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Enterprises aim for cloud security and privacy

In this week's blogs, analysts explore cloud security and privacy, service desk improvements and machine learning for anomaly detection.

According to Jon Oltsik, an analyst with Enterprise Strategy Group Inc. (ESG), in Milford, Mass., 67% of enterprise organizations are in the process of adopting cloud computing, with 66% using at least one software as a service application. At the same time, new ESG research suggests that as much as 60% of enterprise cybersecurity professionals are worried about the steps they need to take -- regarding orchestration, automation and staff levels -- to achieve their cloud security and privacy goals.

In fact, Oltsik writes, many large companies find cloud security and privacy goals hardest to meet within the first six to 12 months after they transition to the cloud. To meet the enterprise need for cloud security and privacy, many organizations rely on data analysis tools from vendors, such as Trend Micro, Illumio, Splunk and These new tools can help "network huggers" accustomed to Layer 3 and Layer 4 packet filtering to adjust their sights for the more distributed world of cloud security and privacy in new networks, Oltsik says.

Check out more of Oltsik's thoughts on cloud security.

Service desk automation grows in popularity

Dennis Drogseth, an analyst with Enterprise Management Associates Inc. (EMA), in Boulder, Colo., has closely followed trends in IT service management. So it's no surprise that he shares the results of an EMA survey of 270 respondents that indicates many professionals would like to see their service desks transformed from today's "reactive, low-tech bastion of ineffective customer interaction" to something more proactive and dynamic -- through a combination of automation and analytics.

In his recent blog post, Drogseth identifies a number of automation priorities to improve service desk performance. Among these priorities are run books for IT process automation, systems configuration automation, network and storage configuration, and application provisioning. Additionally, Drogseth recommends mobile and endpoint configuration, assimilated cloud resources, and an attention to workflow.

Read more of Drogseth's thoughts on service desk automation.

Niara uses machine learning to spot network anomalies

In a recent post, Drew Conry-Murray, a blogger with Packet Pushers, explores the potential of machine learning for network security. Niara Inc., a two-year-old startup based in Sunnyvale, Calif., uses machine learning to process security logs, flows and network packets to find anomalies that might flag as malicious activity. Niara's technology comes among a spate of similar efforts from suppliers such as Cisco, which recently announced plans of its own to acquire Lancope for its anomaly detection software.

According to Conry-Murray, Niara's system is founded on a Hadoop-based analyzer. The analyzer correlates data from a wide range of sources, including intrusion detection systems, firewalls, Splunk, SIEM and Web proxies. To spot anomalies, the system correlates the data to provide context, performing such tasks as linking IP addresses to authorized users. As data is analyzed, Niara creates profiles, which track the risk users, devices and applications might pose to organizations.

Alerts are sent if a risk exceeds a threshold. In the event the alert is a false positive, IT staff can feed that information back to Niara, in the process enhancing Niara's ability to better understand the organization's traffic flows.

Read more of Conry-Murray's thoughts on Niara.

Next Steps

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