Core Masking

{"x":230, "y":25, "w":270, "h":100, "texts": [
"It's your move",
	"But you're white",
"That's ok, you go first",
	"But where's your King, Queen and all the pieces?",
"I took them away",
	"So how can we play?",
"You still have all you pieces, and I can use my pawns to defend and attack",
	"But I can't win! There's no King!",
"That's right",
	"So what's the point of the game?",
"There is no point. You won't be able to take my King (data) or any of the valueable pieces",
	"I don't want to play!",
	"I'm gonna look for someone I can actually play with",
"Good luck",
	"I'm going",
"Bye bye",
	"but I work here. I need this job",
"Then do your job. Don't try to steal data",
	"a girl needs some extra cash..",
"Not from stealing my data",
	"I'm gonna play anyways",

Core Masking

Static Data Masking

Many organizations use production data in test and development systems. Such usage creates a massive and unnecessary security risk. Static Data masking allows you to remove the sensitive data from the non-production systems while retaining or enhancing the test quality. This is by far the most effective and efficient security strategy for non-production systems.

Product Capabilities

Remove sensitive data & Validate the effectiveness

Remove Sensitive Data

Core Masking permanently removes sensitive data from non-production systems by replacing it with masked data. The masking process is non-reversible and cannot be undone. Masking only needs to be applied when data is refreshed from production.

Validate Effectiveness

Core Masking includes a unique Masking Evaluation Tool to help you quantify the effectiveness of the masking and ensure no sensitive data is exposed. With complex masking policies applied to large data sets, it is important to know the effectiveness of the policies.

Retain data Validity, Integrity, and Consistency

Data Validity & Invalidity

The masked data must retain data validity in order to be usable by the application. Data Validity can include field size, valid characters, valid patterns, checksums, and more. In some cases, invalid data such as strange characters or patterns must also be retained if it is part of the original data.

Data Integrity & Consistency

Data Integrity means that primary keys have to be unique and maintain their relationship with foreign keys. Data must retain consistency inside the column, across columns, and across databases. Relationships between columns such as city, state, and zip code must be kept, and much more.

Maintain or improve Test Quality & Good Fakes

Test Quality

An important aspect of data masking is to maintain test quality. If the masked data does not allow for the same quality of testing and development it loses its value. This includes retaining invalid or unusual data, data relationships, and more. Maintaining certain attributes of the data or manipulating them allows creating complex scenarios that can improve test quality.

Good Fakes & Realistic Data

Good data masking is ultimately about creating good fakes – data that looks as real as the production data but is actually completely fake. Creating good fakes relies on retaining the statistical properties of the original data and generating data that fits those parameters. Statistical properties can be the frequency of repeating values, repeating patterns, value distributions, and more.

Flexibility, Types of Data, and Algorithms

Data Masking is ultimately all about the data and how to manipulate it. Core Masking supports a large number of types of data and various ways in which to manipulate each one.

Types of Data

Core Masking supports many different types of data. These include:

  • Name-based data such as first/last names, emails, addresses, etc.
  • Numeric quantities such as dollar amounts, inventory, etc.
  • Patterns such as phone numbers, SSNs, license plates, etc.
  • Limited data sets such as gender, country, city, lookup codes, etc.
  • Dates either in a database data type or in text
  • Special data types such as LOBs
  • Mixed data such as columns containing both people names and company names
  • Composite data such as city, states and zip code


Core Masking supports a very large number of algorithms for manipulating different types of data. In general, those can be classified into 4 categories:

  • Modified data – masked values are based on the original values.
  • Data generation – masked values are unrelated to the original values.
  • Automatic profiles – masked values are based on an automatically generated column profile.
  • Manual profiles – masked values based on manual manipulation of automatic profiles.

Business Value

Test Quality – the problem and the solution


Testing and development teams believe that using production data will improve the quality of their work. This production data usually contain sensitive information that needs to be protected and using it outside of the secured production environment increases the security exposure and the risk. This data is often also covered by various compliance regulations.


Core Masking will allow you to retain the benefits of developing and testing with production data without the security risk associated with using such data. Core Masking does this by allowing you to create good fake data – data that looks like your real production data but doesn’t contain sensitive information.

Flexibility, Types of Data, and Algorithms

Value Proposition

Data masking is all about the data you have. A data masking solution will be valuable to you if it is able to mask the specific type of data you have and maintain the unique properties that are important for your testing. A solution that cannot do that will be useless.

Core Masking

Core Masking supports a very large number of types of data and multiple algorithms to manipulate each type of data in different ways. Core Masking will more than likely be able to mask your specific type of data both today and in the future.

Reduce the time and cost of Compliance & Security

Compliance Scope

Compliance is a time consuming and costly effort that companies are forced to adhere to. The easiest way to reduce the scope of this effort is by eliminating systems and individuals from it. Systems that don’t contain sensitive data and individuals that don’t have access to it are not subjected to compliance. Using masked data in non-production systems is an important step in reducing the scope and cost of compliance.

Security Exposure

Every security initiative starts with reducing exposure of the sensitive data. The first step in this effort is to limit the systems that contain it and the individuals that have access to it. Non-production systems such as test and development pose an unnecessary security risk. Core Masking can eliminate the sensitive data from these systems while maintaining the benefits of having it there.

Maintaining Quality & SLAs along with Security & Compliance

Quality & SLA

Testing and developing with production data is an important step in improving software quality, minimizing production problems, and maintaining SLAs. The earlier production data is introduced into the development and testing cycles, the shorter those cycles are, and fewer cycles are required before a successful deployment.

Security & Compliance

Security and compliance are time-consuming and costly efforts and reducing the number of systems and individuals that are subject to those requirements is an important step in lowering costs. Core Masking will allow you to maintain quality & SLAs without compromising your security or compliance.