As Data continues to be a critical asset for businesses and organizations of all sizes and industries, they thrive on Data including sensitive data for decision-making processes, strategic planning, understanding customer behaviour, etc. Thus, the amount of sensitive data being collected and stored has grown. While businesses maintain endpoint controls and continuous monitoring solutions across the system but often miss the importance of data security.
Earlier, most security-conscious organizations relied on in-house tools and methods to protect their data from any data breach or attacks, but the development and advancements of data-protecting technologies have made data protection easier and cheaper than maintaining and updating internally developed data protection tools.
Gartner, a leading research and advisory company, has recognized the importance of data masking as a crucial technology in enterprise and government security portfolios. Gartner analysts have highlighted that data masking tool is valuable for protecting data from insiders and outsiders. They have recommended its implementation as a “must-have” technology for organizations looking to strengthen their data security.
Data Masking Market Size, Growth and Trends
Data Masking Statistics
|Current Market Size (2023)||US $801 Million|
|Market size in 2028 (expected)||US $1274.8 Million|
|Average cost of a data breach (2022)||$4.35 Million|
|Annual Growth rate||11.54%|
|Key players||K2view, Microsoft and Oracle|
Types of Data that Need Protection
Data privacy or anonymization is typically applied to personal health information (PHI) and personally identifiable information (PII), including sensitive information enterprises, handling of customers, shareholders, or employees.
Some of the common sensitive data types are:
- PII: Personally Identifiable Information, like social security or ID numbers, contact numbers
- PHI: Protected Health Information, like medical records
- PCI-DSS: Payment Card Industry Data Security Standard
- ITAR: Intellectual Property Information
Data protection is critical for enterprises to prevent any kind of data leakages, data loss, and data breaches. By protecting sensitive information, businesses can comply with data security standards such as General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), etc. and avoid legal consequences like hefty fines, legal disputes, and damage to the reputation of the organization. It also helps enterprises obtain a competitive advantage in the marketplace as they are more likely to win the trust of customers and partners and attract and retain top talents.
Techniques like Data Masking, Tokenization, and anonymization are fundamental aspects of data protection. As per the GDPR’s Recital 26, a dataset is anonymous when individuals cannot be identified directly or indirectly.
The process of data protection can be achieved with several techniques, ranging from masking or encoding or substituting a certain portion of data to completely hiding a data set. Techniques like data anonymization involve altering sensitive information in a dataset that cannot be traced back or decoded to its original form for any business purpose and compromises data quality and usability. Thus, this data protection technique is typically used in situations where the privacy of individuals must be protected while data is shared or released for research, analysis, etc.
However, data masking is generally considered to be a more effective method than data anonymization for enterprises as it offers the flexibility of selectively hiding or masking certain fields or values in a dataset while still maintaining the overall format and integrity of the original data. For instance, a credit card number is masked by replacing all the last four digits with asterisks or Xs, allowing the data to be used for testing, development, or other purposes while being protected from any potential data breach. The flexibility in data masking makes it more useful for preparing data to be used for testing, development, or other purposes where authorized access is intended for sensitive data. The chances of accidental disclosure of sensitive information are very bleak with data masking.
Data Masking vs Data Tokenization vs Data Anonymization
Data Masking Features and Comparison
Need for Data Masking
The growing awareness among people about the importance of data privacy and security has led to the development of new data security standards. Globally, new and strict data privacy laws and regulations are introduced, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States. These laws require enterprises to comply and take appropriate measures to protect personal and sensitive data and ensure that individuals have greater control over their personal information.
Thus, enterprises must use sensitive data for testing, customer analysis, or outsourcing to third-party vendors. Therefore, data masking helps businesses that require sensitive information by creating realistic-looking test data that can be used and shared without compromising the security of the original data. By definition, Data masking is a method to obscure or anonymise sensitive or confidential data so that it cannot be accessed or read by unauthorized individuals or systems while creating an alternate version of the sensitive data type for legitimate access. This alternate or masked data is functional and retains the original type of data format so that it can be leveraged for various purposes or any intended purposes.
Types of Data Masking
Here are the types of data masking leveraged for masking sensitive information in different business use cases.
Static Data Masking
This is the simplest type of data masking where sensitive data is obscured or replaced with a fixed value while maintaining relevant attributes and characteristics of data. For example, security card numbers or credit card numbers are masked with a series of “*” or “X.” Additionally, the altered or masked version of sensitive data that can be forwarded to the intended environment or even third parties. This type of data masking protects the data at the actual database layer.
