With cyber threats on the rise—the global average cost of a data breach was $4.88 million in 2024—protecting customer data is more critical than ever. In Salesforce development, testing isn’t just a step in the process; it’s essential. Pushing updates to production without thorough testing can expose your org to security risks, especially when dealing with sensitive business data. But here’s the challenge: You want test data that feels real, but you also need to protect your customer information.
That’s where Salesforce data masking comes in. By replacing real data with anonymized values, data masking minimizes exposure risks in development, testing, and staging environments. It is one of the most effective ways to safeguard sensitive customer and employee information. Implementing a secure data masking policy ensures confidentiality while maintaining data utility for testing and development.
In this article, you’ll learn why Salesforce data masking is a crucial layer of defense against unauthorized access and cyber threats, how it works, and best practices for implementation.
What Is Data Masking?
Data masking is an umbrella term that includes data anonymization, pseudonymization, redaction, scrubbing, and de-identification. It is a security method for protecting sensitive data by replacing it with fictional yet realistic values. Salesforce data masking protects confidential information in non-production environments, preventing unauthorized access while maintaining data usability for testing, development, or analytics. Masked data cannot be reverse-engineered or traced back to the original values without access to the original dataset.
What Are the Two Primary Types of Data Masking?
The two primary types of data masking are:
1. Static Data Masking (SDM)
Static data masking (SDM) is a way to protect sensitive data by permanently replacing it with masked values in a copy of your database. This usually happens in non-production environments, like sandboxes, which are based on your production data. By creating a secure, masked version of the data, SDM lets developers test applications safely without exposing any sensitive information.
2. Dynamic Data Masking (DDM)
Dynamic data masking (DDM) masks data in real-time at the query level, ensuring that only users with proper permissions can access sensitive information, while others see it in a masked form. This allows developers and other users without the required permissions to still use the data for analytics and other business operations, all while keeping customer information secure. It enables organizations to strike a balance between data usability and security.
Other types of data masking include deterministic, substitution, redaction, tokenization, and anonymization. Each method secures sensitive information while maintaining data integrity for different business needs.
Why Is Data Masking Important?
Data masking has multiple benefits. By implementing a secure data masking program, organizations can improve their security posture, reduce the likelihood of data breaches, and protect corporate and employee data from malicious attacks. It is an integral part of an organization's comprehensive data security strategy. Here’s why it’s important:
Enhances Data Security
Data masking protects sensitive data from unauthorized access, as the data is unreadable to those without the necessary permissions.
Supports Safe Development and Testing
Data masking is beneficial in environments where data is accessed frequently, such as development and testing. It allows users to work with realistic data without exposing actual customer information. This process ensures that confidential information such as personally identifiable information (PII) and financial data, remains secure in development, testing, and staging.
Ensures Regulatory Compliance
It helps organizations comply with data regulations like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry Data Security Standard (PCI-DSS), all of which have strict data protection requirements. Data masking obfuscates sensitive customer and employee data, ensuring it remains protected and is not exposed in non-secure environments.
Protects Intellectual Property (IP) and Proprietary Information
Data masking makes it challenging for competitors and malicious actors to access valuable IP—a core value driver and competitive differentiator in industries such as technology and pharmaceuticals. It also shields proprietary information from unauthorized exposure.
Facilitates Secure Third-Party Data Sharing
Data masking facilitates data sharing with third parties such as vendors, partners, contractors, and more. This is especially useful for companies in regulated industries like healthcare and financial services, which regularly share data with external stakeholders as part of their business activities.
Reduces the Impact of Data Breaches
Data masking renders the exposed information useless to malicious actors, even if a system is compromised. It helps organizations save costs by avoiding regulatory fines and paying ransoms to threat actors.
How Does Data Masking Work?
Data masking replaces sensitive data with fictional values while preserving data format and usability. It enables organizations to securely use sensitive data in non-production environments, such as development and testing, without compromising confidentiality. Here’s how data masking works:
Identify Sensitive Data
First, identify sensitive data, such as PII, financial data, healthcare records, and other confidential information, as classified by your organization.
Choose the Right Masking Technique
Select an appropriate data masking technique based on the data type and its intended use. Many techniques are available, such as substitution, encryption, tokenization, and redaction.
Ensure Secure Data Transformation
Apply the selected technique using automated tools or scripts to transform the sensitive data into a masked format.
Usability Testing
Test the usability of the masked data to ensure it is fit for the intended purposes, such as development, testing, or analytics.
By following this structured approach, organizations can protect sensitive data while maintaining its usability for business processes.
