Sound data governance begins by identifying the location of sensitive data and PII across your business. In complicated databases, the discovery process could take days, and assessing the risk even longer. The Hush-Hush Sensitive Data Discovery Tool discovers sensitive data based on both the metadata and elements values, dramatically speeding up the discovery process with 100% accuracy. Our AI uses an expert determination process to select the best algorithm for your needs, allowing you to work quickly and begin the vital task of data de-identification.
De-identify HL7 and x12 message files without installing Biztalk using our utility. We provide both multi-file and single file modes that allow you to define and save models of sensitive elements to predefine algorithms for uniformity of enterprise data masking.
Hush-Hush Text Data Masking discovers and anonymises sensitive data in your text fields. It uses simple configurations of the algorithm selection. There’s no need to write code to provide consistent data masking of text and structured data throughout your enterprise.
Our patented data masking components (Patents: US9886593, US20150324607A1, US10339341) form the core of the Hush-Hush data masking product suite and provide a high level of data protection as required by privacy laws such as HIPAA, GDPR, and CCPA. It satisfies requirements of such privacy frameworks as NIST and HITRUST. No matter which industry you’re in, safeguarding sensitive data and PII is the core purpose of your security framework. Hush-Hush data masking components draw on a variety of industrial-grade algorithms to meet or exceed all accepted standards for data privacy metrics like k-anonymity and l-diversity. We also provide a variety of generic algorithms you can customize to your specific needs.
Hush-Hush SSIS Components are an extension of SQL Server Integration Services that install, integrate, and get to work instantly to automate data protection in your business. These components connect to a variety of databases via OLEDB, ODBC, and ADO.net SSIS drivers for maximum flexibility of implementation. Your team can leverage their familiarity with SSIS to build a custom solution to meet your exact requirements.
.Net users can leverage our API data masking components to embed their own dynamic data masking capabilities into their code. Hush-Hush components provide role-based access to data, which is controlled in the code via our predefined API, reinforcing our “privacy-in-design” architecture. Our API data masking components also extend the functionality of BizTalk’s HL7 accelerator, EDI, and SSRS with simple code implementations.
Designed for SQL Server DBAs, our SQL Server CLR components provide a powerful extension of SQL Server functionality. These CLR components are accessible from the table-valued functions via Common Language Runtime. They move all the complex string operations into the CLR – where they belong, whilst at the same time performing complex rules.
We believe in leveraging technology to help businesses create a culture of data protection that spans every corner of the business – from the beginning of development to across the supply chain. Our data protection product suite allows you to automate de-identification in your development lifecycle quickly for comprehensive data protection and compliance. Your trial pack include SSIS, CLR, and API components as well as the sensitive data discovery tool.
Used together, our comprehensive discovery and masking suite form an essential part of your development lifecycle, preemptively securing data before it travels throughout your business – and beyond. Automatically map, classify, and de-identify sensitive data regularly to ensure 100% compliance with data privacy legislation and prevent internal and external risks.
Hush-Hush PostgreSQL Data Masking Components greatly extend PostgreSQL functionality and use a simple API to call components from PostgreSQL to create complex automatic rules, giving your DBA more time to focus on more important tasks. Read more about PostgreSql Components on our Wiki.
