Data Masking Overview

As the volume of personal data increases across industries and the number of data attacks on companies continues to increase, businesses large and small are looking for best practices to protect their data masking overview.

Not only are they worried about protecting their production data, but also their non-production data, because internal threats account for more than 60% of data attacks. Data Masking Overview is the method most recommended by experts in non-productive environmental industries.

With this in mind, an increasing number of organizations are relying on data masking to proactively protect their data, avoid the cost of security breaches, and ensure compliance.

This is why it’s so refreshing when the aspect of data security has a flawless and direct meaning, as masking data. It’s simple: if you want to cover something up or prevent others from seeing it clearly, cover it up.

The confidentiality of the test data is very important for all businesses. Data hiding is a great way to protect your test data for better test results and less budget overruns in your project. Learn about data masking in this article.

What Is Data Masking?

Data masking is also known as data obfuscation, data anonymization. Secret data is replaced with functional dummy data such as symbols or other data. The main purpose of data disguise is to protect sensitive personal information in situations where the company discloses the data to third parties.

Forrester defines data masking as the process of hiding personal data in a non-production environment so that application developers, testers, authorized users, and outsourcing providers are not exposed to that data.

Personal Data Includes:

  • Name
  • Address
  • Social security number
  • credit card number
  • Financial data
  • Health care information
  • Information for employees or users
  • Other types of confidential information that apply to organizations

To hide personal information, you can write scripts to change numbers, names, addresses, etc. or use a software tool provided by a vendor that automates the process. The goal is to have a scalable masking solution while hiding the masking logic so that it can’t be used to decode or recreate the original data.

How Does Data Masking Work?

Masking data is simple, but there are different techniques and types. In general, companies first identify sensitive data that belongs to their company. They then use algorithms to mask sensitive data and replace it with structurally identical but numerically different data.

What do we mean by structurally identical? For example, passport numbers in the US are 9 digits long, and people usually need to share their passport information with airlines. When an airline creates a model for analyzing and testing a business environment, it generates a different 9 digit passport identification number or replaces some numbers with symbols.

Who Uses Data Masking?

In 2018, companies learned to incorporate data concealment into their security strategies, particularly regarding the requirements of the General Data Protection Regulation (GDPR).

If you’re reading this, you probably know that the GDPR requires all companies that receive data from EU nationals to adhere to their administrative principles before May 2018. For some organizations, this has created a need to strengthen their security strategies by incorporating best practices for data hiding.

There are many types of data that can be protected by masking. Some that are widely used in the business world include the following:

  • rsonally identifiable information or PII
  • PHI or proprietary health information
  • Information about PCI-DSS or payment cards
  • ITAR or intellectual property

All of the above examples are governed by management principles.

Why is data masking important now?

The number of data breaches has increased from year to year (compared to mid-2018, the number of registered violations increased by 54% in 2019). Therefore, companies need to improve their data security systems. The need for data masking is increasing for the following reasons:

  • Companies need copies of production data if they want to use them for non-productive reasons, eg. to test applications or to model business analysis.
  • Your company’s data protection guidelines are also at risk from insiders. Therefore, companies still need to be careful and provide access to employees from within. According to an insider study on data breaches for 2019.
  • 79% of CIOs believe that employees have accidentally compromised company data in the past 12 months, while 61% believe that employees have intentionally compromised company data.
  • 95% are aware that internal security threats are a threat to their business.

GDPR and CCPA force companies to strengthen their data protection systems, otherwise they will have to pay hefty fines.

Why is data masking required?

Data masking is useful to hide information for a number of security scenarios. Here are some of the main reasons companies use data masking:

  • How to protect data from third parties: Although some data is passed on to third parties, consultants and others, it is clear that certain information should be kept confidential.
  • Operator error: Companies trust their insiders to make good decisions. However, data breaches are often the result of operator error and organizations can protect themselves by covering up data.
  • Absolutely real and accurate data does not have to be used for all operations: IT departments have many functions that do not require real data, such as: B. multiple test and application usage.

Defining data masking means understanding the critical role it plays in your company’s overall data security strategy.

What types of masking data are there?

