Integrate and mask
Entity data model ensures integrity
Static and dynamic masking
Protect operational, analytics and test data
Structured and unstructured data
Extensive, extendible masking tools
K2View Data Masking tools are based on our Data Fabric, which organizes fragmented data from disparate systems according to Digital Entity™ data schemas – customer, order, device, or anything else that’s important to the business.
The digital entity unifies everything a company knows about the business entity – including all interactions, transactions and master data. This individualized data organization simplifies data protection and privacy compliance. It accelerates enterprise scale dynamic data masking for operational use-cases like 360 customer view and persistent data masking for test data management and enterprise data pipelines .
K2View in-flight Data Masking eliminates the need for slow, cumbersome and risk-prone staging areas, where unmasked data is exposed to potential breaches. Using our graphical data orchestration tool, data from multiple production systems is integrated, cleansed, and masked on the fly.
The entity-based data model cuts the complexity ensuring that individual customer data from different sources is:
K2View dynamic data masking transforms, obscures, or blocks access to sensitive information fields based on user roles and testing environment privileges. Using data orchestration, a wide variety of in-line masking functions can be invoked to protect the data.
Protect unstructured data including images, PDFs, XML, CSV, text files, and more, with static and dynamic masking capabilities. Replace sensitive photos with fake alternative ones, use OCR to detect content and enable intelligent masking, synthetically generate digital versions of receipts, checks, contracts and other items for testing purposes. By managing unstructured data within the digital entity data schema, referential integrity is ensured, and consistency maintained, across structured and unstructured data.
K2View Data Masking has an extensive library of masking functions designed to provide realistic, but fake, data. The chart below provides a number of examples including masking to valid social security numbers (SSN), selecting names from name directories, random number generation, and address-based zip codes. The library can be easily extended by with Java functions that implement additional masking functions.
|SSN/National ID||Generate valid SSN|
|Credit card||Generate valid number based on card type|
|First name/Last name/Zip code||Select from collection|
|DOB||Shuffle (preserve statistical diversity)|
|Any String/number||Random String/number|
|Concatenation based on new first and last names|
|Const||Static masking based on a pre provided value|
|Address||Based on the provided Zip|
Learn about a new, real-time data masking approach, based on business entities, that is used by some of the world's most data-intensive enterprises.
Download this whitepaper to understand how, using this unique data model, you can:
Mask in-flight with referential integrity
Manage and anonymize unstructured data
Support both persistent and dynamic data masking
Employ advanced and extendible masking functions
Generate production-grade realistic yet fake data