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  • April 28, 2015
  • News

Automated unstructured data classification, protection

TITUS has released Classification Suite 4 of its data identification and information protection suite. The company explains the new version uses content and context to automatically classify and protect information as users handle it, and allows manual and guided classification for maximum flexibility and user engagement. Fine-grained policy control and comprehensive metadata capture also leverage overall security investment, improve data management and increase regulatory compliance.

Administrators can set up policies, for example, to classify e-mail based on recipients; protect e-mail based on the content or classification of attachments; classify and protect documents based on content, filename or location; or prevent printing of sensitive documents to non-secure printers. Customizable alerts warn users of special information handling conditions or possible impending security violations.

Classification Suite 4 also integrates with data loss prevention (DLP) solutions, allowing enterprises to optimize security policy, focus on high-risk areas and capture retention-related metadata for informed archiving or deletion.

TITUS says with Classification Suite 4, enterprises can:

  • identify unstructured data—identify the business value of unstructured data at the time of creation, so an organization knows what data it has and how it should be protected;
  • raise security awareness—automatically add visual markings and handling instructions to e-mail and documents to raise user awareness about the sensitivity and business value of corporate information;
  • engage users—promote a culture of security by making users aware of their responsibilities and provide targeted, interactive education so that security becomes everyone’s responsibility;
  • apply content protection—apply persistent protection to e-mail and documents with Microsoft Rights Management Services (RMS) and S/MIME;
  • optimize DLP and other security solutions—increase the accuracy and effectiveness of data loss prevention solutions with data classification;
  • improve data management—capture retention-related metadata as users create and send information, so that an organization can make informed decisions about archiving, storage, retention and deletion;
  • reduce mobile risks—mitigate the risks of mobile information sharing by extending the benefits of classification to the mobile workforce; and
  • uncover insider threats—report on user behavior to detect policy education gaps and inadvertent or malicious insider activity.

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