How to secure non-production data: A Guide.

Secure Non Production Data


Production data and non-production data are very important to an organization of any size. And sometimes real production data makes it to non-production data in databases. That is one of the reasons why securing the data is so crucial. In this post, we are going to explain what production data and non-production data are. Then we’ll show you how to secure non-production data.


Before we talk about securing non-production data, let’s discuss what production data and non-production data are and how they differ. 


Production data is the data that is the business. Every organization, whether a startup or a big multi-national company, has critical data. For a bank, the customer data and the transactional data are production data. And for an e-commerce giant, the production data is the product catalog, the user information, and the transaction. This kind of data is secured with the best systems available. But any data taken by a hacker can cause both reputational and financial losses. 


Non-production data is generally used for testing and development purposes. In an ideal scenario, it should be fake data, but it should emulate real data. Suppose your production database contains 10 million records. That means the test database should also contain 10 million records. One thing to note is that only the action load and performance testing can be done. But sometimes the developers and testers require real production data. And in such cases, they are given a subset of the data, which is generally replicated. This is the reason why securing non-production data is so important. Even if this subset of production data is stolen by a hacker, it can cause havoc in the organization. 


Every organization uses databases. The data is the main part of the business, and in most cases, it’s the business itself. Now, whether this data is stored for internal purposes (like data on all the employees) or for external purposes (like the catalog of an e-commerce site), it is all considered production data. Every developer needs to work with databases to develop applications. They cannot work on production data, as they can corrupt it or, in worst-case scenarios, delete it. So, all developers work on non-production data from non-production databases. This data generally consists of fake records that replicate the original production database. But in some cases, it contains some real data, as the developers need to check the real structure of the records. The testers testing the database or application work on both production and non-production data because they need to test the application before it goes live in production to real-world users. They also test the production data the way an end user experiences it once the release goes through to production. 


As discussed earlier, the non-production data used by developers can also contain sensitive production data. These records can be sensitive records like credit card details, bank details, and even Social Security numbers. The exact data is not required by developers, but they at least need the structure of the database and the schema of the record. Now, before giving the data to developers or testers, it’s important to mask sensitive data through data masking


As the name suggests, we mask the original data before handing it to the developers or testers. In this process, the company first decides on the sensitive data that cannot go to the non-production database. The perfect masking needs to be done in a manner where the original data doesn’t go to the developer. But the data should have some meaning—a zip code should be a valid one. Some of the methods used for masking are shuffling and multiplier. In shuffling, the names are changed, so John becomes David and vice versa. And in multiplier, a random number is added to numeric data like dates. So, 12/31/2010 becomes 13/11/2019. Data masking is generally done with the help of tools, which we will look into in the next section. These tools mask data in two ways: static masking and dynamic masking. In static masking, the production database is used to create a static database, which contains masked data. This masked data is then used by developers and testers. In dynamic masking, whenever the developer or tester makes some query to the production database, a proxy service receives the request. It gets the real data from the production database but converts it to dummy data by masking it. And then it returns this masked data to the developer or tester. 


Here are some of the top data masking tools available. 


The Enov8 Test Data Management platform speeds up your development & testing process by identifying where data security vulnerabilities reside inside your databases, rapidly remediating those risks, through masking, to avoid breaches and automatically validate PII compliance success. It also comes with IT delivery accelerators for example: Data provisioning (DataOps) automation, Data Mining & Test Data Booking features. Enov8, geared for the larger enterprise, is probably the most “holistic” or feature rich solution.


Oracle Data Masking and Subsetting is a solution from a top provider that also runs on non-Oracle databases. It completes the masking in very little time. Besides masking, it also helps remove duplicate data in testing and development databases. The only drawback is that since it comes from a top vendor, it’s costly. For pricing details, you need to contact Oracle directly. 


Informatica’s persistent data masking tool is again a solution from a top vendor. It is created with big enterprises in mind and helps set data masking from a single location. That means the administrator can set the masking from a single place. It also supports a huge volume of data to mask, which is not possible with small solutions. It is again costly because it is an enterprise product. But Informatica offers a 30-day trial period. 