Dynamic Data Masking (DDM)
In Dynamic data masking, the sensitive data is masked in real-time, such as production environment or business-critical stages to prevent o prevent unauthorized user access. Thus, limiting the sensitive data visibility only authorized users, while non-sensitive data remains visible to all. This approach is commonly used in cloud-based applications. The authentication levels and associated masking rules are accessible on demand for authorized business processes. DDM is extremely useful in organizations that engage in continuous deployment of software or have databases that are integrated with diverse systems and thus, cannot afford to mask and store sensitive information during each data flow between the systems.
On-the-Fly Data Masking
On-the-Fly Data Masking is considered close to DDM, as the sensitive data is masked on the fly or in transition from one production environment to another. To consider the difference between the two types of data masking, in the On-the-Fly Data Masking type, the data masked during the transition is not saved or stored in the target environment, whereas, in the DDM type, the masked data can be stored in the development environment, for example, for other business purposes.
Deterministic Data Masking
In the Deterministic data masking type, the sensitive information with a masked version retains the original data’s structure and format, but without revealing the original data. This prevents any compromise on security. Additionally, this type of data involves mapping the same type of data set to ensure that the same data is masked or replaced with a same value consistently. It is used in software development and testing environments, where it is necessary to use real-world data without exposing sensitive information such as sensitive data such as social security numbers, credit card numbers, or other personally identifiable information (PII).
Thus, for instance, any data set that spans across records in a database, in deterministic data masking, the similar data set will be masked with similar values.
Data Masking Techniques
The term Pseudonymization was introduced in the EU General Data Protection Regulation (GDPR) as a process to secure personal by replacing identifiable information with pseudonyms or codes. This technique includes hashing, encryption and shuffling.
- Hashing involves converting a data string of any length into a fixed-length string known as a hash value or message digest using a mathematical algorithm. This output represented as a string of characters is unique to the input data. For example, a password or credit card number as the input data is hashed and the resulting hash value is stored in a database or file. When the user attempts to log in or make a transaction, the entered data is hashed again and compared to the stored hash value. If the two values match the user is granted access or the transaction is processed.
- Shuffling involves randomly reordering the values in a column while maintaining the same distribution of values. This technique retains the pattern and distribution of data in large datasets while masking sensitive information. For example, shuffling the names column in a dataset containing employee salaries would allow analysts to maintain accurate data patterns and distributions while protecting employee privacy. Similarly, shuffling the names column across multiple records in a dataset containing patient names would allow healthcare providers to maintain accurate patient data for analysis while protecting patient privacy. It is usually used for large datasets, as it allows organizations to maintain the accuracy and usefulness of their data for analysis and modeling purposes while still protecting sensitive information.
Data Encryption is a widely used technique for securing data with an option to restore its original value when required. It involves using an algorithm to convert plain text into cipher text, which can only be decrypted and restored to its original form using a specific key.
Encryption is essential in protecting sensitive information from unauthorized access or interception during network transmission. Different encryption algorithms are available, and the choice of algorithm depends on the level of security required, the application, and other factors. Some commonly used encryption algorithms include Advanced Encryption Standard (AES), Data Encryption Standard (DES), and RSA.
The Data Scrambling technique involves randomly replacing or rearranging the original data with characters or numbers. The purpose of data scrambling is to make the original data unreadable and unusable to unauthorized users, while still maintaining the data’s basic structure and format.
Data scrambling is a relatively simple technique to use and can be effective in certain situations. For example, it may be useful for scrambling data such as names, addresses, and phone numbers to protect personal identifying information (PII) from being accessed by unauthorized users.
The Nulling Out technique masks or conceals sensitive data by assigning a null value and, thus, protecting data from unauthorized usage. In this technique, the null value in place of original information changes the data’s characteristics and affects the data’s usefulness. The method of removing or replacing data with a null value removes its usefulness, making it unfit for test or development environments. Data integration becomes a challenge with data, which is replaced with empty or null values.
Data redaction (blacklining)
The data redaction technique, also known as blacklining, hides data with generic numbers while removing the attributes of the original data’s properties. When sensitive data in its whole and original state is not necessary for development or testing, this technique can be used, which is comparable to nulling out. For instance, replacing credit card numbers displayed on payment sites in the online environment with x’s (xxxx xxxx xxxx 1234) helps to prevent any data leakage and at the same time, the replacement of digits by x helps developers understand how the data might look like in real-time.
