2017 cybersecurity trends at the Black Hat conference
This week, bloggers look into 2017 cybersecurity trends leading up to the Black Hat conference, Movidius deep learning and Mist's approach to WLAN.
Jon Oltsik, an analyst at Enterprise Strategy Group in Milford, Mass., discussed this week's Black Hat conference, based on his research into 2017 cybersecurity trends. Among the leading 2017 cybersecurity trends, Oltsik cited machine learning and artificial intelligence. According to Oltsik, only 30% of cybersecurity professionals consider themselves "very knowledgeable" about machine learning. He recommends vendors be upfront about the capabilities of these emerging technologies and do a better job of educating the market.
Other 2017 cybersecurity trends include automation and orchestration, security operations and analytics platform architecture (SOAPA) and threat intelligence -- the understanding of emerging threats that is traditionally at the heart of Black Hat. According to Oltsik, 21% of organizations consider creating a SOAPA architecture a top priority. Nevertheless, Oltsik describes automation and orchestration as "probably the hottest cybersecurity technology category today." With more than 45% of organizations struggling with cybersecurity headcount, these groups have two choices -- automate or increase the efficiency of staff members.
Explore more of Oltsik's thoughts on 2017 cybersecurity trends.
Movidius premieres new AI and deep learning offering
Greg Ferro, writing in Ethereal Mind, examined a new release from Intel subsidiary, Movidius. The vendor is launching a Neural Compute Stick, which it first demonstrated at CES 2017. Described as an "AI accelerator" on a USB drive, the product is intended to boost Intel's deep learning and AI capabilities and play off Movidius' 2016 launch of Fathom NCS. The ultimate goal for the product is to provide developers with a low-power option for developing offline AI applications.
In Ferro's view, AI computation for as little as $80 at retail might help to improve network telemetry and path selection. He sees potential for the product to improve path forwarding or FPGA for hardware control, as well as to create strong reaction loops for malware detection. Ferro cautions that Intel does not always succeed with its device launches, adding that many network vendors struggle with basic Java or C code and may struggle even more with AI applications. He added that a standards body would be needed to achieve interoperation.
Dig deeper into Ferro's thoughts on Movidius NCS.
Mist approaches WLAN with machine learning
Lee Badman, blogging in Wirednot, offered his assessment of WLAN vendor Mist Systems. "If you're not careful, their story can sound like another one of the many from network vendors where terms like AI and machine learning are bandied about like the Buzzword Flavors of the Month. But Mist was talking this language well ahead of the current curve. "When it comes to its machine learning capabilities, Badman sees Mist as the "real deal," although he recognizes that the company is a latecomer to the WLAN marketplace, going up against big competitors.
According to Badman, Mist has a powerful user interface that places significant -- but not overwhelming -- amounts of information in front of WLAN administrators through a cloud interface. He also praised the company's virtual BLE beacon support, analytics similar to what Nyansa offers and the elimination of controllers.
Can Mist measure up? Read more of Badman's thoughts on Mist.
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