Data Masking vs Encryption: Choosing the Right Approach to Protect Sensitive Data
When managing data processing, protecting sensitive data is a priority, whether for regulatory compliance, maintaining trust, or mitigating data security risks. In this article, we explore data masking vs encryption as two widely used methods for safeguarding information.
Although both techniques secure information, they serve different purposes and are best suited for specific scenarios. We will explore how each method works, highlight their key differences, and guide you on when to choose one over the other. This will help you make informed decisions that strike the right balance between data protection, compliance, and operational needs.
Table of Contents
What is Data Masking?
One of several privacy-enhancing technologies, data masking—also called data sanitization—replaces sensitive information like personally identifiable information (PII) or protected health information (PHI) with realistic but artificial values. This process aids in data privacy management while keeping the data’s functionality for testing, analytics, or development.
Masked data isn’t altered randomly—it retains the structure and integrity of the original data set. When you mask data by substituting specific elements, sensitive information is protected without compromising utility.
Types of data masking
Data masking can take different forms, each suited to specific scenarios. Here’s an overview of the main types:
- Static data masking: Static data masking creates a sanitized version of a database by masking data at rest. This is done by replacing original sensitive data with fictional or scrambled data that looks realistic but is not traceable back to the original information. The original data remains unchanged, but the masked data can be used safely in testing environments or for training purposes without risking the exposure of sensitive information.
- Dynamic data masking: Dynamic masking works in real-time, applying rules to obscure sensitive data as it is accessed. This ensures unauthorized users see only masked data without altering the original source.
- Statistical data obfuscation: This approach modifies data in a way that prevents individual identification but retains its statistical value. It’s often used in analytics or research to ensure privacy without compromising aggregate data insights.
- Deterministic data masking: Deterministic data masking enables specific input values to be consistently replaced with the same masked output. This approach is ideal for scenarios requiring repeatable results across datasets while maintaining anonymity.
- On-the-fly data masking: This approach masks data during the transfer process, ensuring sensitive information is protected before it reaches its destination. It’s especially useful for data migrations or sharing information across systems.
Each of these data masking techniques addresses distinct use cases, offering flexible options to safeguard sensitive information.
Data masking use cases
Data masking minimizes risks and supports secure operations in various scenarios:
- Development and testing: Data masking provides realistic datasets for software development, QA, and testing while protecting sensitive information. Masking data in a test environment allows teams to work with realistic, anonymized data without exposing any personal or confidential details.
- Third-party collaboration: Enables secure data sharing for outsourcing, consulting, and joint projects without privacy breaches.
- Healthcare research: Ensures compliance with privacy laws like HIPAA by masking patient information during research and clinical trials.
- Business intelligence: By masking sensitive data, analysts can use data for trend analysis, reporting, and data-driven decisions without risking privacy violations. This ensures businesses can perform critical analysis while remaining compliant with data protection regulations.
- Data migration and upgrades: Safeguards sensitive data during transfers, preventing exposure or misuse by migration teams.
- Customer data protection: Protects sensitive fields, such as credit card details and credentials, across various customer-facing operations, reducing misuse risks. It plays a vital role in securing unstructured data—like emails, chat logs, and voice recordings—that often contain sensitive information.
- Auditing and compliance: Simplifies external audits by masking confidential data while maintaining regulatory compliance.
Regardless of the use case, manual data masking is time-consuming and error-prone, often resulting in incomplete protection or unusable data. Automating the process with smart masking technology ensures consistent PII protection and regulatory compliance.
At Syntho, we specialize in automated data masking solutions that transform data from sensitive to anonymized without compromising its usability. Explore our demo to see how this simplifies compliance without compromising data quality.
What is Data Encryption?
Data encryption is a type of data obfuscation that converts readable data (plaintext) into unreadable text (ciphertext) using a cryptographic algorithm and key. Only authorized users with the decryption key can access the original data.
Encryption protects data from theft, alteration, or unauthorized access and can be applied to data whether it’s stored on local devices, transmitted across networks, or being processed—whether on-site or via remote cloud servers. Data encryption is often required to meet regulatory standards for organizations in sectors like healthcare, education, finance, and retail.
Types of data encryption
Encryption is an important part of securing sensitive personal data and business information in digital environments. It’s used for everything from online shopping to sending private emails, often without us even realizing it. There are two main types of encryption that help protect our data in these everyday activities:
- Symmetric encryption uses the same encryption key for both encryption and decryption. This approach is highly efficient for encrypting large volumes of production data. However, it relies on the secure sharing of the encryption keys between the sender and the recipient. If the key is compromised, anyone with access to it can decrypt the data, creating a security risk. The most common encryption algorithm used in symmetric encryption is AES (Advanced Encryption Standard), which, along with DES (Data Encryption Standard), is extensively implemented to protect sensitive data.
