Using vendor-neutral platforms to host, connect, and secure edge applications and workloads is the best practice for ensuring the scalability and flexibility of financial edge architectures. Moving away from dedicated device stacks and taking a “platformization” approach allows financial institutions to easily deploy, update, and swap out applications and capabilities on demand. Vendor-neutral platforms help reduce hardware overhead costs to deploy new branches and allow banks to explore different edge software capabilities without costly hardware upgrades.

Additionally, using a centralized, cloud-based edge management and orchestration (EMO) platform is the best practice for ensuring remote teams have holistic oversight of the distributed edge computing architecture. This platform should be vendor-agnostic to ensure complete coverage over mixed and legacy architectures, and it should use out-of-band (OOB) management to provide continuous remote access to edge infrastructure even during a major service outage.

How Nodegrid streamlines edge computing for the banking industry

Nodegrid is a vendor-neutral edge networking platform that consolidates an entire edge tech stack into a single, cost-effective device. Nodegrid has a Linux-based OS that supports third-party VMs and Docker containers, allowing banks to run edge computing workloads, data analytics software, automation, security, and more. 

The Nodegrid Gate SR is available with an Nvidia Jetson Nano card that’s optimized for artificial intelligence workloads. This allows banks to run AI surveillance software, ML-powered recommendation engines, and AIOps at the edge alongside networking and infrastructure workloads rather than purchasing expensive, dedicated GPU resources. Plus, Nodegrid’s Gen 3 OOB management ensures continuous remote access and IMI for improved branch resilience.

Get Nodegrid for your edge computing use cases in banking

Nodegrid’s flexible, vendor-neutral platform adapts to any use case and deployment environment. Watch a demo to see Nodegrid’s financial network solutions in action.

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The goal of AI orchestration is to provide a single, unified platform for teams to oversee and manage AI-related workflows across the entire organization. This post describes the ideal AI orchestration solution and the technologies that make it work, helping companies use artificial intelligence more efficiently.

AI challenges to overcome

The challenges an organization must overcome to use AI more cost-effectively and see faster returns can be broken down into three categories:

1. Overseeing AI-led workflows to ensure models are behaving as expected and providing accurate results, when these workflows are spread across the enterprise in different geographic locations and vendor-specific applications.
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2. Efficiently provisioning, maintaining, and scaling the vast infrastructure and computational resources required to run intensive AI workflows at remote data centers and edge computing sites.
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3. Maintaining 24/7 availability and performance of remote AI workflows and infrastructure during security breaches, equipment failures, network outages, and natural disasters.

These challenges have a few common causes. One is that artificial intelligence and the underlying infrastructure that supports it are highly complex, making it difficult for human engineers to keep up. Two is that many IT environments are highly fragmented due to closed vendor solutions that integrate poorly and require administrators to manage too many disparate systems, allowing coverage gaps to form. Three is that many AI-related workloads occur off-site at data centers and edge computing sites, so it’s harder for IT teams to repair and recover AI systems that go down due to a networking outage, equipment failure, or other disruptive event.

How AI orchestration streamlines AI/ML in an enterprise environment

The ideal AI orchestration platform solves these problems by automating repetitive and data-heavy tasks, unifying workflows with a vendor-neutral platform, and using out-of-band (OOB) serial console management to provide continuous remote access even during major outages.

Automation

Automation is crucial for teams to keep up with the pace and scale of artificial intelligence. Organizations use automation to provision and install AI data center infrastructure, manage storage for AI training and inference data, monitor inputs and outputs for toxicity, perform root-cause analyses when systems fail, and much more. However, tracking and troubleshooting so many automated workflows can get very complicated, creating more work for administrators rather than making them more productive. An AI orchestration platform should provide a centralized interface for teams to deploy and oversee automated workflows across applications, infrastructure, and business sites.

Unification

The best way to improve AI operational efficiency is to integrate all of the complicated monitoring, management, automation, security, and remediation workflows. This can be accomplished by choosing solutions and vendors that interoperate or, even better, are completely vendor-agnostic (a.k.a., vendor-neutral). For example, using open, common platforms to run AI workloads, manage AI infrastructure, and host AI-related security software can help bring everything together where administrators have easy access. An AI orchestration platform should be vendor-neutral to facilitate workload unification and streamline integrations.

Resilience

AI models, workloads, and infrastructure are highly complex and interconnected, so an issue with one component could compromise interdependencies in ways that are difficult to predict and troubleshoot. AI systems are also attractive targets for cybercriminals due to their vast, valuable data sets and because of how difficult they are to secure, with HiddenLayer’s 2024 AI Threat Landscape Report finding that 77% of businesses have experienced AI-related breaches in the last year. An AI orchestration platform should help improve resilience, or the ability to continue operating during adverse events like tech failures, breaches, and natural disasters.

Gen 3 out-of-band management technology is a crucial component of AI and network resilience. A vendor-neutral OOB solution like the Nodegrid Serial Console Plus (NSCP) uses alternative network connections to provide continuous management access to remote data center, branch, and edge infrastructure even when the ISP, WAN, or LAN connection goes down. This gives administrators a lifeline to troubleshoot and recover AI infrastructure without costly and time-consuming site visits. The NSCP allows teams to remotely monitor power consumption and cooling for AI infrastructure. It also provides 5G/4G LTE cellular failover so organizations can continue delivering critical services while the production network is repaired.

Gen 3 OOB also helps organizations implement isolated management infrastructure (IMI), a.k.a, control plane/data plane separation. This is a cybersecurity best practice recommended by the CISA as well as regulations like PCI DSS 4.0, DORA, NIS2, and the CER Directive. IMI prevents malicious actors from being able to laterally move from a compromised production system to the management interfaces used to control AI systems and other infrastructure. It also provides a safe recovery environment where teams can rebuild and restore systems during a ransomware attack or other breach without risking reinfection.