Data Masking Techniques
Some of the most common data masking techniques to protect sensitive data are:
Substitution
This technique involves substituting actual data in the database with fictional but realistic values. It’s useful for anonymizing names, addresses, and other PII in datasets while ensuring that the substituted data remains useful for developers. For example, actual names in a Customer Relationship Management system (CRM) can be replaced with alternative names to protect customer identity.
Encryption
This technique uses a mathematical model to scramble data, making it unreadable without a decryption key. This technique ensures data is secure both during storage and transmission. For example, social security numbers can be converted into unreadable ciphertext using algorithms that users can only decipher with the appropriate decryption key.
Tokenization
This method replaces data with unique identifiers or tokens, commonly used for payment processing. These tokens are mapped back to the original data using a tokenization system. For example, bank account numbers can be replaced with tokens in financial records to maintain customer privacy in case of a breach.
Redaction
This involves masking or removing specific parts of data, such as hiding the middle digits of credit card numbers.
Shuffling
This technique randomly rearranges the order of data values within a dataset, obscuring the original relationship between data points while preserving the overall data distribution.
Nulling Out
This method sets sensitive data fields to null or blank values, effectively maintaining the database structure even as the original value is removed.
Use Cases of Data Masking
Data masking helps to prevent sensitive data and enables organizations to maintain security and privacy. This security method is helpful in multiple business scenarios.
Applications Development and Testing
Data masking enables developers to work with realistic test data without exposing sensitive information, reducing security risks in software development and testing.
Regulatory Compliance
Data masking enables you to comply with data regulations like GDPR and HIPAA and protect PII and other sensitive information. It ensures that data is protected from unauthorized access and breaches, enabling organizations to avoid punitive fines and regulatory actions.
Data Analysis and Research
It allows data scientists and analysts to work with large datasets while preserving individual privacy. For example, a health researcher could analyze anonymized patient datasets to identify disease trends without viewing the patient's actual details, thus maintaining privacy while deriving useful business insights.
Data Governance
It ensures data governance by controlling access to sensitive data by dynamically masking it for users with limited permissions.
Third-Party Data Collaborations
Data masking ensures that sensitive information remains protected when it is shared with third parties. Some industries such as healthcare and financial services routinely share data with external organizations as part of business processes, and data masking helps them to maintain data integrity and minimize the risk of data breaches.
Employee Training
Masked data enables organizations to use real-world data and examples for training sessions without exposing customer information.
Challenges in Data Masking
While data masking effectively protects sensitive data, organizations must address several challenges for successful implementation as part of their data security strategy.
Some key data masking challenges include:
Identifying Sensitive Data
Locating sensitive data across large datasets can be time-consuming and resource-intensive, especially without advanced tools. Businesses often struggle to efficiently find and classify confidential information.
Balancing Data Usability with Security
A major challenge is ensuring that masked data remains useful for development, testing, and analysis while still protecting sensitive information. The data should retain its structure and relevance without compromising security.
Maintaining Data Integrity
Masked data must remain consistent and reliable across different systems and environments to maintain its integrity. If data integrity is compromised, the test database may become unusable.
Optimizing Performance
Data masking can be resource-intensive, impacting system performance and requiring financial investments in hardware and software. Data masking solutions must be capable of handling large databases while keeping up with the continuous generation of new records.
Data Masking Best Practices
A systematic approach incorporating best practices is required to successfully leverage data masking for data security while maintaining usability.
Identify and Classify Sensitive Data
A systematic approach to identifying and classifying sensitive data is essential for successfully implementing data masking. Organizations should regularly update their data inventories and use advanced AI-powered tools to locate and classify sensitive data across all systems and store this information in a centralized data catalog.
Choose the Right Masking Technique
Selecting appropriate masking techniques depends on the data structure, sensitivity, and intended use cases. Understanding both the business and technology context is critical, as different scenarios may require different masking approaches.
Deploy Masking Tools Strategically
Implement data masking tools close to the data source to prevent unmasked data from being transferred to less secure environments. Adopt a layered approach by applying data masking across various system layers to ensure data protection at different stages of its lifecycle—from storage to transmission—reducing the risk of exposure.
Conduct Regular Audits and Updates
Regularly auditing and updating data masking strategies helps you stay ahead of evolving security threats and regulatory requirements. Masking techniques should be continuously assessed and refined to remain effective for new data types.
Host Regular Training Programs
Conduct regular training programs to ensure employees know the importance of data masking, follow best practices to protect data, and contribute to a strong security culture within the organization.