K2View’s Data Product Platform is one of the top data masking products on the market, and it does both static and dynamic masking. K2View not only masks traditional data but also records PDFs and images. In fact, it even masks the original image by blurring it. Because of the cost, it is most suitable for large organizations. 


DATPROF’s data masking tool has a state-of-the-art algorithm, which not only masks the data but can also generate a lot of dummy data from it. Besides traditional data, it also supports XML and CSV files. It has an easy-to-use interface and can create templates, which can be used later. The drawback of these templates is that they can be created on a Windows machine only. It does support a large number of records. 


Accutive Data Discovery and Masking is a top tool that also does data discovery of sensitive data. This is done automatically and can use preconfigured keywords. Or keywords like “credit card” or “Social Security numbers” can be added by the administrator. Besides this feature, the masked data is consistent across multiple destinations. Like if the masking of Rohit is done to John in the development database, then it is the same in the testing database. Also, data can be moved between multiple kinds of databases. It can be moved from an Oracle database to a MySQL database, or from a flat file to a MySQL database. The UI is very easy to use, and they have one of the most cost-effective products.


In this post, we first discussed production data and non-production data, as well as the differences between them. Then we reviewed how to secure non-production data through the process of data masking. This process masks sensitive data from the users of non-production data. We also looked into the top tools available for data masking. 


This post was written by Nabendu Biswas. Nabendu has been working in the software industry for the past 15 years, starting as a C++ developer, then moving on to databases. For the past six years he’s been working as a web-developer working in the JavaScript ecosystem, and developing web-apps in ReactJS, NodeJS, GraphQL. He loves to blog about what he learns and what he’s up to.

A Coder Guide to Data Science?

Data Scientist DataOpsZone

Data Science is an interdisciplinary field that utilizes mathematics, statistics, and computer science to extract meaningful insights from large datasets. It can be used to uncover patterns and solve complex problems in a variety of industries such as healthcare, finance, marketing, and engineering.

 Choosing the right language for a data science project is essential, and there are a variety of languages to choose from. Python, R, SQL, MATLAB, and Scala are some of the best languages for data science, each offering unique features and capabilities that make them suitable for different tasks.

Lets talk about the top 5 languages n more detail.

The What & When of:

  • 1. Python
  • 2. R
  • 3. SQL
  • 4. MATLAB
  • 5. Scala


What is Python?

Python is a high-level, general-purpose programming language that is popular among data scientists for its flexibility, wide range of libraries, and ease of use. Python is used for data analysis, machine learning, web development, and more. It is a great language for beginners as it has a simple syntax and provides a wide range of libraries and modules to help with data manipulation and analysis.

When to choose Python?

Python is a great choice for data science projects that require a lot of data manipulation and analysis. It is also a great choice for projects that have a large and diverse dataset, as its wide range of libraries and modules will make it easier to process and visualize the data. Python is also a great choice for beginners, as it is easy to learn and provides a wide range of resources to help with data analysis.


What is R?

R is a programming language and software environment for statistical computing and graphics. It is popular among data scientists for its powerful statistical analysis capabilities and its wide range of libraries for data manipulation and visualization. R is particularly popular among academics and researchers, who use it to analyze data and build predictive models.

When to use R?

R is a great choice for data science projects that require a lot of statistical analysis. It is also a great choice for projects that require powerful data manipulation and visualization capabilities. R is popular among academics and researchers, so it is a great choice for projects involving research or analysis.


What is SQL?

SQL (Structured Query Language) is a domain-specific language used to interact with databases. It is used to store, retrieve, manipulate, and analyze data stored in a relational database. SQL is popular among data scientists to access and analyze data stored in relational databases, as it is easy to learn and offers powerful features for data analysis.

When to use SQL?

SQL is a great choice for data science projects that involve accessing and analyzing data stored in a relational database. It is also a great choice for projects that require a lot of data manipulation, as SQL offers powerful features for data analysis. SQL is also easy to learn, making it a great choice for beginners.


What is MATLAB?

MATLAB (Matrix Laboratory) is a high-level programming language and environment used for technical computing and data analysis. It is popular among data scientists for its powerful numerical computing and visualization capabilities. MATLAB also has a wide range of libraries for data analysis and machine learning, making it a great choice for data scientists.

