What is Data Masking? Types, Techniques, Uses & Best Practices
Every company must monitor security and data privacy regulations when using or sharing data. Failure to mask sensitive information can lead to legal violations, penalties, and loss of trust. That’s why businesses invest in data masking technology that obscures real data in their datasets.
The challenge is to maintain the utility of data after masking. Datasets must retain referential integrity and relationships to be useful for software testing, analytics, and research. Ensuring this balance between privacy and usability for essential business processes can be tricky. Fortunately, we can share strategies to address this.
In the article below, you will learn about common data masking techniques, types, and use cases. We will also describe the best practices that can help companies ensure compliance at scale. But let’s begin with the definition of data masking.
Table of Contents
What is Data Masking? Meaning & Definition
Data masking is a process that replaces personally identifiable information (PII) in datasets with randomized information. The primary aim of data masking, also known as data sanitization, is to protect the sensitive data of individuals and businesses.
Suppose your marketing team is preparing a financial report. To comply with the law, you must replace customers’ names, dates of birth, and SSNs with random numbers. Data masking technology can protect this data while preserving the format and the relationships between tables in the original file.
The original data is altered through various data shuffling, manipulation, and encryption techniques. It can happen at different stages of data processing: in the source database, during data transfer, or at the memory level. The data masking process usually goes like this:
- The process begins by locating personally identifiable information and other sensitive data in a dataset. It involves classifying and tagging specific data elements, such as names, addresses, and financial information.
- This data is transformed through various masking algorithms and techniques. Masking rules should be consistent to maintain data integrity and reliability across the dataset.
- The altered data is tested for effectiveness. Masked data must provide the appropriate security level, and query results should be comparable to those from the original data.
While the overall goal is clear, companies use data masking methods for various purposes.
Why Data Masking Is Used By Companies
Companies implement data masking to comply with data privacy laws. These laws manage the security and privacy mechanisms companies must put in place to use, store, and share sensitive data.
The regulated data includes personally identifiable information (PII) and protected health information (PHI). PII refers to any data that identifies an individual, such as name, address, and SSN. PHI is a subset of PII and includes medical records, health insurance information, and any data related to an individual’s treatment.
Nearly all regulations are based on these key laws:
- General Data Protection Regulation (GDPR) in Europe and UK-GDPR in the UK
- Health Insurance Portability and Accountability Act (HIPAA) for healthcare organizations in the US
- Payment Card Industry Data Security Standard (PCI DSS) for businesses that handle credit card information
- California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA)
Masking techniques help organizations comply with these regulations by eliminating all direct and indirect identifiers. After masking, the datasets become de-identified (anonymized) and thus excluded from data privacy laws.
Data masking also helps protect sensitive data from unauthorized access. Given the rising cost of data breaches across industries, as investigated by IBM, companies must make every effort to mitigate the damage. By concealing the PII, you prevent the risk of leaks in case cybercriminals breach your databases.
In addition, masking enables secure data sharing. Companies can run tests, do research, or collaborate with other businesses using masked data without compromising data privacy.
Businesses introduce data masking processes to secure data storage. These processes are usually applied to cloud environments or large repositories with archived data.
Finally, data masking helps build trust with customers and stakeholders. Proactive data protection measures demonstrate a strong commitment to privacy and security, setting a company apart from its competitors and serving as a key retention factor.
At the same time, data must remain usable. One factor that makes data masking important is the ability to use process datasets in a non-production environment. However, not all masking types and techniques can preserve the original quality of data or guarantee top efficiency.
Types of Data Masking
Types of masking depend on the overall approach and context. These are the most common types of masking in typical application scenarios:
Static data masking
Static data masking applies rules to transform sensitive information in a dataset. The masking rules are pre-defined, ensuring consistent application across multiple environments. The real data is changed irreversibly, so you must first make sure you won’t need the original information.
As the name suggests, this type is best used for files that remain static over time. Statistic data masking helps create anonymized datasets for user training, analytics, or archival purposes.
Dynamic data masking
Dynamic data masking modifies sensitive data as users query or access it in real time without altering the original information in the database. To implement it, you must configure role-based access rules that specify which data elements to mask and under what conditions.
Companies use dynamic data masking in live production environments. One example might be when customer service reps need to access customer records without viewing payment information.
