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Keeping Your Data and Applications Safe in the Cloud

When cloud-based services first became popular more than a decade ago, business leaders embraced their versatility, scalability and predictable costs, but many did so with a sense of unease: was their valuable data truly safe when housed in an offsite data center to which they had no physical access? Would third-party cloud service providers offer adequate security controls to guard against the increasing sophistication and frequency of threats? 


To IT professionals, it seemed that the very features that made cloud computing attractive—its use of shared resources and the speed and flexibility with which workloads could be created or modified—were at odds with traditional best practices in network security.


Although these concerns have not vanished, organizations today are adopting cloud-based technologies and applications at an increasingly rapid pace. In fact, more than 80% of organizations today store data in the public cloud, and industry leaders predict that 83% of workloads will move to the cloud by 2020. 


In this environment, cloud security is becoming an increasing concern—and top priority—across nearly all industry verticals.


The Challenges: Visibility and Infrastructure Complexity

Today’s cloud-based infrastructure environments are extraordinarily complex. In a recent survey conducted by Forrester Research, more than 85% of companies described themselves as employing a multi-cloud strategy, meaning that they rely on various public and private clouds for different application workloads. Each environment, whether public, hybrid or dedicated, comes with its own unique set of security challenges.


With private cloud solutions, enterprise customers are responsible for all aspects of the security of their data, infrastructure and physical network. In public clouds, the vendor assumes responsibility for securing the physical infrastructure and hypervisor, while the tenants must secure their own virtual networks, applications, access management systems and data. When resources or applications are delivered as a service, the vendor assumes responsibility for most aspects of their platform’s security, but the customer retains ownership of their data, and responsibility for how the applications are used.


As businesses more increasing numbers of workloads to these intricately complex environments, they tend to lose visibility into their deployments. It can be difficult to access log data from the public cloud, or to obtain it in as much detail as can be gathered on-premises. It can also be challenging to correlate anomalies in this data with patterns found in on-premise data or in data collected from roving endpoints.


Leveraging the Cloud’s Computing Power to Find Solutions

Among the chief advantages offered by the cloud computing model are ubiquity and scalability. If your organization needs a great deal of processing power to solve a complex problem involving a very large amount of data, you can turn to a cloud-based high-performance computing infrastructure for as long as you need the resource, no matter where you’re located.


And, in fact, deriving actionable intelligence from the network security event logs generated in today’s complex multi-cloud computing environments is exactly this sort of complex computational problem. To solve it quickly enough to speed up the identification, containment and elimination of threats requires high-volume, high-velocity data processing. 

Before the advent of cloud computing, many organizations relied on network-based tools such as Security Information and Event Management (SIEM) solutions to perform this sort of data analysis. Although SIEM still remains a critical component in comprehensive and holistic IT security toolkits, first-generation SIEMs haven’t evolved to keep pace with the expansion of the attack surface and increasing complexity of systems. 


To maximize the security of your organization’s data and applications in the cloud, look for a next-generation solution that can integrate raw streaming data with logs from all devices in the local ecosystem and all services and processes in the cloud for deep analysis. This requires powerful and elastic computing resources that can handle billions of events per second along with contextual information. 


Relying on Behavioral Analytics for Speed and Efficiency

The correlation is simple: the faster your organization can identify and contain threats, the lower the risk of a breach. 


But the process of detecting and remediating threats is highly complex. No matter how deep the talent and expertise of your security personnel—and regardless of whether they’re an internal resource or sourced externally—humans are becoming increasingly incapable of monitoring incoming threats in real time. The sheer volume of data is simply too great.


Maintaining an always-on 24/7/365 Security Operations Center (SOC) housing a team of experts available for monitoring, analysis and incident response can help ensure the rapid detection of intrusions and risks. But SOC teams often face an unmanageable volume of false-positive threat alerts, and must spend too much of their limited time distinguishing real incidents from incorrectly-flagged ones.


This complexity can be substantially reduced by incorporating AI and machine learning into cybersecurity incident response protocols. By incorporating advanced network and user behavioral analytics and predictive threat modeling, today’s most advanced solutions can generate alerts with much more accuracy. This means that SOC teams can focus their attention on the incidents that most warrant it, dramatically improving their effectiveness and efficiency.


And because such advanced behavioral analytics platforms are built with real-time unsupervised and semi-supervised learning capabilities, their performance improves over time. The longer they’re in use, the lower the overall volume of alerts, and the greater the accuracy of each individual alert. Their “intelligence” also allows these systems to combat insider threats, identify dangerous user errors, and prevent previously undiscovered zero-day exploits—threats that can’t otherwise be anticipated.


Taking Advantage of Global Threat Intelligence

Traditional signature-based endpoint protection providers collected their own internal repositories of known threat data. This enabled them to update their products reactively to guard against malware after it had been identified and cataloged—a process with inherent lag time.


Today’s most advanced protection platforms rely on threat information from a much broader array of sources, and they can access this information more quickly. Drawing upon governmental, institutional, commercial, crowd- and open-sourced threat intelligence, they can compare anomalies in network behavior and user activities with data derived from an up-to-date overview of the global threat landscape. The result is an increase in the speed and accuracy of known threat detection.


As more and more organizations migrate increasing quantities of data and resources to the cloud, it’s becoming an increasingly appealing target for attackers. But the very features of cloud computing that make it so attractive to businesses—its global accessibility, efficiency, and availability of the processing power needed to solve complex computational problems—can be used to help cybersecurity teams work more efficiently and smarter.


To learn more about how Netswitch’s Secureli Advanced Threat Protection platform relies on advanced behavioral analytics to keep your cloud-based resources safe in an ever-changing threat landscape, contact us today. 

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