Page 21 - FCW, September/October 2019

Unsupervised machine learning lets
the technology develop an understanding of how the network’s users typically behave and alert administrators when something abnormal occurs.
Learn more at Carah.io/Cyber-Micro-Focus
challenges. Agencies still rely on a lot of mainframe capabilities that are costly to maintain. In addition, those mainframes run on COBOL, a very robust but very old language. The workforce that supports
it is retiring from government, and new employees don’t have experience with COBOL.
Those legacy systems run mission-critical government functions, so we need to find
a way to secure them and make them cost- effective. Moving legacy systems to the cloud can reduce the annual maintenance cost of some systems by as much as 90 percent and can enable organizations to take advantage of innovative new technologies. For example, some commercial clouds have machine learning and analytics built in.
Micro Focus has developed technologies and capabilities that enable
organizations to take a low-risk approach to moving their COBOL-based mainframe applications into the cloud. By modernizing in that way, agencies experience the same availability, the same or even better performance, and improved security.
Rob Roy is public sector CTO at Micro Focus Government Solutions.
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