In much the same way that banks rely on algorithms to identify potential fraudulent transactions, Oracle today announced it is applying machine learning algorithms to a suite of Oracle Security Operation Center cloud services in a way that makes it easier to identify anomalous user behavior. Andy Smith, senior director of product development for Oracle’s […]
In much the same way that banks rely on algorithms to identify potential fraudulent transactions, Oracle today announced it is applying machine learning algorithms to a suite of Oracle Security Operation Center cloud services in a way that makes it easier to identify anomalous user behavior.
Andy Smith, senior director of product development for Oracle’s security portfolio, says the Oracle Identity Cloud Services is being given Adaptive Access capabilities that track login attempts. Over time, each login is then rated in real time using a analytics application developed by Oracle. Machine learning algorithms can then be deployed in either a supervised or unsupervised mode using an Oracle Cloud Access Security Broker (CASB) service that compares subsequent login attempts against a baseline of login attempts to assess the likelihood a cybercriminal is attempting to breach an application using stolen credentials.
Oracle also revealed today that it has integrated CASB with the Blue Coat secure web gateway for managing cloud access. Previously, CASB already added support for security platforms from Check Point Software, Palo Alto Networks, Fortinet and Sophos.
Finally, Oracle today announced that CASB has been integrated with Oracle’s own portfolio of software-as-a-service (SaaS) applications in addition to Salesforce, Microsoft Office365, Box, Google G-Suite, ServiceNow, AWS, GitHub and Rackspace cloud services.
Smith says the supervised mode is compelling because it affords security professionals the opportunity to train CASB to identify potential threats at a time when there is a chronic shortage of trained IT security professionals.
“In the past, we were only able to provide an unsupervised capability,” says Smith. “Now those algorithms can be trained to look for something specific.”
Via this update, Oracle is essentially helping organizations to reduce their risks by relying more on machines to identify potential threats. Like most new employees, it may take some time for those machines to come up to speed on a new environment. But those machines do come with the added benefit of never forgetting what they have learned, nor one day leaving because they got a better offer somewhere else.
Michael Vizard is a seasoned IT journalist, with nearly 30 years of experience writing and editing about enterprise IT issues. He is a contributor to publications including Programmableweb, IT Business Edge, CIOinsight and UBM Tech. He formerly was editorial director for Ziff-Davis Enterprise, where he launched the company’s custom content division, and has also served as editor in chief for CRN and InfoWorld. He also has held editorial positions at PC Week, Computerworld and Digital Review.
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