The Substitution approach works with a range of data types and maintains the original structure of the data. The meaning of data is altered by replacing it with a different value. For instance, changing the initial name “X” to “Y” in the customer records preserves the data’s structure and makes it look to be an acceptable data input while also protecting against the unintentional disclosure of the true values.
Best Practices for Implementing Data Masking
Let’s see some of the important best practices for data masking.
Identify the data:
Substantial time should be spent to identify the most sensitive data points, access to authorized people who can view those data points, and their intended usage. The types of data that need protection should be discussed and categorized within an enterprise.
Choose and validate Masking technique
There are several masking techniques for data protection available in the market; enterprises should choose the one that works best for the intended type of data and ensure that the same technique can be applied consistently across the database and enterprise. Once the technique is finalized, it is important to test and validate the effectiveness of the masking process and the accuracy of the masked data. The access to the appropriate data of the authorized users should also be checked.
Secure Masking Technique
The security of masked data produced by the masking technique is as critical as the original data being masked. For instance, data masking techniques, like, substitution, rely on algorithms and lookup tables to transform sensitive data into a different form while preserving its usability for authorized users. However, if malicious actors breach these algorithms and lookup tables, the original sensitive data can be leaked to unauthorized users.
Data Masking with various platforms
Best Enterprise Data Masking Tools
K2view Data Masking Best Value
Microsoft Azure Best Values
Oracle Data Masking and Subsetting Most Popular
K2View Data Masking
K2View offers data protection by masking all the data of a particular business entity, including clients, orders, credit card details, etc., and controls the integration and transmission of the encrypted Micro-Data of each business entity. The platform uses dynamic data masking techniques to modify, disguise, or deny access to sensitive data based on the user’s authorized rights.
Their graphical Data Transformation and Orchestration tool uses K2View’s in-flight data masking to avoid having to mask large volumes of data fully. Instead, it integrates and masks data when a quick transition from source systems (production) into target applications is necessary. Additionally, it can protect unstructured data by using a combination of data masking and integration with various source systems and target applications. This ensures that sensitive data is secured regardless of its format. The platform utilizes SHA-266 and SHA-512 encryption algorithms to protect sensitive data and manage accessibility. This way the data is securely masked and cannot be accessed by unauthorized users.
Microsoft Azure offers native data masking techniques like using d and various pre-defined data protection and security services, like auto data discovery and classification of sensitive data in databases and seamless integration with third-party databases. These services help to mask data across various data sources and formats.
It offers a masking solution that can be used for data both statically and dynamically i.e., permanent data masking in the database or on-the-fly data masking of sensitive information as it is accessed or transitioned.
It also provides built-in masking capabilities, such as substitution, randomization, and shuffling, thus allowing custom masking functions to meet specific business requirements. The masking solution also provides Role-based access control (RBAC) to manage roles and access to sensitive information for different users or groups of users.
The platform is a comprehensive and powerful solution for data masking that can help organizations to protect sensitive data and comply with regulatory standards.
Oracle Data Masking and Subsetting
Like other platforms, Oracle Data Masking also offers a comprehensive solution that enables organizations to remove sensitive information from non-production environments while retaining the utility of the data. Oracle Data Masking and Subsetting can be used across various environments such as test, development, and analytics, and supports various masking and subsetting techniques such as conditional masking, randomization, key-based reversible masking, etc., giving leverage to customers to customize these techniques to match their requirements.
Additionally, offers automatic sensitive data discovery and compliance with data privacy standards such as GDPR and PCI DSS. The pre-defined templates for these standards help customers comply with the regulations.
Learn more about Data Masking
Key References and Conclusion
The future of data masking looks promising as people and enterprises with increasing awareness of the importance of data security and privacy. According to a report by MarketsandMarkets, the market size has grown from USD 384.8 Million in 2017 to USD 767.0 Million in 2022, at a Compound Annual Growth Rate (CAGR) of 14.8%. The vulnerability of data to unauthorized exposures is the key factor driving the Data Masking Market.
Additionally, with the adoption of new technologies, such as artificial intelligence (AI), machine learning (ML), and big data analytics, the volume of data required has increased and thus more data is stored on the cloud without compromising its privacy or security. This drives organizations to look for new advanced ways to secure their data and comply with the latest security regulations. The adoption of data masking solutions is growing across various industries, including banking, healthcare, or retail, etc.
Overall, the statistics indicate that the demand for data masking solutions will rise in the coming years as organizations prioritize data security and privacy.