Uncover Everything About Data Masking Tools

The reason why V from V for Vendetta wore a mask was to make the point that anyone could be behind it. If you’ve seen the movie, you’d remember the spectacle climax scene where everyone is wearing the same mask as his to reiterate the point (don’t worry, it’s not a major spoiler!). The inspiration behind the mask was Guy Fawkes and since the book and movie, the mask has gained popularity in the real world too (even if for the wrong reasons) to protect one’s identity, most infamously used by the Anonymous hacker group. Organizations do the same thing with the data they have amassed through the process known as data masking – yes, literally give data a mask to hide its true value.

Data Masking is the process of converting original data into a morphed or hidden set of data. Usually used for training or testing purposes, it protects Personally Identifiable Information (PII). The format remains the same but the values or characters are changed. In most cases, the original data isn’t required, however similar data is needed. In the case of outsourcing, sensitive data need not be shared. There are numerous techniques and types of data masking offered by Data Masking Tools which is what we will cover. Let’s begin with the basics!

What Is Data Masking?

Data masking is altering, replacing, covering, encrypting or deleting personal, confidential or sensitive data from databases while sharing it internally, externally or during outsourcing. Essentially every organization that needs to share data across functions for training, testing or other purposes needs to use Data Masking Tools. Primarily this includes organizations that must adhere to data protection acts, regulations or work with Personally Identifiable Information (PII), Protected Health Information (PHI), Payment Card Information (PCI-DSS), Health Insurance Portability and Accountability Act (HIPAA).

Data Masking Tools offer a range of features such as:
 

• Organizing data and tracking changes when the data needs to be masked.

• Identifying and sorting out unstructured data.

• Assessing and securing data from unauthorized viewers or data leakages.

• Retaining the integrity of the original data.

• Providing quick and effective data substitution in the form of realistic data.

• Ensuring irreversible masking to prevent decryption of sensitive data.

• Covering a wide range of techniques, data types, functions, etc.

• Offering a repeatable process in the future.

In a world where data is as good as currency, Data Masking Tools are banks that protect your wealth and in some cases, literally. Now let’s see where these tools came from!
 

The Evolution of Data Masking Tools

Data masking isn’t a new process as a discipline. It has been utilized since the time businesses kept their written data in storage lockers and printed documents had information blackened out. Of course, this doesn’t speak of the modern form of Data Masking Tools as software, which is a much more recent addition to the industry.

The modern form of Data Masking Tools started blooming around 2012 when it was given popularity by Gartner. Since then, its importance has been realized and such tools have been adopted at an increasing rate. However, before there were software tools built for data masking, organizations built in-house tools to deal with the requirements and threats faced regarding data leaks and breaches. According to IBM, in 2022 the average cost of a data breach was $4.35 million, while for the US alone it was $9.44 million. This was what pushed businesses and software developers to create Data Masking Tools.

Suffice it to say Data Masking Tools are a necessity today. Let's now see how they roll.

How Do Data Masking Tools Work?

 
Data Masking Tools offer several techniques to mask data. These include:
 

• Data Encryption

  This technique is the most intricate. It involves scattering data using mathematical equations algorithms. Users who wish to view the original data must possess an encryption key to do so.

• Data Scrambling

  Data characters are interchanged with each other, using values already present but just changing the position.

• Data Substitution

  Values are replaced with fake, similar and realistic values not included in the original data.

• Data Redaction

  Values can be replaced with a single character instead of different ones, for example as commonly observed with credit/ debit cards using the ‘XXXX XXXX XXXX 1234’ method, which is also known as redaction.

• Value Variance

  Usually used for financial data, where data values are replaced by a number or value that falls in between a range, usually closer to an average or could be taken from anywhere between the highest and lowest value.

• Deletion Or Nulling Out

  Data is marked as ‘null,’ ‘empty’ or ‘missing’ when viewed by unauthorized users.

• Pseudonymization

  When any identifiable markers that can be linked to the individual are viewed, the information pertaining to the individual is removed.

Ahead of these, there is a concept known as ‘Additional Complex Rules,’ which makes use of rules that can be applied to data masking to further enhance data protection.

All clear so far? Surely must be! So, we move to the types of Data Masking now!
 

The Types Of Data Masking

There are a few different kinds of tools for data masking depending upon requirements. The most common types include:
 

• Static Data Masking

  A separate copy of the database is made in which all the sensitive data is masked, replaced or removed. Once the process is complete, the altered data can be shared.

• Dynamic Data Masking

  Here masking happens at the time data is needed. In this approach, a secondary database isn’t created but rather the masked data is streamed directly.

