The immense amount of sensitive information stored in data warehouses makes them attractive targets for data hackers. This means that securing a data warehouse efficiently is of high importance. At present, most companies secure their data using methods, such as swapping, substitution, encryption or number and date variance.
How do you make sensitive information incomprehensible to any eyes it is not meant for, ensuring the data is not personally identifiable, while also allowing the database to be used with real data?
The answer is through data masking.
What is Data Masking?
Data masking entails changing specific data elements within a database in a way that retains its structure while altering the information. Thus, sensitive information is protected as it becomes unavailable to anyone except authorized users.
What Functions Does Data Masking fulfill in a Data Warehouse?
Creating test data is one of the most common reasons data masking is needed within a data warehouse. Users working within testing environments usually have more rights, allowing for uninterrupted access to work with data. Inability to access all the data will render the tests inconclusive. While this is an intrinsic aspect of the job, it leads to an obvious problem: certain users could copy the data and leak it out of the organization for their personal gains. The result would be a loss of revenue and client trust, and potential penalties as well.
By using data masking technique, you can keep your data safe even when it is handed to users for testing and other purposes. Through accurate user rights management and data masking methods, you can secure personally identifiable and sensitive data. This sort of data is especially susceptible to breach via employee data theft or hacking. Data masking enables you to create an alternate version of your warehouse’s data that is structurally identical to the original. So when you need data for user training or software testing, you don’t need to provide users with the real data – just give the functional substitute instead.
Additionally, data masking improves the data warehousing functionality of the enterprise. Since it necessitates the registration of sensitive content, it gives you a better overview of where the data is located and who has access to it. Keeping track of users limits the risk of data breaches from within the organization.
Types of Data Masking
Data masking generally falls into static and dynamic categories. The one exception to this binary categorization is on-the-fly data masking. Let’s have a look at these types of data masking:
Static Data Masking
With static data masking, sensitive data is masked and extracted within the original database environment. The data is moved and reproduced into a test environment and then shared with third parties. Static data masking could be an essential step in working with third-party consultants, but there’s a safety caveat attached to it. This is because real data is extracted throughout the process of data masking, and this opens a backdoor that could lead to potential breaches.
Dynamic Data Masking
Dynamic data masking involves securing data in real time, meaning that the contents never leave the production database. This makes it less vulnerable to breaches, as the data is jumbled in real time to make it incomprehensible and is never exposed to anyone accessing the database. Only authorized users can see the authentic data.
Unlike static data masking, the problem here stems not from security, but database performance. Every second spent running a proxy is a second when the database is not functioning, and that’s a concern in the enterprise environment.
On-the-fly Data Masking with ETL
On-the-fly data masking also occurs in real time. It utilizes a process known as Extract-Transform-Load (ETL) and entails masking data within the memory of a specific database application. If the application is running, the masking will not interfere with continuous delivery. This method is most suitable for agile companies.
Selecting the strategy for data masking will depend on your organization’s size and location, deployment methods, and the data complexity.
Common Data Masking Methods
Data masking involves changing the original value to another one, and there are a variety of ways to accomplish this. Different enterprises will need to use different data masking techniques within their data warehouse, depending on the type of data they store and its purpose and application. Let’s look at some of the commonly used methods:
- Shuffling: The values in a column are randomly shuffled.
- Randomizing: Random values are generated.
- Hiding: Views are used to entirely hide the value.
- Substitution: Each number or character is replaced by a given value.
- Scrambling: Some part of the value is scrambled with a symbol.
- Blurring: A value is turned into a certain range of values.
- Encryption: An algorithm is used to encrypt the value, which can only be decrypted using a secret key.
However, every data masking method you use will bring its unique challenges. When you use the substitution method, for example, you have to make sure you cleanse the data, which in itself is a time-consuming process. When you encrypt the data, you might face field overflow – the false data could overflow the storage capacity allocated before. Despite their limitations, however, it is vital that you use one or more of these techniques to safeguard your data.
Researchers are coming up with new and innovative data masking solutions to keep data warehouses secure with minimal issues. This paper, for instance, proposes that data warehouses employ a transparent data masking solution for numerical values in a data warehouse based on the mathematical modulus operator. The proposed technique is specifically tailored for a data warehouse architecture and is one of many breakthroughs in the field. On the other hand, packaged database solutions provide a well-designed way of dealing with your data warehousing needs as well.
Make sure your data warehouse employs the right data masking techniques to ensure maximum security. Get in touch with our data architects to work out a plan to keep your data warehouse safe from breaches.
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