How To Choose the Right Data Masking Tools and Software
You must diligently follow a systematic approach to select the data masking tool that best fits your business and technical requirements. You must carefully consider multiple evaluation factors, such as the types of data being masked, the complexity of data structures, regulatory requirements, and performance considerations. Some key evaluation criteria for choosing the correct data masking tools and software are as follows:
1. Masking Techniques
Ensure your chosen tool supports various masking techniques such as substitution, shuffling, and encryption to accommodate different data types and privacy requirements.
2. Data Discovery
Select tools with advanced data discovery features to enable you to identify sensitive data across complex databases.
3. Flexibility and Customization
Your tool should allow you to specify and apply different masking rules based on your organization's unique sensitive data needs.
4. Scalability and Performance
You should select a tool that can efficiently handle large datasets with minimal impact on system performance and that scales as your data volume grows.
5. Support for Different Data Sources
Ensure your tool seamlessly integrates with your existing infrastructure and supports various data sources. It must maintain data consistency and integrity throughout the masking process, preserving referential integrity across databases.
The right data masking tool will strengthen your organization's data security posture.
How To Deploy Masked Data with Flosum
Flosum’s comprehensive end-to-end DevSecOps platform built 100% native on Salesforce offers a suite of solutions, including Data Migrator and Trust Center. Data Migrator enables developers to securely, reliably, and effortlessly transfer data between Salesforce orgs while safeguarding sensitive information through data masking. Flosum’s Trust Center solution lets you create a masking template that automates the anonymization of sensitive customer data at rest in Salesforce environments, on every refresh.
How It Works
Data Migrator is installed in the source org where the data is coming from, not the target org. It moves data directly from one to the other, making the whole process secure and smooth, with no need for extra systems in between.
Key Use Cases of Flosum Data Migrator
- Data Seeding (Sandbox Seeding): Sandbox seeding populates a Salesforce sandbox environment with data, typically from a production org or another sandbox. This is essential for creating realistic testing environments where developers and administrators can safely test configurations without affecting the live production environment. Flosum’s Data Migrator allows you to push data from production to sandboxes, enabling more effective testing and troubleshooting.
- Record-Based Configuration Migrations: If you’re working with applications like CPQ or nCino, Data Migrator can help migrate configurations from sandbox to production. This allows testing in the sandbox before moving configurations to higher environments.
Flosum's Native Data Migrator makes data migration effortless, enabling developers to move entire datasets or subsets in minutes, all while maintaining parent-child relationships.
Steps to Deploy Masked Data with Flosum
- Install Flosum Data Migrator: Begin by installing the Flosum Data Migrator in your Salesforce organization. This tool is available on the Salesforce AppExchange and is recognized for its high user satisfaction ratings.
- Select Data for Migration: Identify the data sets you wish to migrate. This can include specific objects and fields from your production environment that you want to move to a sandbox or another org.
- Mask Sensitive Data: Simply double-click on the sensitive fields you want to mask during the data migration process to anonymize them.
- Configure Migration Settings: Set up the migration process by defining the source and target environments. You can choose to migrate data directly or use overlay steps and filters to customize the process. By using overlay steps, users can tailor the data migration process to their specific needs, ensuring that complex data relationships are preserved across different Salesforce environments.
- Execute Migration: Run the migration process. Flosum Data Migrator will handle the transfer of data while maintaining relationships and ensuring that sensitive information remains masked.
- Verify Results: After migration, verify that the data has been successfully transferred and masked as intended. This step ensures that your testing environment is properly set up for development and testing purposes.
Frequently Asked Questions (FAQ)
What Is Salesforce Data Masking?
Salesforce data masking is a security process for protecting sensitive data by replacing it with fictional yet realistic values using one of the many masking techniques. It ensures developers can work with realistic data in a non-production environment while protecting the confidentiality of sensitive data.
What Is Data Masking with an Example?
Data masking replaces sensitive information with masked values to protect privacy while retaining usability. For example, certain digits of your credit card number can be replaced with a generic placeholder like "--****-1234," enabling developers to test systems without exposing your sensitive financial information.
Is the Salesforce Data Mask Free?
Salesforce Data Mask is not free, and the available information indicates it can cost businesses around 15% of the average order value (AOV). If you want accurate pricing, you should contact a Salesforce sales expert.
How Do I Set Masking Rules in Salesforce?
You must open the Data Mask app, navigate to the Configurations tab and Select "New" to create a masking configuration. Then choose standard and custom objects containing sensitive data to apply masking rules. Subsequently, specify masking rules for each field, such as replacing with random characters or using predefined libraries, and save your configuration. Finally, execute the masking job to apply the rules to your sandbox data.
Interested in learning more about how Flosum can help you streamline your Salesforce DevOps processes? Connect with an expert for a demo today!
