As IT leaders recognize the importance of data in developing data-driven applications and software and unlocking competitive advantage, it is becoming more critical to provide secure access to data that flows across an organization to innovate faster and at scale without compromising privacy and security.
This need invites the practice of cloud Data Masking to provide a workable, secure solution.
The practice of substituting original production data with structurally identical, inauthentic data is known as data masking. Essentially, the data is in the same format as before, but the values have changed.
In this article, you will learn the following:
Data Masking in a Nutshell
Data masking is a method of shielding sensitive information by replacing the original value with a fake but realistic counterpart. It is an umbrella word encompassing data anonymization, pseudonymization, redaction, scrubbing, or de-identification.
The majority of firms have robust security procedures to secure production data both in storage and use. However, data sometimes gets utilized for less than certain purposes, such as testing or training, or by third parties outside the business. These purposes can jeopardize the data, as in data breaches, and lead to compliance issues.
In this context, data masking is an option that allows access to information without failing to protect sensitive data.
The primary goal of masking data is to protect complex and confidential data in situations where someone without their permission could see the data.
Types of Data Masking
Depending on the use case for your masked data, you can utilize different types of data masking, such as the following:
Static Data Masking (SDM)
Although the first way of masking data may appear outdated, it may be the best option in some situations.
Static masking entails retaining multiple copies of the data with varying levels of redaction and granting access to data users based on policies. When you grant access to users, you do it based on the level of sensitivity that the user requires.
The data in this data masking procedure gets redacted as it gets created, which is usually done as part of the extract, transform, and load (ETL) process or by parallel insertion of data to separate destinations with varying detail levels.
Dynamic Data Masking (DDM)
Dynamic data masking on the datastore typically gets accomplished using technologies such as the dynamic masking policies of Snowflake or MS SQL or by utilizing VIEWs. Using data masking tools for SQL Servers, masking data in storage removes traces such as logs or changes in data captures.
DDM occurs dynamically at run time and streams data straight from a production system, eliminating the requirement for masked data to get saved in another database. Applications like customer service and medical records use it to process role-based security. Thus, DDM is used in read-only environments to prevent masked data from being written back into the production system.
Moreover, you can implement DDM by utilizing a database proxy, which alters queries sent to the actual database and returns masked data to the asking party. Although you do not need to construct a masked database in advance using DDM, the application may have performance issues.
On-the-fly Data Masking
When data gets transferred from a production environment to another environment, such as a test or development environment, on-the-fly data masking happens. Data Masking on the fly is appropriate for organizations that:
- Continuously deploy software
- Have a lot of integrations
Data Masking is necessary for these organizations because it is challenging to keep a continuous backup copy of masked data; thus, this method will only communicate a piece of the masked data when required.
Data Masking Tools
Data Masking tools are safeguarding tools that prevent the misuse of complex data. Data obfuscation tools replace sophisticated data with fictitious data. You can use them during application development or testing where the end-user enters the data.
Here are some of the best open-source data masking tools that leading data masking vendors offer:
Satori allows you to dynamically hide any data platform accessed based on your security regulations, which you may define based on identities, data locations, and data categories. You can also visit this page to learn more about how our masking works.
Power BI from Microsoft and Tableau from Salesforce are two of the top BI tools, and you can configure data masking within them. Power BI and Tableau are powerful analytics tools designed to help people see and understand data. They can revolutionize how problems are solved and, in some circumstances, influence an entire organization’s culture.
Because both technologies are designed to visualize data, one way to compare them is how well they accomplish their core goal: assisting people in seeing and understanding data.
Existing Data Store Technologies
As mentioned, you can either program policies or by using database views (or sometimes a combinaiton of data views, entitlement tables and functions), and create your own dynamic data masking on the data stores themselves.
Aside from these, other notable data scrambler software and tools include:
- IRI FieldShield
- Executive Data Discovery & Masking
- Oracle Data Masking and Subsetting
- Informatica Persistent Data Masking
Data is becoming more important in the realm of information technology. As a result, keeping a competitive edge in a data-driven world is critical, and the necessity for data security is becoming more apparent.
In this way, data masking protects your data from unintentional and deliberate threats by guaranteeing that sensitive data is not accessible outside the production environment.