• Deterministic Data Masking

  Here specific data that is replaced with a similar value is replaced in associated datasets everywhere else as well. For example, if “Steve” is replaced with “Tony” in a particular dataset, it will be replaced the same way in associated datasets as well.

• Statistical Data Obfuscation

  Production data of companies include figures in the form of statistics, which is masked before it is shared with non-production users. Differential privacy is included in this, which allows the sharing of patterns in a data set without revealing information about individuals in the data set.

• On-the-fly Data Masking

  For organizations where data sharing is a continual process between different environments like production, testing or development. Here data is masked in smaller batches or subsets before the transfer, as and when needed.
   

Such a wide range of techniques and types offered by Data Masking Tools speaks (unmasked) volumes about its importance. So, let’s at those now.
 

Why Are Data Masking Tools Important?

Data is used by many, if not all, organizations to improve their services and production, test strategies, develop internal projects and for training purposes while ensuring top-notch data security practices. It remains important to maintain the privacy of customer details as well as company information, especially given the strict laws related to privacy and data leaks and breaches. This is where Data Masking Tools come into play. They help businesses with:
 

• Data Privacy

  Masking data is the first step in ensuring rules and regulations are followed and maintaining the privacy of personal information or sensitive data when it is shared internally or externally for any function.

• Preventing Data Breaches

  In the case of data breaches, masked data would be of no use to anyone in possession of it. Also, in the unfortunate event that a company faces an intruder or someone without authorization gains access to their data, having the data masked prevents data leakage.

• Restricted Access

  Certain functions across a company only require a specific and limited set of data or details to be carried out. Data Masking Tools can be set up to share only the necessary amount of data required on a repeatable basis.

• Data For Tests

  Strategizing is a big part of business plans. This usually requires very specific information from past customers, which could include spending amounts or even personal information. Data Masking Tools make it possible to carry out tests without having to compromise the quality of data by providing suitable substitutions.

As you see, Data Masking Tools protect organizations from breaches, ensure compliance and ensure regulations are heeded. This offers some benefits to its users. Let’s look at those!

What Are The Benefits Of Data Masking Tools?

Data Masking Tools prevent organizations from overlooking threats and lapses in data security by masking sensitive data. Additionally, they offer the following benefits:
 

• Threat Prevention

  It helps in preventing data leaks, data loss, breaches, data misuse and other external threats.

• Stronger Cloud Infrastructure

  As cloud-based applications and servers are gaining popularity and becoming the norm, organizations must ensure data saved on their cloud is well-protected.

• Protects Data

  Data can be shared with authorized internal teams without revealing critical data. When outsourcing processes or working with external companies and third-party vendors, Data Masking Tools enable the safe transfer and exchange of data.

• Data Sanitization

  In the event of deleting files, data sanitization replaces the old values with masked data helping bolster data security.

• Maintains Original Format

  Data Masking Tools make sure the real data format and structure are maintained in the adjusted data as well.

The utilization of data is only going to increase in terms of acquisition, storage and usage. This means that Data Masking Tools will need to be more comprehensive and adaptable to the changing requirements. Let’s see what’s in store for the future of Data Masking Tools.

The Future Of Data Masking Tools

As part of a new and developing data economy, Data Masking Tools will soon progress to become automated. With the integration of Artificial Intelligence, Data Masking Tools will be smarter, more efficient and be able to learn and flag areas that need improvement. Additionally, organizations will turn to AI integrations for business plans which will rely on data, increasing the ability for Data Masking Tools to identify when they need to step in.

The rising focus on data protection and preventing data leakage will lead to organizations adopting comprehensive Data Masking Tools, especially with the accelerating adoption of cloud hosting and cloud computing. After all, data breaches and cyber-attacks on the cloud are not news. This is evident from the trend and projections of the Data Masking Technology market, which had a revenue of 468 million USD in 2019 and is expected to reach 1039 million USD in 2025, with a CAGR of 14.2% during 2020-2025. As the reliance on customer data grows, implementing Data Masking Tools across various data touchpoints will not be optional and we will see even smaller businesses invest in such tools.
 

So, What Have We Learned?

 
Businesses rely quite a bit on the data they possess to enhance their products and services and serve their customers better. Quite often this means sharing critical and confidential company information with different departments that don’t necessarily need to see that information. Alternatively, such data could be accessed by unauthorized viewers in unwanted circumstances. Sometimes for these functions, companies make use of third-party vendors and when they outsource, they also find the need to share data and information.

Hence, it’s important to protect the private and sensitive information that goes with it. All of this becomes even more vital when organizations must adhere to stringent rules and regulations regarding privacy and data protection laws. Therefore, companies use Data Masking Tools, which ensure they adhere to regulations and remain safe from data breaches, leaks, or unwanted and unauthorized users from collecting confidential and personal data.