Data Masking vs Tokenization: Know the Differences and Use Cases

Protecting sensitive data while keeping it usable is a constant struggle for companies today. With data breaches happening more often and getting more sophisticated, knowing how to protect valuable information is essential. While there are many ways to protect data, in this article, we compare Data Masking vs Tokenization to understand how they are different and can help your data security strategy.

This article breaks down the differences, benefits, and practical uses of data masking and tokenization so you can make a better choice for your organization’s data security. We’ll look at how each approach works, its strengths and weaknesses, and where each method really shines.

If you’re curious about how data masking compares to other techniques like anonymization, be sure to check out our article on Data anonymization vs. Data masking for more context. But for now, let’s dive into data masking and tokenization.

What Is Data Masking?

Data masking is a method used to disguise sensitive information in datasets while still keeping the overall structure and usability of the data. Data masking can be used in production and non-production environments, places like testing, development, and training where data needs to be realistic but not actually sensitive.

How Data Masking Works

Data masking replaces real data with fake, yet believable values. For instance, a set of customer names might be replaced with random but plausible alternatives like “John Doe” or “Jane Smith.” The goal is to protect the data from being exposed while keeping it in a useful state.

Types of Data Masking Techniques

Let’s look at some common data masking techniques that help secure non-production data environments:

1. Static Data Masking: Creates a separate masked copy of the production database for non-production environments, ensuring that sensitive data doesn’t slip into less secure areas.

2. Dynamic Data Masking: Masks data in real-time as it’s accessed based on user permissions, allowing different users to see different versions of the data depending on their access rights.

3. Deterministic Data Masking: Ensures consistency by replacing specific data values with the same masked values across datasets. This technique is useful for maintaining referential integrity and relationships between data.

Core Benefits of Data Masking

Data masking offers multiple advantages, especially in non-production scenarios:

1. Protects Sensitive Data: It shields confidential information from unauthorized access.

2. Regulatory Compliance: Meets data privacy regulations by ensuring real data isn’t used in development or testing.

3. Data Integrity: Preserves the usability and format of the data, which is crucial for testing and training without risking exposure.

4. Seamless Testing and Development: Enables secure development, software testing, and analytics without compromising sensitive data.

Static data masking is ideal when you need a safe, yet realistic dataset for development, testing, or training without exposing real customer data.

What Is Tokenization?

Tokenization replaces sensitive data with unique symbols or “tokens” while storing the original information separately. It’s designed to protect live data in transaction-heavy environments like payment systems and active customer databases.

How Tokenization Works

Tokenization follows these steps:

1. Data Collection: Sensitive data, like a credit card number, is captured.

2. Token Generation: The system creates a random string, or token, that has is not related mathematically to the original data.

3. Token-Data Mapping: The original data is stored securely in a separate location called a token vault. The data in the vault is linked to the token.

4. Token Distribution: The token replaces the original data in your systems.

5. Data Retrieval: When needed, the token can be used to retrieve the original data from secure storage, but only for authorized users.

In essence, tokenization completely removes the sensitive data from your environment and substitutes it with a non-sensitive placeholder.

Common Tokenization Techniques

Tokenization can be implemented in various ways, each with its own strengths and ideal use cases. Consider these tokenization techniques:

Vaulted Tokenization

Stores sensitive data in a highly secure “vault” and maps it to a token. Commonly used in payment systems, this method securely links tokens to credit card numbers or other high-value data.

Vaultless Tokenization

Instead of storing sensitive data, vaultless tokenization uses algorithms to create and reverse tokens on the fly. This approach is faster and more efficient in high-transaction environments.

Format-Preserving Tokenization

Generates tokens that retain the format of the original data, like replacing a 10-digit phone number with a 10-digit token. This is useful in finance and other fields where maintaining data structure is essential.

Key Differences Between Data Masking vs Tokenization

While both data masking and tokenization aim to protect sensitive information, they have distinct characteristics that make them suitable for different scenarios. Understanding these differences is crucial for choosing the right method for your specific data protection needs.

Security Level

Data Masking: Protects data in non-production environments like testing and training by replacing it with fake but realistic values.

Tokenization: Protects sensitive data in live environments by replacing it with meaningless tokens. This makes it much harder for hackers to extract value from a breach.

Reversibility

Data Masking: Generally non-reversible. Once data is masked, it cannot be turned back into its original form. This permanence makes it ideal for scenarios where the original data is not needed. Examples might include during software testing or data analytics.

Tokenization: Reversible by design. Authorized users or systems can retrieve the original data using the token, making it suitable for situations where access to the actual data may be necessary including when processing payments or verifying identities.

Use Cases

Data Masking: Static data masking is best for software testing, training, and non-production uses where real data isn’t needed. Dynamic data masking is effective across environments.

Tokenization: Ideal for payment processing, healthcare data protection, and e-commerce, where you need strong protection without compromising data usability.

Regulatory Compliance

Data Masking: Helps comply with regulations by ensuring that sensitive data is not exposed in non-production environments. It’s particularly useful for meeting requirements related to data minimization and purpose limitation.

Tokenization: Especially effective for compliance with stringent regulations like PCI-DSS. By removing sensitive data from systems and replacing it with tokens, tokenization can significantly reduce the scope of compliance audits and the associated costs.

Strategic Implementation Considerations for Data Masking vs Tokenization

When deciding between data masking and tokenization, consider your business needs, compliance requirements, and the specific context in which your data is used. Here are some key factors to think about:

Assessing Business Impact and Needs

First, assess the types of sensitive data you handle and where it flows within your organization. If your primary concern is protecting data used in development and testing, data masking is a practical solution. But if you’re dealing with live, transaction-heavy data like payment details, tokenization is the way to go.

Evaluating Cost-Benefit and ROI

Data masking tends to be less complex and cheaper to implement, making it cost-effective for non-production use. For production environments, organizations can use dynamic data masking that enables fine-grained policies to be implemented. Tokenization, on the other hand, requires investment in secure token vaults but can lower compliance costs and reduce risk in high-stakes environments.

Considering Customization and Scalability

Consider your organization’s growth plans. If you’re planning to handle more sensitive data types or expand into new areas, you might need a solution that can scale. Tokenization offers flexibility and can be adapted to protect various data types across multiple systems.

Future-Proofing Data Protection

If you’re dealing with stringent regulations like PCI-DSS, tokenization can help reduce audit scope and compliance costs by keeping sensitive data out of your environment. Static data masking is more suitable when regulations require that sensitive data is anonymized in non-production scenarios. However, dynamic data masking can act as a simple yet effective solution for masking data across environments and achieving the necessary compliance standards.

Dynamic Data Masking with Pathlock

Dynamic Access Controls (DAC) from Pathlock is built on an Attribute-Based Access Control (ABAC) security model. This enables a customizable and scalable, policy-based approach to data security, governance, and access control. Since the module’s dynamic data masking capabilities are governed by these easily configured ABAC policies, you can ensure that sensitive SAP data and transactions will be obfuscated without fail in scenarios where user access or actions indicate risk as defined in your organization’s custom policies.

The module’s centralized ABAC policy administration capabilities ensure that you can easily define and apply granular, dynamic access control policies without the need for redundant policy administration efforts on a per-role basis. With an intuitive user interface, customizing the out-of-the-box policies or creating your own is as easy as selecting filters to apply and requires no technical expertise for configuration.

Ultimately, the DAC module provides a least-privilege security approach that goes beyond traditional access controls, allowing organizations to ensure data security while still allowing employees to perform their necessary duties on a need-to-know basis.

Sign up for a demo today to see how Pathlock can protect your data while enabling your users to work in a secure and compliant access environment.