As big data gets bigger and more enterprises adopt the cloud, many companies lose track of where sensitive data is located, while some don’t even realize that it’s there at all… thus, they can’t take appropriate measures to protect it. It’s this sensitive data inadvertently left in the clear that causes the biggest pains for companies when they get audited or experience a breach.

DgSecure Detect enables you to discover, count, and report on sensitive data assets in real time wherever they live or move across data repositories, on premises and in the cloud. It’s a precise, highly scalable, resilient, and customizable solution, finding and summarizing sensitive data at the element level.

It’s easy to use our pre-defined templates for sensitive data types to quickly build security policies, and build your own customized sensitive data elements through a sophisticated regular expression (regex) pattern builder. DgSecure Detect then combs through structured, semi-structured, or unstructured content (across databases, Hadoop, Teradata, Cassandra, NoSQL, files, and SharePoint), and finds sensitive data, such as credit card numbers, Social Security Numbers, names, email addresses, medical IDs, ABA bank routing numbers, and financial codes.


In 2015, there were a total of 781 data breaches in the United States alone involving 169,068,506 personal records. – Identity Theft Resource Center 2016

For retail and consumer organizations, damage to brand/reputation caused by cyberattacks was up 72% in 2015. – PwC, The Global State of Information Security Survey 2016

In the United States, 88% of CEOs are somewhat or extremely concerned about cyber threats. – PwC 2016 US CEO Global Survey


  • Handles high volumes of disparate, constantly moving, and changing data with time stamping to support incremental change and life cycle management.
  • Supports a fluid or flexible information governance model that has a mix of highly “invested” (curated) data as well as raw, unexplored (gray) data such as IoT (Internet of Things) data, clickstreams, feeds, and logs.
  • Handles a variety of data stores such as traditional relational databases and enterprise data warehouses as well as non-relational big data sources (Hadoop) and file repositories (SharePoint and file shares).
  • Processes structured, semi-structured, and unstructured or free-form data formats.
  • Provides automated detection and processing of a variety of file formats and file/directory structures, leveraging meta-data and schema-on-read where applicable.
  • Provides deep content inspection using techniques such as patent-pending neural-like network (NLN) technology, and dictionary-based and weighted keyword matches to detect sensitive data more accurately.