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Edge computing, cloud data centers drive decentralized IT

This week, bloggers explore the growth of decentralized IT, thanks to edge computing; Infoblox DNS security updates; and how machine learning is becoming a feature, rather than a product.

Eamon McCarthy Earls
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Published: 11 Apr 2018

Chris Drake, an analyst with GlobalData in Sterling, Va., said edge computing is primed to change enterprise data center workloads and drive a shift to decentralized IT. Edge computing includes resources such as storage, compute and networking that are closer to data-generating devices and end users. As decentralized IT gains traction, edge computing environments will vary considerably.

Although decentralized IT -- and the decentralization of data centers -- is an aspect of edge computing, these systems are likely to operate as an added tier of processing, security and storage, rather than replace data centers and cloud-based architecture.

According to Drake, enterprise internet-of-things (IoT) initiatives are a driving factor in the growth of edge computing. Vendors are introducing dedicated edge servers, micro data centers, more hyperconverged infrastructure and IoT gateways to support the needs of remote office deployments. Vendors will need to decide between purpose-built and do-it-all systems.

"Expect to see important progress over the next year among leading IT vendors such as Cisco, IBM, Nutanix, and VMware as they strive to capture this market through new marketing campaigns, investments, and technology innovations," Drake wrote.

Explore more of Drake's analysis of decentralized IT.

Infoblox updates its DNS security

Drew Conry-Murray, writing in Packet Pushers, reviewed Infoblox's moves to update its ActiveTrust Cloud domain name server, or DNS, security service. The product is available as a SaaS offering, aimed at enforcing web policies and blocking DNS attacks.

Machine learning is now part of the package to spot malicious behavior and prevent data exfiltration. The offering integrates with other DNS, IP address management and Dynamic Host Configuration Protocol systems from Infoblox.

According to Conry-Murray, the product's new features allow companies to block access to prohibited websites, such as pornography or gambling sites. Based in the cloud, the service analyzes DNS queries, keeping a running list of malicious domains. For the time being, Infoblox has not announced pricing.

Read more of Conry-Murray's assessment of the Infoblox announcement.

Is machine learning a product or a feature?

Jon Oltsik, an analyst with Enterprise Strategy Group in Milford, Mass., said, in 2010, security analytics systems began to incorporate big data technologies to find "needles in growing haystacks" of security data. In the eight years since, Oltsik said these technologies morphed into machine learning, resulting in the security category of user and entity behavior analytics (UEBA), which monitors logins, network connections and remote access for anomalies.

RSA's recent acquisition of UEBA vendor Fortscale highlights how the market is now evolving, Oltsik said. RSA plans to incorporate Fortscale's machine learning within its NetWitness product, reflecting a trend where machine learning is viewed as a feature, rather than a stand-alone product.

Other companies like Splunk and Hewlett Packard Enterprise have made similar acquisitions.

At the same time, machine learning is now incorporated in security tools such as network behavior analytics systems from Darktrace, Vectra and Palo Alto, as well as among endpoint security offerings.

"The intersection between artificial intelligence and security technology is still in its genesis phase and we are in a cycle of massive innovation right now, driven by cloud computing, open source, big data technologies, artificial intelligence, etc.," Oltsik wrote. "Given this, CISOs [chief information security officers] should remain open-minded about new types of more revolutionary security technologies that aren't simple adjuncts to what they've done in the past," he added.

Dig deeper into Oltsik's thoughts on machine learning.

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