Frequently Asked Questions (FAQs)
Dataguise delivers a unique approach to discovering and protecting sensitive and personal data assets in many different platforms both on premises and in the cloud. The data discovery is foundational, scaling both horizontally and vertically and scanning multiple Petabytes of data, across different repositories and versions. Data discovery leads to data-centric protection that can be done via intelligent masking and/or encryption.
Dataguise customers span a broad range of industries—financial services, insurance, healthcare, government, technology, and retail—and include some of the world’s largest, industry-leading companies. We work with organizations that embrace the tremendous potential of data and are committed to being responsible data stewards.
The fundamental goal that Dataguise helps organizations achieve is to minimize risk and costs as they store and use personal information and other sensitive data to drive business growth.
The Dataguise discovery process begins by defining a policy, which allows organizations to select which sensitive elements they need to discover. The rest of the process is automated and the scanning itself is agentless, via remote components called Intelligent Data Processors (IDPs) that can scan both data at rest and in motion. The discovery process leverages a combination of pattern matching, contextual analysis, data profiling, full or fuzzy reference data matching, unsupervised and unsupervised machine learning. Obviously not every type of sensitive element requires all these methods, and it’s the complexity of the underlying sensitive type that determines exactly how many of these heuristics will be leveraged. Also, for optimal efficiency, each platform and object type supported by Dataguise has a native connector built for it.
There are 2 main reasons that make data-centric security imperative.
- The infrastructure level security is necessary but not sufficient. This is because safeguarding the infrastructure and repositories from external and internal forces is never foolproof and once penetrated, often leaves the data exposed. Hence proper optics into the sensitivity of data enables proper controls to be implemented at the data level
- To unlock the utility and usefulness of data, it has to be shared with the authorized users. Data sharing policies define whether or not, based on the sensitivity of the information, data can be fully shared by itself, or if it has to be masked. This means scanning and protecting data at the data-level, to enable dev/test or analytics.
Dataguise discovery and protection products are designed to maximize ease of use, simplicity, and automation. Installation and configuration take only a few minutes and the product can be operational and produce reports and protected data in the same time. The product can be easily operated by web-based UI or orchestrated via any of the popular workflow solutions.
Dataguise prides itself in building a technology that can scale to scan and protect multiple petabytes of raw data. Metadata however, although not necessary, may play an important role in either assisting in scanning performance or by helping ambiguous data elements to be correctly classified with the right semantic definition. Dataguise can generate metadata as a result of its scanning process, or if there is some metadata that already exists, it can use it in the ways mentioned above.