Data Masking Methods for Data-Centric Security

Enterprises have data as the most valuable asset and act as an input for business analytics. As enterprises learned about the potential of data, they focused more on collecting large volumes of data and further transitioning it to cloud storage, etc. This transition and collection of large volumes of sensitive data created large swaths of security vulnerabilities – with average data breaches costing companies $4.24 million by 2021. This hefty price brings a strong push in businesses to adopt data security solutions such as Data Masking that protects data from any external or unauthorized intrusion.

With the data-driven style of business, the chances of data leaks also increase, further making implementing data security protocols/methods becoming a priority for businesses. The adoption of assured security methods like data masking brings confidence and increases reliance on the company. Data masking methods secure sensitive data by creating a dummy substitution of this data for database teams without compromising security.

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Types of data masking

With the Data Masking method, businesses can mask sensitive data in many ways. Depending on business requirements, one can select the type of data masking. Below are the various types of Data Masking:

Static data masking (SDM)

This type of data masking helps create a sanitized version of production data (fully or partially masked data set), later utilized or sent in different environments, such as testing, development, or training. With the SDM, within an organization, sensitive data can be passed to downstream teams or even third parties, where there is a risk of any actual data leakage. Thus, the SDM type provides the final output as an altered or masked version of sensitive data that can be forwarded to the intended environment.

Dynamic data masking (DDM)

Dynamic data masking (DDM) type is more commonly used to conceal or mask real-time data – data sets within business processes are altered depending upon the required access or authentication required for particular processes. Unlike SDM, in dynamic masking, no physical changes are made to the original production data/database, and data is masked and copied to the different environments on demand, thus creating a data transfer limitation in concealing the data sets as they are requested or accessed. With DDM, businesses can implement role-based (object-level) authentication to databases or systems.

On-the-fly data masking (OFDM)

The type is typically used when business processes require continuous movement of data that needs to be masked; for instance, businesses perform software testing extensively. This type functions best to provide a development or testing environment with masked data as soon as it is produced, thus, not requiring any specific staging environment to prepare the masked data for transfer. The process includes masking subsets or pieces of data, as required.

Deterministic data masking

This type of data masking is used for databases with mapping data sets that have similar types of data. In such a database, using deterministic data masking always substitutes one value with another in mapping data sets. For instance, a database with multiple tables containing personal or sensitive information of a customer, like the first name, can thus be replaced with a fixed substitution name. If ABC is the first name present in multiple tables, the ABC is masked with XYZ at every instance in the database.

Unstructured data masking

As the name suggests, this type of data masking is very useful for unstructured data (qualitative and not often being able to categorize as sensitive data by various data tools). Such data includes Unstructured scanned images, such as insurance claims, bank checks, and medical records. This data is shared and accessed by many people in different formats within businesses exposing sensitive information to be at risk.

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Data masking with various platforms – enterprise data masking tools

Secured and sanitized sensitive information enables businesses to maximize the potential of big data. Enterprise data masking tools provide an end-to-end platform with a wide range of features for integrating raw, scattered, structured/unstructured data from various sources.

For example, Informatica makes a robust and versatile data masking platform capable of solving difficult data use cases. Informatica offers resources, called Cloud Data Masking to help safeguard data privacy during sensitive scenarios. The data masking resource helps the data in providing a complete, cloud-native data governance (compliance) and privacy solution. Thus, allowing masked data based on user, roles, and locations. Another enterprise data masking tool is K2view – which has been top-scored in the Gartner Data Masking Report 2022 and offers data through its data product platform. The data product platform streamlines the process of masking all the data pertaining to particular business entities, including clients, orders, credit card details, etc., and controls the integration and transmission of the encrypted Micro-Data of each business entity. For operational services like customer data management (Customer 360) or Test data management, etc., it uses dynamic data masking techniques to modify, disguise, or deny access to sensitive data based on user responsibilities and rights.

The graphical data transformation and orchestration tool uses its in-flight data masking tool to avoid having to fully mask huge data and instead integrates and masks data when a quick transition is necessary from any source systems (production) into any target application. It also uses a combination of data masking types to protect unstructured data.

Conclusion

As businesses incline more to cloud software or applications, it is necessary to enhance the level of security and privacy assurance. Data masking methods comply with numerous data protection requirements, including CCPA, HIPAA, and PCI DSS. Security systems like data masking protect business operations, customers, users, and sensitive information. Depending on the business requirement, various types of data masking are suitable for all businesses dealing with sensitive data.