It is essential to protect confidential information, such as financial records, employee personally identifiable information (PII), or consumer information, both to safeguard it from hackers and to comply with an ever-increasing array of data privacy regulations worldwide. An extra layer of data security is cost-effectively introduced by dynamic data masking. With dynamic data masking, organizations may provide the required level of access to data to approved users without having to modify a single line of code or database.
Introduction to Dynamic Data Masking
Dynamic data masking is the method of uniquely masking, scrambling, covering, auditing, or blocking data access at the individual user level. The solution screens them in real-time as application requests pass through the data masking layer and masks sensitive data based on user position, duty, and other IT-defined laws. Row-level or column-level protection may also be implemented and limit the number of rows returned in response to a query. In this way, dynamic data masking ensures that company users, external users, part-time staff, business associates, IT teams, and outsourced consultants are able to access sensitive data in the exact quantity and degree of security as required to do their job, thus limiting unnecessary exposure of sensitive data.
Dynamic Data Masking: The Need
Data privacy regulations such as PCI-DSS, CCPA, GDPR, HIPAA, SOX, and others came into being in response to a growing threat: disclosure and theft of confidential information. Such regulations require enterprises to restrict access to data depending on the business function of the user.
Dynamic Data Masking: Best Practices
In the following step-by-step process, the best practices will allow an organization to install, test, and deploy a data masking solution easily:
1. Based on the protection needed, classify data into three categories:
- Extremely sensitive data (credit cards info, last names, addresses, account numbers, passport numbers, social security numbers, etc.)
- Moderately sensitive data (financial reports, first names, birth dates, etc.)
- Non-sensitive information
2. Identify programs that use data that is private. An applicant for a data masking initiative is any application that contains personally identifiable information (PII). Prioritize apps with the greatest number of confidential data and the highest number of users.
3. Defining scenarios for approval. How will the implementation of your data masking decide what needs to be masked and for whom? You will choose the applications, reports, and batch processes that need to be encrypted in this stage. The fields that should be masked, the data masking criteria, and the processes that need to be configured.
4. Map the data through the logging and auditing mode of the data masking solution by running the scenarios.
5. Create masking rules and test them inappropriate applications and resources to check that they function on all related screens.
6. Functionality test. If it affects other application functions or destroys referential integrity, the data masking implementation is useless. Consider restricting the number of masks for each application in order to protect output.
7. Audit the process. The ability to control who has accessed masked data and when is necessary for compliance purposes.
8. To improve data security throughout the enterprise, extend data masking to all business verticals.
Data security and productivity should go hand in hand in today’s hypercompetitive marketplace. Organizations can easily scale up to secure confidential and private information in real-time with dynamic data masking, without connecting IT to expensive, time-consuming program and database adjustments that can affect efficiency and without interfering with the ability of workers to fulfill their responsibilities. Several companies are providing dynamic data masking solutions. Organizations should invest in and deploy an excellent dynamic data masking solution to enhance their data security posture.