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A workload can refer to everything and anything sitting on top of a machine. For example, in the case of a piece of physical hardware, an operating system (OS) or the applications and data running on top of it would be considered the workloads. When speaking about cloud computing, a workload refers to an app or data that’s being transferred to or from the cloud, users, endpoints, data centers, and so on.
The cloud offers enormous opportunities, but it’s also increased vulnerability to cyberattacks such as malware. Cybercrime in general has surged as workloads have increasingly moved onto the cloud. Data has become more mobile, empowering businesses to work in a more agile way, but that puts information and business systems at risk.
Security strategies have evolved to focus on endpoint protection in a response to the growing enthusiasm for mobile and remote working. However, these technologies tend to miss what’s happening in the cloud. Any business using multiple public or private clouds need to protect themselves at the workload level, not just the endpoints.
Workload segmentation automatically builds a real-time application topology and dependency map down to the process level. It then highlights the required application paths and compares them to the total available network paths, recommending policies to minimize the attack surface.
This can be a core protection strategy for workloads because it eliminates the excessive access allowed by flat networks. Such networks allow attackers to move laterally and compromise workloads in cloud and data center environments. Segmenting, or isolating, applications and eliminating unnecessary pathways will contain any potential compromises to the affected asset, essentially reducing the “blast radius.”
Segmenting applications and workloads—also known as microsegmentation—allows you to create intelligent groups of workloads based on characteristics of the workloads communicating with each other. As such, microsegmentation is not reliant on dynamically changing networks or the business or technical requirements placed on them, which means it enables both stronger and more reliable security.
Cloud workload segmentation allows you to apply quantifiable metrics to your cloud infrastructure and cloud network security by implementing:
Software Identity-Based Protection
Workload segmentation looks beyond network IP addresses to verify the secure identity of the communicating application software and workloads in public or private clouds, hybrid clouds, multicloud deployments, on-premises data centers, or container environments. Learn why identity is foundational for cloud workload protection.
Workload segmentation uses machine learning to automate the entire policy life cycle for microsegmentation and workload protection. There’s no need to build policy manually during deployment or ongoing operations. Modern workload segmentation platforms can even recommend new or updated policies when apps change or are added.
Adaptability for Updates and Changes
Cloud workload segments are based on the identity of communicating software, not the network itself. This means segments can adjust as new applications and hosts are added, verified, and permitted to communicate. The result: hardened security minus operational burden and complexity.
Plenty of complications can stand in the way of gaining greater protection for your cloud workloads. Let’s briefly go over some of those.
Given the prevalence of the above challenges, cloud workload segmentation can seem difficult. However, with the right platform and a quality security partner, you can ensure the apps and data traveling to and from your clouds are kept secure by closing off their exposure to the internet. In the next section, we’ll discuss key traits you should be on the lookout for when shopping for a workload segmentation solution.
A cloud- and mobile-ready workload segmentation platform:
An effective cloud workload segmentation platform should build policies based on the identity of applications, hosts, and services communicating in your cloud, not the network environment. This lets you avoid the operational complexity of trying to determine application dependencies, learn where each host is located, or monitor thousands of data points.
Cloud architectures aren’t fit for traditional security tools that use IP addresses, ports, and protocols as the control plane. Proper cloud workload segmentation should cryptographically fingerprint software based on immutable properties that attackers can’t exploit for consistent workload protection across all networking environments.
Continually Assesses Risk
A modern cloud workload segmentation solution can automatically measure your visible network attack surface to understand how many possible application communication pathways are in use, quantify risk exposure based on the criticality of communicating software, and use machine learning to recommend the fewest possible security policies.
There are various segmentation methods that can help your workloads stay secure as they enter and leave the cloud.
Network segmentation is the division of a network into multiple subnetworks—each with subnet-specific security policies and protocols—to attempt to prevent lateral movement. It’s one of the most widely used means of reducing a network's attack surface to combat cyberattacks, however, with the increase in mobility offered by the cloud, traditional network segmentation has been rendered ineffective at preventing lateral threat movement.
Microsegmentation uniquely identifies each resource (e.g., server, application, host, user), which allows organizations to configure permissions that provide fine-grained control of data traffic, enabling them to prevent lateral movement of threats, workload compromise, and data breaches.
Zero Trust Workload Segmentation
In a zero trust cybersecurity model, a company could set up a policy, for example, that states a particular application running on a host can only talk to other application software running on other hosts. For instance, all PCI-related software can be microsegmented to tightly control access to the PCI environment and reduce the number of systems in scope.
Using a cloud-based, zero trust approach to secure connections between users and applications based on business policies, without connecting them to the corporate network—an approach known as zero trust network access (ZTNA)—delivers stronger security in public clouds and data centers.
ZTNA connects users directly to applications on a one-to-one basis, never to the network, eliminating lateral movement. This lets you achieve segmentation in a fundamentally different and more effective way that’s impossible with legacy VPNs and firewalls.
Plus, if a device or workload moves, the security policies and attributes move with it. By applying zero trust segmentation rules down to the workload or application, IT can reduce the risk of an attacker moving from one compromised workload or application to another.
When it comes to zero trust segmentation, only one cloud-based security vendor builds and innovates its platform with zero trust principles at the core—that vendor is Zscaler.
Zscaler Workload Segmentation (ZWS) simplifies microsegmentation by automating policy creation and management while protecting your applications and workloads in data centers and cloud environments. With one click, our cloud native platform reveals risk across your organization and applies identity-based protection to workloads—without any changes to your network.
Its software identity-based technology provides gap-free protection with policies that automatically adapt to environmental changes. In short, ZWS optimizes attack surface elimination.
ZWS begins by mapping the application communication topology using machine learning, a process that takes about 72 hours (vs. months to do so manually).
Once complete, Zscaler can measure the total network paths available and the application paths your business applications require. Typically, only a fraction of the existing pathways is required. All unnecessary communications paths can be eliminated to reduce your attack surface.
ZWS supports a variety of use cases, including: