Blog
June 20, 2025
Data Masking vs. Tokenization: When to Use What
Data Management,
Security & Compliance
Data masking vs. tokenization: which should your organization be using to protect sensitive data?
The simplest answer: if you need to easily re-access original data, tokenization is preferable. If you need irreversibly transformed data for development or analytics, masking is the superior choice. (This is especially true when it comes to using data for AI. In fact, 83% of organizations worry about personal data being re-identified in AI systems, according to our State of Data Compliance and Security Report.)
In this blog, we will dive into the specific challenges and strengths for masking vs. tokenization before exploring one solution and its use cases across industries.
But first, let’s discuss the basics: what do these terms mean?
What’s the Difference Between Masking and Tokenization?
To start, let’s go over some quick definitions and key differences between data masking and tokenization.
Data Masking
Data masking is a method that hides sensitive data by replacing it with realistic but fake information. The main benefit is that masked data keeps the same format and usefulness as the original data but doesn't show sensitive information.
Data masking has become essential for building and testing applications. It lets teams work with realistic data without the risk of exposing sensitive information.
In this blog, we'll focus specifically on static data masking. This is where data gets permanently changed before it goes into non-production environments.
Tokenization
Tokenization is a data masking technique that replaces sensitive data with non-sensitive placeholders called "tokens." These tokens keep the same format and structure as the original data while protecting the real values.
The original sensitive data is stored safely in a "token vault." This vault maps the tokens back to their original values. Only authorized users can access the original data when needed.
Key Characteristics of Tokenization
- Preserves data format and length.
- Can be reversed (you can get original values back when authorized).
- Creates separation between sensitive data and working systems.
- Widely used for PCI DSS compliance and payment data protection.
Understanding the Key Challenges of Masking and Tokenization
Before we dive into key considerations when deciding between data masking and tokenization for enterprise environments, let’s cover some of the primary challenges of both methods.
Data Masking Challenges
Maintaining Referential Integrity
Data masking should preserve referential integrity across related database tables. In other words, if a value appears in multiple places, the masked value should remain consistent. If values are not consistent, systems that use this data will break.
Impact on Analytics & Testing
Simply replacing all values with the same placeholder often makes the data useless for analysis. For example, if you mask all customer names as "John Doe," you can't analyze trends based on names or regions.
Tool and Platform Compatibility
Adding home-grown masking solutions often requires custom code or middleware. This added layer of complexity makes systems more challenging to maintain.
Tokenization Challenges
Key Management & Vault Dependency
Tokenization relies on secure, centralized token vaults to store and retrieve token mappings. If a vault goes down, slows down, or isn’t set up correctly, any process that needs real-time access to tokens can fail.
Reversibility & Risk Trade-offs
Tokenization is designed to be reversible, meaning it allows data to be restored when needed. But this also means access must be carefully controlled to manage who can “de-tokenize” sensitive information. Without strong controls, there's a risk of exposing protected data.
Integration with Legacy Systems
Older systems or reporting tools may not handle tokens well. They may expect data to be in its original format or rely on exact values—like full credit card numbers—for functions such as lookups or validation.
Masking vs. Tokenization: Key Differences for Enterprise Leaders
| Feature | Data Masking | Tokenization |
| Data Transformation Approach | Changes the actual data values while keeping the format and statistical properties the same. | Replaces data with a token that points back to the original value stored in a secure vault. |
| Reversibility and Data Access | By design, static masking typically cannot be reversed. This reduces the risk of sensitive data leaks. | Designed to be reversible through the token vault for authorized users. Maintains access to original data, which creates potential exposure points. |
| Implementation Impact on Development Teams | Provides realistic test data without compliance risk. Generally, more straightforward to set up. | Needs additional infrastructure and token management processes. |
| Performance and Scalability | No runtime performance impact with static masking. Scalable across enterprise applications and databases. | Potential performance impact from token lookup operations. Scalable across enterprise applications and databases. |
| Security Reality | Irreversible static masking truly removes the possibility of sensitive data leaks. | Reversible method where underlying data stays intact. Your system security is only as strong as its weakest access point. |
Compliance Requirements: Masking vs. Tokenization
Regulations like the General Data Protection Regulation, California Consumer Privacy Act, and Health Insurance Portability and Accountability Act do not specifically require you to choose between masking or tokenization. Each organization has unique compliance needs and risk profiles. Your decision should reflect these specific factors.
| Data Requiring Protection | Non-Sensitive Analytical Data |
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Choosing the Best Method for Compliance
Choosing the right data protection method starts with a thorough risk assessment. Begin by analyzing the sensitivity of your data and understanding the regulatory requirements that apply to your industry.
For highly sensitive information, such as medical records or financial data, static masking is often the best option. By permanently removing identifiable elements, it minimizes the risk of data exposure. On the other hand, tokenization can be a suitable choice for moderately sensitive data when the ability to retrieve original values is necessary. This method allows for secure re-identification, balancing data accessibility with protection.
Consider your data usage patterns too. If teams need realistic test data but don't require access to the real values, masking is typically a better choice. On the other hand, if business operations rely on retrieving original data values, tokenization may be essential, even though it carries a slightly higher security risk.
Review your compliance obligations carefully. Regulations like GDPR and HIPAA have strict rules about data privacy. Static masking often provides the strongest compliance advantage because it eliminates sensitive data completely.
Masking vs. Tokenization for AI Development
Poorly implemented data protection in AI environments could lead to privacy violations, compliance failures, and bias in AI outputs.
AI models can learn sensitive data and find patterns that allow re-identification of anonymized data. Organizations often implement misguided compliance efforts with reversible tokenization. These tokens could expose sensitive information when they're converted back to original values.
Irreversible static masking is critical for AI training data. This method permanently transforms sensitive data while maintaining its analytical usefulness.
Don't let AI keep you up at night. Learn the three key challenges you must address now to protect sensitive data in AI and analytics environments. And discover best practices for safeguarding your organization.
Sleep Better: Get AI Without Compromise
Selecting the Right Approach for Your Enterprise Environments
| When to Choose Tokenization | When to Choose Data Masking |
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Many teams struggle to add masking or tokenization into their development processes. Security teams often work apart from development teams. This siloed work style can cause misalignments in the CI/CD pipeline. When data masking or tokenization isn't automatic, it leads to delays and manual fixes.
Developers sometimes use protected data for testing without verifying changes. This practice can lead to mistakes and incorrect assumptions about the data's functionality.
In one real-world example, a financial technology company I worked with tried adding masking to their development pipeline using scripts. But issues with settings and missing central rules caused test systems to expose private information. The company had to conduct a complete audit to find the problems and rebuild their pipeline with better security rules.
Mask More Data With Perforce Delphix
Delphix automatically discovers sensitive data, then irreversibly masks it with realistic yet fictitious values. It gives teams high-quality, compliant data for development, testing, AI, and analytics.
How it Works
Delphix automatically finds sensitive data across a broad array of databases, data warehouses, and data pipelines. It then replaces that data with realistic, production-like values. This is done using a full library of pre-built and customizable algorithms. This method keeps the data useful and keeps relationships between data intact.
Delphix works across all your data sources — from small SQL databases and on-premises systems, to massive cloud platforms like Snowflake, Databricks, and Oracle.
With Delphix, compliant data is delivered quickly and automatically. Your development, testing, and analytics teams get the data they need, when and where they need it.
Proven Results Across Industries
Organizations worldwide trust Delphix for comprehensive data masking and compliance. Here's how leading enterprises protect sensitive data while accelerating innovation:
Financial Services: Boeing Employee Credit Union
Boeing Employee Credit Union (BECU) needed a way to automatically find sensitive data and mask it the same way every time. Using Delphix, they masked 680 million rows in just 15 hours. This allowed over 200 developers to get self-service access to data.
“Not only does Delphix reduce our risk footprint by masking sensitive data, but we can also give developers realistic, production-like environments." — Kyle Welsh, CISO, BECU
Hospitality: Choice Hotels
Before Delphix, database updates took weeks to complete at Choice Hotels. Developers were getting in each other’s way. They also struggled to protect their customers' private information. After using Delphix, they cut the time to rebuild their systems from two weeks to less than one day. They also sped up their development work and successfully set up automated masking of PII/PCI data across all their systems.
Watch Choice Hotels' testimonial
Insurance: Delta Dental
Delta Dental used to spend 8 weeks just extracting data. Protecting sensitive data for compliance was difficult. With Delphix, they can mask data and easily deliver it to a team of 200 developers in minutes.
Read the Delta Dental case study
See For Yourself How Delphix Masking Works
With Delphix, you don’t have to choose between speed, quality, and security.
Get a no-pressure demo from our product experts to see how Delphix lets you ensure compliance, eliminate data risks, remove bottlenecks, and deliver higher-quality data for better AI and analytics outcomes.