keeping the length of the original entry and formatting
• Preserving some original character values based on criteria
• Partially masking with the inclusion of special values
• Parsing across multi-field values

Controlling these attributes means that masking is versatile.

The differences described and the use cases provide a great foundation for when to use redaction vs. masking. Here are the questions you may ask:

Do you need the obfuscation to conceal completely or retain formatting?

• If it’s the former, you’ll want to redact. If it’s the latter, you should select masking.

What usability actions do you need to preserve?

• You can review the use cases to determine which is the best fit.

Which type of data is it?

• Masking and redacting are applicable to any time of sensitive data.

What compliance regulations do you need to meet?

• Depending on the specifications regarding data protection, one may be better than the other.

PK Protect combines data discovery and remediation in one centralized platform. No matter which action you need to take, you can do so consistently with policy-driven data protection strategies.

See how it works today by requesting a demo.