There are several types of data masking that you need to consider in the next few steps. Most experts will agree that masking data is static or dynamic, There are two main types of data masking:

  • Static data masking: Static data masking refers to the process used to hide important data in a native database environment. The content is duplicated in a test environment and can then be shared between third parties or other parties as needed.
  • The data is covered up and retrieved from the production database and transferred to the test database. While this is a necessary process for working with outside consultants, it is not ideal. This is because hiding data for duplicate databases will extract the real data, which can create loopholes that encourage breaches.
  • Dynamic data masking: Through dynamic data masking, automation and rules enable IT departments to provide real time data protection. That is, it never leaves the production database, so it’s less vulnerable to threats. The data will never be available to anyone with access to the database because the content is mixed in real time, making the content fake.
  • A resource called a dynamic masking tool finds and hides certain types of sensitive data using a reverse proxy. Only authorized users can view authentic data. Concerns about dynamic data hiding arise mainly from database performance. In a corporate environment, time is money and even milliseconds are valuable. Apart from the launch time of the proxy, there may be concerns about whether the proxy itself is secure.

General data masking technique or method:

There are a number of techniques that IT professionals to mask data in database can use when covering data. Here is a list of data masking techniques and how they can be applied to your business:

  • Encryption: When data is encrypted, authorized users must be able to access it with a key. This is the most complex and the safest type of data masking. Here the data is covered by an encryption algorithm.
  • Scrambling Characters: The most basic masking technique is mixing characters. This approach shuffles the characters in a random order so that the original content is not displayed. Using character encoding, for example, an employee with ID number # 458912 could read # 298514 in a production record in a test environment.
  • Nullifying or delete: As the name suggests, when you use this approach, data becomes null for anyone who is not allowed to access it.
  • Difference in numbers and dates: Done right, changing numbers and dates can provide you with useful notes without revealing important financial or transactional data. For example, a record giving employee salaries could give you a salary range between the highest and lowest paid employees while they were undercover. You can ensure accuracy by applying the same variant to all salaries in the kit so they don’t change.
  • Replacement or substitution: This substitution effectively mimics the look and feel of real data without compromising other people’s personal data. This approach replaces authentic appearing values with true values. This effectively hides authentic data and protects it from future breaches.
  • Shuffling: Mixing uses one record, not another, like replacement. However, when shuffling, the data is randomly shuffled in separate columns. The source set looks like authentic data but contains no real personal information.

Data masking best practices

When it comes to your company’s processes, you want to learn from the best. Here are best practices for creating a data masking strategy in your organization:

  • Finding Data: This first step identifies and catalogs the various types of data that may be sensitive. This is often done by a business or security analyst who compiles a comprehensive list of data items across a company.
  • Situation assessment: This phase requires oversight from the security administrator, who is responsible for determining the presence of sensitive information, data location, and ideal data masking techniques.
  • Apply masking: Remember, for very large organizations, it cannot be assumed that one data masking tool can be used throughout the organization. Its implementation must take into account the architecture, proper planning, and look at the future needs of the company.
  • Test Masking Results: This is the final step in the data masking overview process. Quality assurance and testing are required to ensure that the masking configuration produces the desired results. If this is not the case, the DBA restores the database to a previously covered state, optimizes the scanning algorithm, and completes the data cracking process again.

Top data masking tools:

Tool NamePlatform ConnectivitySupported technology
DATPROF Data Masking ToolOracle, SQL Server, PostgreSQL, IBM DB2, EDB Postgres, MySQL and MariaDB.GDPR, synchronization template, Synthetic test data, TDM, CISO, ERD, Runtime API, Deterministic masking
Microsoft SQL Server Data Masking</strong>T-Query, Windows, Linux, Mac, cloud.DDM
Informatica Persistent Data MaskingLinux, Mac, Windows, Relational DB, Cloud Platforms.SDM, DDM
Oracle – Data Masking and SubsettingCloud Platforms, Linux, Mac, Windows.SDM, DDM, Data Virtualization with SDM, Tokenization.
Accutive Data Discovery & MaskingOracle, SQL Server, DB2, MySQL, Flat Files, Excel, Java based platforms, Azure SQL Database, Linux, Windows, Mac.SDM, Database Subsetting, ETL, REST API.
Conclusion about Data Masking Overview

There are some very important details a data booster must provide in order to successfully protect data and make it functional for testing and development. First, the hidden data must look like production data and have reference integrity, in other words, the connection between and between data must be maintained. Although production data can be used as input for the masking process, masked data cannot be undone.