Data masking examples help businesses comprehend the various methods they can use to secure and protect data. Moreover, data masking examples describe the alteration of data to replace the original data with false values. Further, it maintains the format while only the values change to appear more authentic and genuine.
Moreover, according to a study by Mordor Intelligence, “The Data Masking Market was valued at USD 483.90 million in 2020 and is expected to reach USD 1044.93 million by 2026, at a CAGR of 13.69% over the forecast period 2021 – 2026. In this information age, cyber security is very important.”
Data masking aims to conceal sensitive and confidential data accessible in proprietary databases. However, businesses must ensure that masked data should be actionable for training, testing, and development. As a result, companies can adapt to various requirements with data masking examples and accordingly mask the data. Hence, in this article, we will know more about these data masking examples and their benefits.
Understanding Data Masking and its various Examples
Data Masking helps businesses generate a structure of false values to replace the original data. Moreover, the false values are usable for training and testing software. It also secures the original data by offering a functional proxy where there is no requirement for the actual data. Data masking is also referred to as data obfuscation that encrypts, shuffles, and substitutes actual data with false values.
Further, BMC provides the following definition for data masking, “Data masking, also known as data obfuscation, hides the actual data using modified content like characters or numbers. The main objective of data masking is creating an alternate version of data that cannot be easily identifiable or reverse engineered, protecting data classified as sensitive.”
Data Masking also provides substitute values for users to access while securing confidential information. Moreover, it offers businesses and users many techniques to alter data with each of its unique advantages.
Requirements to execute Data Masking
There are specific requirements to ensure the success of data masking solutions. Hence, it guarantees the data to be meaningful after the masking techniques:
- Firstly, the data must maintain its functionality and remain logically applicable.
- Further, the data must endure constant changes to ensure the security of the original data.
- Moreover, obfuscated data must maintain its consistency throughout multiple functions.
Where is Data Masking used?
Here are the following scenarios that describe the best use cases of Data Masking:
- For instance, the business needs to provide access to its data while outsourcing third-party capabilities. As a result, while masking the data, it is pivotal for companies to maintain authenticity with the data. Hence, hackers and mal-practitioners assume they are dealing with the original data when it is covert.
- Moreover, businesses use data masking to reduce human errors that may threaten the security of the data. Therefore, data masking limits the scope for such errors. Companies need to understand that not all operations require actual data. That is to say, and they can depend on false data to train employees and prepare them for real-time scenarios.
What are the general rules of Data Masking?
- Firstly, data masking must be a non-reversible process. That is to say, the false values in the database must not transform back into the original data. Therefore, it may lead to a critical data breach if the data is easily retrievable.
- Further, data masking techniques should avoid altering the nature of the data. In other words, any alterations must stay true to the format set for the data.
- Businesses can also mask non-sensitive data as, in some cases, it may affect the security of the original data. Moreover, companies must understand their requirements and consequences before hiding such data.
- Moreover, businesses must automate the process of data masking as it is a systematic process. Hence, proving that datasets and their requirements change constantly. Therefore, continuity in the data masking process can be time and cost-efficient.
When executing data masking, businesses must use the best practices that offer suitable solutions. Hence, here are the steps companies must adhere to for efficient data masking:
Identifying Confidential Data:
Firstly, businesses must comprehend the data that requires masking and protection. Therefore, companies must use solutions that automate the discovery of confidential data and mask the same.
Further, businesses must employ a data security team to segregate sensitive data and authorize access to the same. Companies can also align access controls on the basis of job roles and titles. Hence, offering selective information for specific employees with various functions.
Data is an essential asset that drives modern business into the digital world. As a result, there is a significant increase in collecting data the enterprises need to process. Therefore, companies must comprehend their requirements and objectives and execute data masking techniques.
Analyzing the Outcomes:
In conclusion, the final step is analyzing the data masking outcomes. Moreover, this step proposes quality assurance and testing to ensure data masking reaches expectations
- Firstly, data masking helps businesses meet the compliance requirements of governing bodies like PCI, HIPAA, etc. Moreover, any sensitive data must undergo security and authorization processes. Therefore, by implementing data masking, businesses can protect information by enabling authorization of the original data to specific audiences.
- Further, data is an important asset that can be used for training, testing, and development purposes. As a result, businesses can ensure employee quality practices by offering employees and users simple data sets. Hence, maintaining a suitable training and testing environment while protecting confidential data.
- Moreover, while data masking replaces values, it maintains the data format. Although businesses often use third-party solutions or share the data for examining. Therefore, with data masking, businesses can depersonalize certain sensitive information and gain practical insights from associates.
- Data masking also maintains a specific structure and format, which helps during developments and testing. Hence, it offers data without compromising its integrity and helps gain insights.
- Most Important Data Masking offers solutions against the following threats:
- Data branches,
- Data loss,
- Account hacking,
- Insecure Interfaces,
- Malicious attempts by data users.
- Firstly, businesses must define and identify data that require masking. Moreover, the practice includes identifying sensitive information, authorizations, and their functions in business operations. Further, each data component need not use data masking. However, detecting the sensitive data in productive and non-productive activities can structure the usage and its significance.
- Moreover, businesses may use multiple techniques to mask various sets of data. Further, enterprises select the data masking techniques according to their requirements and compliances. In some cases, businesses also tend to develop strategies to fit their unique needs.
- Businesses must also ensure that data masking is a continuous process as the data evolves with business operations. Moreover, companies must avoid data masking from the ground up. Therefore, automating data masking can offer faster solutions and implement changes to sensitive data in real-time.
Data Masking Examples in Testing and Developing Data Management:
One of the top examples of data masking is in testing and development. Moreover, businesses often use data masking to test and develop their data for various processes. Further, data masking helps companies learn more about confidential information and ensure security for different data sets. Companies also use test results to segregate the data into private, public, or development requirements. As a result, this helps firms prepare for strategic data analysis. Hence, it ensures high security for datasets by following the lifecycle and allocating, transferring, monitoring, and masking confidential data.
Examples of Data Masking in Compliance:
Businesses often follow a compliance or data governance policy to maintain data integrity. Moreover, with the emergence of various technologies, there is a significant in producing and creating data. Therefore, businesses must protect their data across multiple segments. As a result, they are governing bodies like PCI, NERC, GLBA, GDPR, FERPA, HIPAA, etc., leverage data masking benefits to fulfill various requirements. Hence, mitigating malpractices in hacking sensitive data and maintaining compliance regulations.
Data Masking Examples in Securing Production and Strategic Database:
Data is an essential asset in businesses that helps make strategies. Moreover, as businesses evolve, they develop their products, services, and solutions. Although with the increase in competition, companies must offer newer solutions to retain their customers. Therefore, they use sensitive data to prepare strategies and develop the products to introduce them to their audiences. However, while incorporating such changes in their operations, businesses must do so discretely. Hence, with data masking techniques, companies can avoid any breaches and effectively manage the utilization of their data.
In conclusion, Data masking examples enable businesses to use data efficiently while not compromising data integrity. It also helps companies avoid discrepancies while outsourcing specific requirements with third-party resources. Hence, data masking examples describe how businesses maintain the original data structure and format while processing non-operational functions.