As per a recent estimate, the global average cost of a data breach exceeds $3.5 million. It is, therefore, essential to protect sensitive business information. In addition, data protection is now everyone’s responsibility across the entire organization with more stringent laws and governance standards. Besides, the instances of data breaches are increasing rapidly. That being said, to proactively protect their data, avoid the expense of security breaches, and ensure enforcement, a growing number of organizations rely on data masking.
Defining Data Masking
Data masking is a method businesses use to conceal data, as is evident from the name itself. Real data is usually obscured by random characters or other data. The primary function of masking data is to shield confidential, private information. Various types of data can be shielded using masking, but the following are widely used in the business world:
· PHI or Protected health information
· PII or Personally identifiable information
· ITAR or Intellectual property
· PCI-DSS or Payment card information
The Different Types of Data Masking
Most experts agree that data masking, with one exception – on-the-fly data masking – is static or dynamic. Here’s a look at three key data masking types:
Static Data Masking
Static data masking refers to the masking of critical data in the original database environment. The material is duplicated in a test environment and can then be shared with third-party suppliers or other required parties. In the manufacturing database, data is masked and extracted and moved into the test database.
Dynamic Data Masking
Automation and rules allow IT departments to protect data in real-time during dynamic data masking. This makes sure that the data never leaves the production database and, as such, is less vulnerable to threats.
As the contents are jumbled in real-time, making the contents inauthentic, data is never revealed to those who access the database.
Using a reverse proxy, a resource called a dynamic masking tool finds and masks certain forms of sensitive data. It will only be possible for approved users to see the authentic data.
Dynamic data masking issues mainly stem from database performance. Time is money in an enterprise setting. In addition to the time considerations of running such a proxy, a cause for concern might be whether the proxy itself is reliable.
On-The-Fly Data Masking
On-the-fly data masking happens on-demand, similar to dynamic data masking. An Extract Transform Load (ETL) method occurs in this type of data masking where data is masked within a given database application’s memory. For agile businesses based on continuous delivery, this is especially useful.
Data Masking and Data Security
In a variety of security conditions, data masking is useful. Here are some of the key reasons for enterprises to use data masking:
Protection of third-party vendor data: During exchanging certain information with third-party advertisers, consultants, and others, certain information must be kept confidential.
Operator mistake: Companies trust their insiders to make the right decisions, but data breaches are often the product of operator mistakes, and data masking can protect companies’ data.
Not all operations involve using fully actual, reliable data: Within an IT department, several functions do not need real data, such as some testing and application use.
Data Masking: Some Best Practices
Some data masking best practices are as follows:
Find Data: This first step includes recognizing the different types of data that might be vulnerable and cataloging them. Company or security analysts who compile a detailed list of enterprise-wide data elements also carry out this.
Assess the Situation: This stage involves monitoring by the security administrator who is responsible for deciding if there is confidential information, the location of the data, and the ideal technique for data masking.
Implement Masking: It is not realistic to assume that a single data masking technique can be used for the entire business for very large organizations. Instead, architecture, proper planning, and a look at potential market requirements must be taken into consideration for implementation.Test Results for Data Masking: This is the final step in the process of data masking. To ensure that the masking configurations yield the desired results, QA and testing are required. If they don’t, the DBA will restore the database to the pre-masked state, tweak the masking algorithms, and once again complete the process of data masking.