When to use MATLAB?

MATLAB is a great choice for data science projects that require a lot of technical computing and visualization. It is also a great choice for projects that require a lot of data manipulation and analysis, as it has a wide range of libraries for data analysis and machine learning. MATLAB is also a great choice for projects involving numerical computing, as it has powerful numerical computing capabilities.


What is Scala?

Scala is a general-purpose programming language that is often used for data science projects. It is a combination of object-oriented and functional programming, and is popular for its powerful features and scalability. Scala is a great choice for data science projects, as it is easy to learn and offers a wide range of libraries for data manipulation and analysis.

When to use Scala?

Scala is a great choice for data science projects that require a lot of data manipulation and analysis. It is also a great choice for projects that require scalability, as it offers powerful features for data manipulation and analysis. Scala is also a great choice for projects that require a lot of object-oriented programming, as it offers a combination of object-oriented and functional programming.

One Size Doesnt Fit All

In many cases, a hybrid approach is best for data science projects. This involves combining the best features of different languages and tools to create a powerful and flexible data science solution. For example, combining Python and R can provide the best of both worlds, with Python providing powerful data manipulation and visualization capabilities, and R providing powerful statistical analysis capabilities.

No matter what language or tools you use, the most important thing is to choose the right ones for your particular project. Finding the right combination of languages and tools to best suit your project can take some experimentation, but it is well worth the effort.

Author Jane Temov

Jane Temov is an IT Environments Evangelist at Enov8, specializing in IT and Test Environment Management, Release and Data Management product design & solutions.

GDPR Software: 11 Options to Help You Comply in 2022

GDPR Software 11

Businesses today have an ever-growing list of privacy restrictions to deal with when collecting and managing data. One of the most notorious pieces of privacy legislation is the EU’s General Data Protection Regulation (GDPR), which became the law of the land in 2018 and carries stiff penalties for violators.

Suffice it to say that GDPR compliance can be challenging. This is largely due to its size and scope as well as its evolving nature. In order to meet GDPR requirements, many organizations are turning to purpose-built software solutions that are designed to be GDPR-compliant out of the box. 

Without a doubt, this is the fastest and safest way to use data and avoid regulatory complications for businesses that sell to customers who reside in the EU.

What Is the GDPR?

The GDPR is one of the most comprehensive and far-reaching global privacy protocols implemented to date. It replaced the EU’s Data Protection Act and is now the main data privacy law in the EU.

While the GDPR is extensive, it boils down to some basic foundational principles. At a high level, companies that handle data from consumers in the EU need to operate with lawfulness, fairness, and transparency. They also have to limit the data they collect and focus on data minimization, accuracy, integrity and confidentiality, and accountability, among other things. 

GDPR also grants users eight basic rights to personal data and privacy, including the right to data access, data consent, data deletion, data portability, processing restriction, notification, and rectification.

The GDPR applies to all kinds of personal data, ranging from health and biometric data to basic identity information like names, mailing addresses, and email addresses. GDPR also impacts any company that collects or processes the personal data of residents of the EU regardless of the organization’s location. It applies to large companies as well as to small businesses with 250 or more employees.

Violators of the GDPR may face penalties of up to €20 million (about $23 million) or up to 4 percent of annual worldwide turnover from the previous financial year, whichever is larger.

What Is GDPR compliance?

When an organization is GDPR-compliant, it means the company meets the law’s various requirements for handling personal data.

The list of requirements is extensive. Some of the most important points involve designating an EU representative, embracing an opt-in mode of data collection, establishing time limits for breach notifications, and responding to customer requests for personal data.

Top GDPR Software Solutions To Consider 

In light of the extensive nature of GDPR, it comes as no surprise that organizations are struggling to comply. According to one study, 85 percent of U.S. companies believe that GDPR compliance regulations put them at a disadvantage against their European competitors.

Even though complying with GDPR is proving to be difficult for global businesses, recent technology advancements make it easier. In fact, there are a variety of GDPR compliant tools on the market that can help streamline workflows and keep you out of trouble. 

1. PrivIQ (Formerly GDPR365)

PrivIQ offers a one-stop shop for GDPR compliance. This platform provides everything you need to know to understand your company’s risks and to manage data privacy. 

Some of PrivIQ’s top features include data mapping, access to privacy notice and governance documents, breach logging support, and graphical dashboard reports. 

2. Onspring

Onspring provides cutting-edge risk management software that simplifies workflows, improves transparency, and helps maintain GDPR compliance. 

This software is excellent for capturing and remediating risks as they appear, including financial, reputational, and third-party threats. The software also makes it possible to control access by user, role, and group.

3. SolarWinds Access Rights Manager

SolarWinds Access Rights Manager (ARM) gives you everything you need to manage access rights across your entire IT environment to ensure GDPR compliance. 

Of note, the GDPR requires detailed user access monitoring. This is especially important for users with sensitive data. ARM can produce custom Active Directory and Azure AD reports, providing instant visibility into what different users can access. 

4. LogicGate Risk Cloud

LogicGate Risk Cloud is a cloud-based platform with prebuilt applications that perform a variety of critical GDPR-related functions. 

For example, the platform automates and centralizes customer requests, investigates breaches, and communicates with supervisory authorities. Additionally, LogicGate ensures that third parties are managing personal data effectively. 

5. Netwrix Auditor

Netwrix Auditor can minimize risk during a data breach. The platform quarantines sensitive data, secures overexposed documents, and manages privilege attestations, among other things.

By using a solution like Netwrix Auditor, your team can promptly discover security threats. If a breach occurs, you can spend less time combing through systems and databases and put more effort into dealing with customers and strategizing on a fix. 

6. OneTrust

OneTrust helps companies enhance their privacy programs. The platform offers prebuilt workflows, templates, automation, and regulatory intelligence to help operationalize data and remain in compliance with GDPR.

On top of this, this platform provides transparency about online tracking and captures consent for tracking technologies, cookies, and marketing communications. It also helps maintain and distribute policies and notices.

7. Vigilant Software Compliance Manager

Vigilant Software Compliance Manager identifies legal and regulatory information security requirements for GDPR. 

Using this software, your company can understand the specific actions that it needs to take to comply with various information security laws. Compliance Manager provides effective dates, direct links to legislation, and implementation requirements. 

8. Boxcryptor

Boxcryptor delivers advanced data protection using state-of-the-art encryption, which is a fundamental part of GDPR. 

With the help of Boxcryptor, your company can ensure that all data receives adequate protection in the cloud. The software encrypts files end to end locally on user devices before they go to cloud storage, enabling strong access control.

9. Didomi

Didomi is a leading privacy and consent management platform. The company offers Didomi for Developers, a comprehensive platform that runs on open APIs and helps integrate customer consent into operations.

Didomi makes it easy to build customer permission into your technology, enabling you to simplify privacy protection and preference management.

Further, the platform provides legal and business teams with real-time consent and preference data for easy compliance reporting. It also enables teams to know when consent is required when collecting new data or using it for different purposes. This in turn reduces risk and lets teams operate with greater confidence.

10. Iubenda

Iubenda makes apps and websites legally compliant across multiple legislations and languages, and GDPR is a main focus. 

With Iubenda, you can access helpful services like a privacy and cookie policy generator, a terms and conditions generator, and a consent solution. The company also offers a cookie solution to manage consent preferences for GDPR and other similar regulations.

11. Enov8 Data Compliance Suite 

Enov8’s Data Compliance Suite uses automated intelligence to identify security exposures and address issues before they lead to major incidents. 

The platform gives IT teams clean production-like data for developing and testing platform changes, eliminating complex and time-consuming manual work. 

Simply put, Enov8 enables teams to work faster and with less risk while eliminating costly remediation efforts and compliance issues. 

GDPR Compliance Can Be a Breeze with Enov8

Achieving GDPR compliance doesn’t have to be a nightmare. With the right software in place, your team can continue developing and using data at a fast pace while avoiding costly fines and penalties. 

To learn how Enov8 can help your organization achieve and maintain GDPR compliance, take the platform for a spin.

Post Author

This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling.