This post was originally published here by Rich Campagna.
It should come as no surprise that Higher Education, like every other industry, is increasingly turning to Cloud Access Security Brokers (CASBs) like Bitglass to protect sensitive data in the move to the cloud. After all, these organizations are adopting cloud applications more aggressively than most industries, enticed by very attractive licensing from the big cloud productivity platform vendors like Google and Microsoft.
From my discussions with customers and prospects in this vertical, there are three types of sensitive content that these organizations want to protect:
- Student Personally Identifiable Information (PII) – large universities have 10’s of thousands of students and a lot of sensitive personal information on all of them (and on their faculty).
- Protected Health Information (PHI) – every university with a medical school is subject to HIPAA compliance and an obligation to protect PHI.
- Intellectual Property (IP) – in research universities, a lot of intellectual property is developed, both solely, and in partnership with industry. Licensing this IP can become a very valuable revenue stream for the university and its partners.
Of these three types of data, the third one (IP) is typically most difficult to identify and to protect. This data takes a wide range of different forms – from software source code to financial data, and every other form imaginable. Subject matter is equally as varied as the data formats. This is exactly the type of data that has been most difficult for traditional DLP systems to characterize and detect.
At the same time, the data must be shared and accessible to a wide range of stakeholders – researchers, collaborators from industry, peers at other universities, and more. This means that in addition to the content, the context by which data is being accessed is equally important in making an informed decision. Managed vs unmanaged devices? Appropriate internal and external collaborators? IP in development or in commercialization phase? Etc.
These challenges and more are why I’ve been excited to see our team execute on great leaps forward from traditional DLP. Leveraging new, machine learning approaches to content/context identification, we are working in close collaboration with universities, and with customers in other industries to solve what, until now, has remained unsolved. Bitglass has not only adapted DLP to the cloud, but has also moved it to the top of the class.
Photo:Fortinet Blog
