Introduction to Data Masking
Data masking is an important technique to develop a structure same to the available one but has an inauthentic update on the company’s information that can be used for multiple reasons like user training and software testing. But the main aim is to save the original data by having an operational substitute for various situations whenever the real data is not necessary. The methods, advantages, applications, and disadvantages are discussed in this article. Though most enterprises have strong security measures to save the production information and the data which is less reliable can be used for operations.
Why do we Need Data Masking?
The issue is addressed if the functions are outsourced, and the enterprise has minimum control over the circumstances. In terms of compliance, now, most enterprises are not ready to expose the actual data. So to address the mentioned issues, the process of data masking is applied. Here the format of the data is not changed; only its values are modified. The data can be changed in multiple ways, such as character shuffling, word substitution, character substitution, encryption.
Irrespective of any methods, the value should be altered in any way, which makes reverse engineering or detection impossible. The vendors of products in data masking are Informatica, IBM, Oracle, Compuware, and dataguise. They have a complete operating database for non-production reasons with no risk to disclose any sensitive data and is a threat to business
How is Data Masking Carried Out?
Compiling of sensitive data components should have the following limitations:
- The output of the data should denote the source data, and representing it is important to use it effectively for testing and development of the data. The masked data should be in irreversible way, and source data should not be created as an original from the masked data. It should be understood what data should not be masked.
- It is not necessary to mask every data element; only the sensitive data should be masked first, and the non-sensitive data are masked if it requires any re-creation of sensitive data. The process of output is repeating, and the same sourcing data is repeatedly masked by the same masking technique to attain the same output. It is necessary to maintain substantial integrity and should ensure that the masked data should be usable again.
- The specific masking methods are used to save the context and format of the data components because they should be consistent, meaningful, repeating, and they should be used effectively.
- Substitution is a process that replaces the number of sensitive data with other meaningful data. For example, the postal code can be randomly substituted from a count of valid postal codes.
- Spacing, nullifying, and masking is substituted with non-meaningful values. The social security value is substituted with some XXX-XX-XX-XX. The encrypted keys offer other examples where the data is easily substituted with space.
- Data variance and numbers include the method to modify every value or date by entering any random values to their real values. It gives reliable obfuscation by maintaining the distribution and range of values. For example, the salary of an employee is entered by 15 to 20 percentages in either upward or downward direction. Similarly, the date of birth can be entered with a span range of 45 days after or before the actual date of birth.
- Encryption on format preservation is an algorithm which gives repeating values to save the original format. The actual value can be extracted by using a suitable decryption technique. The decision of using a suitable algorithm is based on the data security policy of the enterprise. The encryption methods can be narrowed with 128 advanced encryption standard algorithm with a 24-byte triple data encryption algorithm, and to secure the 24-byte key, the secured hashing method is used.
Advantages & Disadvantages of Data Masking
Below are the advantages and disadvantages mentioned:
- The substitution preserves the feel and looks to the existing data, but when it deals with higher dimensional data, it becomes confused and difficult to find the relevant information to substitute.
- The shuffling saves the look of data and effectively handles higher dimensional data, but it is ineffective when managing minimum sized data. The original data is not disturbed, and if the algorithm is not sufficient, it may be turned as unshuffled.
- The data variance and number tool work effectively on the numerical data by maintaining the distribution values but applies only to the numerical values.
- Encryption is an effective method in data masking, but it affects the formatting in the look of the data. It is simple to see the data as encrypted. With enough effort, the encryption can be broken, and any third party can access the data easily.
Application of Data Masking in Various Fields
The critical aspect of protecting the data in any enterprise is that any breach should not occur in data security, and data privacy can lead to the indirect or direct financial loss, which comprises failed reputations that affects customer loyalty. The masking of data becomes an essential requirement in enterprise-level data management. The cautious detection of sensitive data components and the considered measures in masking needs to be in the correct place where the client data is extracted.
Typically, the sensitive data comprises personally identifiable information, general customer details, health records, and other sensitive business transactions and client details which require strong protection. The technology operations and daily business like loading data to test scenarios, data migration to advanced process, and addressing the vendors, third-party business for sampling. It can call for data masking techniques which are standard and reusable across the organization.
Conclusion
The best and effective solution in data masking is to plan and deploy the data masking methods. The data masking process is implied to get a clear layout on the process of dynamic masking and gets a perfect solution for database security. The suite of database security and data sunrise packages are built with static and dynamic data masking requirements.
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