Data breaches are one of the most serious issues confronting the business world and management in the twenty-first century, resulting in millions of dollars in losses and damage to brand reputation each year. These losses are also supported by research - according to IBM’s research, the average cost of a data breach in 2021 will be $4.24 million, the highest value in the last 17 years. Data masking is one of the most effective techniques to prevent these risks and thereby eliminate or decrease losses.
Data masking plays a very important role in access security allocation, and is one of the sub-headings of cyber security protocols. It is also one of the data security requirements indicated by the term “pseudonymization” in the GDPR.
In this article, we will discuss the definition, type, and function of the data masking method, which is one of the key aspects of a privileged access security system developed to avoid data breaches, as well as analyze the benefits of this method for organizations.
What is Data Masking?
Data masking, also known as data hiding, data anonymization, and pseudonymisation, is one of the building elements of privileged access infrastructures. Data masking is a cyber security technique that uses modified, functioning, fictional data like characters and numbers to hide real data.
Data masking protects vital and classified data by making it difficult for a cyber attacker to identify. The data masking approach assures that data is constant across numerous databases and that its usability is not altered, to create a version of data that cannot be seized by reverse engineering software.
However, it should be emphasized that the main benefit of this strategy against a cyber attack is the capacity to safeguard the most sensitive data, such as credentials and passwords, even when sharing data with third parties involved in your business model.
Importance of Data Masking and Types of Data That Should be Masked
Data masking is of crucial importance to organizations for many different reasons. First of all, data masking reduces the chances of sensitive data being seized, making it easier for organizations to comply with regulations such as the GDPR. Compliance with these kinds of regulations provides considerable security enhancements and competitive advantages to organizations.
This method maintains the availability and consistency of data. While data masking sustains the functionality of data, it also renders it useless in case cyber attackers access and try to use it. In addition, data masking reduces the risk of a breach in data sharing by way of cloud computing and third-party applications, which are a natural part of digital workflows.
In addition to all these, data masking minimizes the threats of outsourcing projects you work on. As organizations often build their outsourcing business relationships on mutual trust, situations may emerge where the existing data security control is inadequate. Since data masking is based on the principle of least privilege, it prevents the misuse of data in business-related transactions as well.
It may be useful to emphasize the importance of data masking with recent data obtained from business and lines of work sources that are highly important for organizations.
The number and cost of data breaches occurring in business and management devices are increasing each year. IBM's latest report presents striking data on the mounting costs of breaches. For example, while the average cost of a data breach was $146 in 2020, this cost has gone up to $161 in 2021. Another data point in the same report reveals that the detection and controlling processes of violations takes much longer than before. While organizations needed 280 days to detect and contain a data breach in 2020, it has increased to 287 days in 2021. The extension of the detection and control period makes it even more important to use data masking.
According to the information in Verizon's DBIR-2021 Data Breach Investigation Report, 85% of the violations involve human error. This data is also confirmed by the IBM report in terms of cost, which reveals the human involvement in the breaches. The report shows that breaches by malicious people cost $4,61 million, while business email breaches cost $5,01 million.
According to the numbers in the IBM and Verizon reports, cyber attackers or internal threats can target vital categories of data and carry out malicious operations or cyberattacks. So, what kind of sensitive data are we talking about? There are numerous kinds of sensitive data depending on the organization, but we can roughly list them as:
Health system data
Banking system data
Human resources data
What are the Types of Data Masking?
The main types of data masking techniques include static data masking, dynamic data masking, deterministic data masking, and on-the-fly data masking. It employs encryption, hiding, mixing, cancellation, substitution, number and date difference, and aging date methods. Now, let's go over these different types of data masking in more detail.
Static Data Masking
With the static data masking method, batches of data are masked in the original database. The test data environment is then cloned into a new test environment, making it easier for businesses to share data with third parties.
Dynamic Data Masking
Unlike the static method, with dynamic data masking there is no need for a new test environment to store the masked data. Data remains in the original pool and can only be accessed by authorized personnel in your IT network. This way, the data is never displayed to users who do not have privileged access rights.
In addition, the obfuscation process in this method is done in real-time to conceal the data, and only privileged accounts can display the real data. A reverse proxy method is generally used to achieve dynamic data masking. The other method which is used to attain this result is called instant data masking.
Deterministic Data Masking
The deterministic type of data masking involves replacing the data in the columns with the same type of value. For example, if you have a name column consisting of multiple tables in your databases, it is possible to create many tables with names. For example, if you mask 'Jonathan' with 'Claire', then 'Jonathan' is displayed as 'Claire' not only in the masked table but also in all related tables. Then, every time you activate this masking, you continue to see 'Claire' instead of 'Jonathan' on the tables.
On-the-fly Data Masking
On-the-fly data masking can be achieved when data is transferred from production environments into another medium such as test or development. On-the-fly data masking is more common in environments with uninterrupted software installation and data flow or extensive and interlocking integrations, as it is unfeasible to keep a permanent backup copy of the masked data in such mediums.
With our Privileged Access Management (PAM) product Single Connect, with its Database Access Manager & Dynamic Data Masking module, you can record all administrator activities in the database and mask them to eliminate any concerns regarding the transactions performed in your IT network. With the Dynamic Data Masking module, sensitive data in database query result sets can be concealed using some special definitions such as Redaction/Nulling, Shuffling, Blurring, Tokenization, and Substitution.
Single Connect is one of the most effective Privileged Access Management solutions in the world, and protects organizations and institutions' sensitive data from being exposed. Single Connect is a proven Privileged Access Management product recognized by Gartner in the Gartner Magic Quadrant for PAM and by Omdia in the Omdia Universe: Selecting a Privileged Access Management Solution, 2021-22. Single Connect helps you protect your privileged access environment and makes your workflows sustainable with its advanced modules.
If you need further information about data masking and Single Connect, please feel free to contact us. Our expert team will be happy to answer your questions.