- Asymmetric encryption, also known as public-key encryption, uses a pair of keys: a public key for encryption and a private key for decryption. This method eliminates the need to share a secret key, offering enhanced security, especially for communication protocols. The public key is available to all, while the private key stays confidential. While slower than symmetric encryption, it’s ideal for email encryption, digital signatures, and SSL/TLS protocols for securing online transactions. Common algorithms include RSA (Rivest-Shamir-Adleman) and ECC (Elliptic Curve Cryptography).
In many systems, both types of encryption are combined. Symmetric encryption handles fast encryption of large sets of real data, while asymmetric encryption ensures the safe exchange of encryption keys, adding an extra layer of security. This hybrid approach helps organizations maintain a persistent data security solution.
Data encryption use cases
Data encryption work and its applications span across industries, supporting privacy, compliance, and secure operations. Here are the most common use cases for encryption:
- Data privacy: Safeguards sensitive information, including financial records and personal details, during storage or transmission.
- Regulatory compliance: Encrypted data meets legal and industry standards like GDPR, HIPAA, and PCI-DSS.
- Cloud security: The global cloud encryption market reached $3.1 billion in 2023, highlighting its critical role in safeguarding from data breaches and unauthorized access, even when storage infrastructure is compromised.
- Secure communication: Protects email and messaging systems to ensure confidentiality during exchanges.
- Data integrity: Helps verify that data remains untampered with, particularly during transfer between systems or networks.
- Financial security: Safeguards credit card information and banking details during e-commerce or online transactions.
- Copyright protection: Prevents unauthorized use of digital content such as music, e-books, and videos.
- IoT device security: Safeguards data transmission and storage across connected devices.
With a better understanding of data encryption and masking definitions and use cases, let’s now look at how they compare in terms of protecting sensitive data.
Data Masking vs Data Encryption: What Is the Difference?
When it comes to protecting sensitive data, encryption and data masking are two commonly used methods. However, they serve different purposes and operate using distinct methods. The table below explores the key differences, helping you choose the right approach for your needs.
| Aspect | Data Masking | Data Encryption |
|---|---|---|
| Purpose | Replaces sensitive data with fictitious but realistic values for non-production environments | Converts data into an unreadable format that requires a key for decryption |
| Use cases | Best for test data management, training, and software development | Ideal for transferring sensitive data or data at rest, such as in databases or cloud storage |
| Security | Limits access by making data unusable or unreadable to unauthorized users | Offers stronger protection by ensuring only authorized users with a decryption key can access the data |
| Impact on functionality | Masked data remains usable for testing and development purposes | May prevent access to encrypted data without the decryption key, potentially affecting workflows |
| Performance | Does not require high computational power, making it faster for non-production data | Requires processing power for encryption and decryption, especially with large datasets |
| Data sensitivity | Protects sensitive data during non-production use without revealing real information | Secures sensitive data in all environments, including transit and storage |
Data Encryption vs Data Masking: Which One to Choose?
For companies needing secure, test-friendly environments, data masking provides the perfect balance between security and performance. It allows teams to perform work in real-world conditions without the risk of exposing sensitive information.
Encryption, on the other hand, is optimal for protecting data during transit and while stored in databases or cloud environments.
Both advanced encryption methods and data masking are vital components of a comprehensive data protection strategy. Depending on the use case, one may be more suitable than the other.
At Syntho, we specialize in automated data masking solutions. Our platform helps you:
- Automatically identify PII: Save time by detecting PII with automation.
- Enhanced privacy protection: Minimize risks by altering or removing PII.
- Maintain data integrity: Preserve relationships and integrity across systems and databases.
Contact us today to see how Syntho can help you protect data and achieve compliance without compromising quality.
CEO & founder
Syntho, the scale-up that is disrupting the data industry with AI-generated synthetic data. Wim Kees has proven with Syntho that he can unlock privacy-sensitive data to make data smarter and faster available so that organizations can realize data-driven innovation. As a result, Wim Kees and Syntho won the prestigious Philips Innovation Award, won the SAS global hackathon in healthcare and life science, and is selected as leading generative AI Scale-Up by NVIDIA.
Fuel innovation, unlock analytical insights, and streamline software development — all while maintaining the highest data privacy and security standards.