Statistical obfuscation
In statistical data obfuscation, the PII is modified to create statistical representations. The processed data keeps the original properties and relationships within the data while obscuring the sensitive data.
With statistical obfuscation, companies can do in-depth analysis without compromising data security or privacy. Techniques used for this type of data masking include shuffling, substitution, and generalization.
Deterministic data masking
Deterministic masking consistently replaces specific values with identical artificial values. For example, a user named “Jane Doe” will always be renamed “Jane Smith.”
This type of data masking usually involves substitution and tokenization. It maintains data relationships and referential integrity across columns and files but greatly increases privacy risks. Malicious actors might uncover the information if they discover consistent patterns or mapping rules for the original data.
On-the-fly data masking
On-the-fly masking happens in memory during transit and real-time access. Information is masked with the Extract-Transform-Load (ETL) process. It’s read from the source database, obfuscated, and then inserted into a new table in the target database. The source data remains unaltered.
This data masking type protects sensitive data in integration or continuous deployment (CD) scenarios, such as DevOps pipelines. The tool can mask the PII at a required stage of the development life cycle and pass it to the next stage.
The next key stage is choosing an appropriate masking data method that fits your application scenario.
Popular Data Masking Techniques
Types refer to general categories, whereas techniques are specific methods and algorithms used to modify sensitive information. The most popular methods include:
Data encryption
Encryption transforms textual data into an unreadable format using algorithm keys. Only the correct decryption key owner can convert the encrypted data back to its original form. Typically, companies use AES (Advanced Encryption Standard) to protect data in transit and RSA (Rivest-Shamir-Adleman) to secure digital signatures.
This is a baseline technique used by most data masking tools. However, encryption can introduce performance overhead as it requires computational power. It can downgrade your system’s performance when dealing with large datasets or real-time data processing.
Substitution
Substitution replaces sensitive elements with fictitious values that retain realistic qualities and usability. It supports various data types and keeps the original format. For example, this might be replacing real names or social security numbers with random ones.
As for the downside, substitution can introduce identifiable patterns that might expose it to re-identification attacks. What’s more, outdated tools can lose some context and relationships, affecting data during testing.
Shuffling
Data shuffling text reorders the data in columns and datasets while keeping the actual values. It works especially well in scenarios where you want to preserve data consistency for analytical purposes, like obfuscating the sequence of transaction records while retaining the original values.
A challenge is to ensure that the shuffling doesn’t introduce unintended biases or patterns that could make the data useless.
Date aging
Aging involves altering only the dates in the datasets to protect PII. The key advantage of date aging is that it maintains the chronological integrity of the data. This allows you to run compliant time-series analyses and identify trends.
When it comes to risks, aging can affect usability for certain types of analytics. For example, aged dates might not align with specific real-world events or external data sources.
Generalization (binning)
Generalization groups data into broader categories to obscure specific values. For instance, individual ages might be converted to age ranges: 25 years becomes 20-30 years or “in their 20s.”
This is one of the most widely used techniques of data masking for analysis because it retains the utility of datasets. However, overgeneralization can make the information too vague for specific research purposes.
Masking out
Masking out involves scrambling parts of sensitive values with random or masked characters. For example, it can replace all digits of a credit card number except the last four. It’s particularly useful for applications where partial data, such as customer service interfaces or receipt generation, must be visible.
However, this isn’t a comprehensive data masking solution. Since it protects only parts of data, fraudsters might combine it with external data to identify individuals.
Nulling (blanking)
Nullifying replaces the data with a null value or placeholder. For example, a customer’s email address is replaced in a table with “N/A.” This technique helps comply with data security laws, as it completely removes sensitive information.
While easy to implement, nulling won’t work for meaningful analysis where relationships between data points matter.
Scrambling (hashing)
Data scrambling rearranges the characters within a string to hide the original values. This method maintains the same length and character set but changes the order. For example, the string 1ABCD2 might be scrambled to DAB21C.
Scrambling helps protect passwords, account numbers, or other identifiers in production data and non-production environments. However, it only obfuscates data at the string level and doesn’t address other data types. Even worse, some data masking tools might still keep the original value discernable from the scrambled data.
Hashing
Hashing transforms given data or a string of characters into a fixed-length value (hash). It uses algorithms to produce unique hash values for different inputs that can not be reversed-engineered.
This method is used to set up tables that store keys and value pairs accessible through the index. This allows you to quickly retrieve data when you need to read the original values.
Tokenization
Tokenization replaces production data with randomly generated tokens referencing the original data stored in a secure token vault. For example, a credit card number might be replaced with a token like T12345.
With tokenization, businesses can process payments without directly accessing sensitive data. As for the challenges, tokenization can introduce overhead in environments with high transaction volumes. You must also implement strong security measures for the token vault that maps tokens to original data.
Some techniques are more effective than others, and not all of them preserve uniqueness, attributes, and relationships. Companies must know which technique to use for each data type to ensure compliance.
Data Masking: Best Practices for Compliance at Scale
The growing volume of data makes it difficult to apply masking at scale. Organizations can use these practices to comply with regulations without overwhelming their employees.
- Identify data that require masking. Find sensitive data across locations, databases, tables, and columns. Natural language processing (NLP) and optical character recognition (OCR) can help detect and mask sensitive content within images, PDFs, XMLs, and other unstructured data.
- Implement consistent rules. Introduce a data governance framework with consistent rules across environments. This includes applying appropriate data masking techniques based on the type of data and its intended use. For example, substitution might work best for test datasets, while data encryption is the go-to method for archived files.
- Secure access to masked data. Only authorized personnel must be able to access original data with sensitive information. Implement role-based access controls to restrict access to PII based on job roles and responsibilities to minimize the risk of unauthorized access.
- Integrate with data management processes. You can automate data masking for your entire data lifecycle. This will give you an extra level of security if data is obfuscated for integration, ETL, and collaborative sharing.
- Offer training and awareness programs. Run training sessions on masking, de-identification, and anonymization. Make sure your staff is well-aware of privacy regulations and security policies.
- Regularly re-assess the effectiveness. Test the results of masking techniques to ensure they provide the right level of privacy and usability. It’s best to compare masking methods for different types of data to gauge how the masking affected the quality of the original data.
- Use automated tools to avoid manual work. Manual data masking is often time- and resource-intensive. Moreover, it carries the risk of human error. To automate the process and minimize errors, consider investing in tools with AI-powered PII scanners.
Your organization might not require all the techniques and practices we described. Understanding which ones to actually apply in real-world scenarios and how to do that is just as important.
Data Masking Technology Use Cases
Data masking can mitigate risks and support multiple data management strategies. You can integrate data masking techniques into various business processes, including:
- Development and testing. Data masking allows developers and QAs to work with realistic datasets without compromising sensitive information. Techniques like substitution, shuffling, and encryption keep data usable and protect privacy.
- Collaboration with third parties. Data masking allows organizations to share data for in-depth analysis and research. Companies can collaborate without the risk of violating privacy laws.
- Healthcare research. Healthcare providers can mask patient data before using it for research purposes. This ensures compliance with GDPR, HIPAA, and other local regulations during clinical studies.
- Data monetization. Companies can sell valuable de-identified data to other organizations for testing, research, and algorithmic training.
- Improved data security. By obscuring the sensitive data, data masking techniques reduce the attack surface for cyber threats. This can drastically limit the damage from data breaches and prevent leaks of PII.
- Disaster recovery. Quick recovery is essential to business continuity, but backup data often contains PII. Data masking ensures that sensitive data remains protected even if unauthorized parties access backup data.
Masking not only ensures compliance but also offers numerous benefits for your business. With advanced tools, data masking processes can be largely automated.
Automated Data Masking with Syntho
Effective data masking protects sensitive information and supports testing, analytics, and research. It also helps build customer trust, secure production data sharing, and enhance data security.
Manual data masking is inefficient and prone to human errors. It takes too much time and might result in incomplete masking or useless data. In contrast, smart masking technology ensures consistent PII protection and compliance.
Syntho offers automated data masking solutions to protect sensitive information across all data sources. Try our demo to see how it can help you achieve compliance without compromising quality.
About the author
Chief Product Officer & Co-founder
Marijn has an academic background in computing science, industrial engineering, and finance, and has since then excelled in roles across software product development, data analytics, and cyber security. Marijn is now acting as founder and Chief Product Officer (CPO) at Syntho, driving innovation and strategic vision at the forefront of technology.
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