Getting the most out of your AI investment

An AI orchestration platform should streamline workflows with automation, provide a unified platform to oversee and control AI-related applications and systems for maximum efficiency and coverage, and use Gen 3 OOB to improve resilience and minimize disruptions. Reducing management complexity, risk, and repair costs can help companies see greater productivity and financial returns from their AI investments.

The vendor-neutral Nodegrid platform from ZPE Systems provides highly scalable Gen 3 OOB management for up to 96 devices with a single, 1RU serial console. The open Nodegrid OS also supports VMs and Docker containers for third-party applications, so you can run AI, automation, security, and management workflows all from the same device for ultimate operational efficiency.

Streamline AI orchestration with Nodegrid

Contact ZPE Systems today to learn more about using a Nodegrid serial console as the foundation for your AI orchestration platform. Contact Us

This blog describes four edge computing use cases in telecom before describing the benefits and best practices for edge computing in the telecommunications industry.

4 Edge computing use cases in telecom

1. Enhancing the customer experience with real-time analytics

Each customer interaction, from sales calls to repair requests and service complaints, is a chance to collect and leverage data to improve the experience in the future. Transferring that data from customer sites, regional branches, and customer service centers to a centralized data analysis application takes time, creates network latency, and can make it more difficult to get localized and context-specific insights. Edge computing allows telecom companies to analyze valuable customer experience data, such as network speed, uptime (or downtime) count, and number of support contacts in real-time, providing better opportunities to identify and correct issues before they go on to affect future interactions.

2. Streamlining remote infrastructure management and recovery with AIOps

AIOps helps telecom companies manage complex, distributed network infrastructure more efficiently. AIOps (artificial intelligence for IT operations) uses advanced machine learning algorithms to analyze infrastructure monitoring data and provide maintenance recommendations, automated incident management, and simple issue remediation. Deploying AIOps on edge computing devices at each telecom site enables real-time analysis, detection, and response, helping to reduce the duration of service disruptions. For example, AIOps can perform automated root-cause analysis (RCA) to help identify the source of a regional outage before technicians arrive on-site, allowing them to dive right into the repair. Edge AIOps solutions can also continue functioning even if the site is cut off from the WAN or Internet, potentially self-healing downed networks without the need to deploy repair techs on-site.

3. Preventing environmental conditions from damaging remote equipment

Telecommunications equipment is often deployed in less-than-ideal operating conditions, such as unventilated closets and remote cell site shelters. Heat, humidity, and air particulates can shorten the lifespan of critical equipment or cause expensive service failures, which is why it’s recommended to use environmental monitoring sensors to detect and alert remote technicians to problems. Edge computing applications can analyze environmental monitoring data in real-time and send alerts to nearby personnel much faster than cloud- or data center-based solutions, ensuring major fluctuations are corrected before they damage critical equipment.

4. Improving operational efficiency with network virtualization and consolidation

Another way to reduce management complexity – as well as overhead and operating expenses – is through virtualization and consolidation. Network functions virtualization (NFV) virtualizes networking equipment like load balancers, firewalls, routers, and WAN gateways, turning them into software that can be deployed anywhere – including edge computing devices. This significantly reduces the physical tech stack at each site, consolidating once-complicated network infrastructure into, in some cases, a single device. For example, the Nodegrid Gate SR provides a vendor-neutral edge computing platform that supports third-party NFVs while also including critical edge networking functionality like out-of-band (OOB) serial console management and 5G/4G cellular failover.

Edge computing in telecom: Benefits and best practices

Edge computing can help telecommunications companies:

• Get actionable insights that can be leveraged in real-time to improve network performance, service reliability, and the support experience.
• Reduce network latency by processing more data at each site instead of transmitting it to the cloud or data center for analysis.
• Lower CAPEX and OPEX at each site by consolidating the tech stack and automating management workflows with AIOps.
• Prevent downtime with real-time analysis of environmental and equipment monitoring data to catch problems before they escalate.
• Accelerate recovery with real-time, AIOps root-cause analysis and simple incident remediation that continues functioning even if the site is cut off from the WAN or Internet.

Management infrastructure isolation, which is recommended by CISA and required by regulations like DORA, is the best practice for improving edge resilience and ensuring a speedy recovery from failures and breaches. Isolated management infrastructure (IMI) prevents compromised accounts, ransomware, and other threats from moving laterally from production resources to the interfaces used to control critical network infrastructure.

Vendor-neutral platforms help reduce hardware overhead costs to deploy new edge sites, make it easy to spin-up new NFVs to meet increased demand, and allow telecom organizations to explore different edge software capabilities without costly hardware upgrades. For example, the Nodegrid Gate SR is available with an Nvidia Jetson Nano card that’s optimized for AI workloads, so companies can run innovative artificial intelligence at the edge alongside networking and infrastructure management workloads rather than purchasing expensive, dedicated GPU resources.

Streamlined, cost-effective edge computing with Nodegrid

Nodegrid’s flexible, vendor-neutral platform adapts to all edge computing use cases in telecom. Watch a demo to see Nodegrid’s telecom solutions in action.

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Edge computing mitigates many of these challenges by decentralizing cloud and data center resources and distributing them at the network’s “edges,” where most retail operations take place. Running applications and processing data at the edge enables real-time analysis and insights and ensures that systems remain operational even if Internet access is disrupted by an ISP outage or natural disaster. This blog describes five potential edge computing use cases in retail and provides more information about the benefits of edge computing for the retail industry.

5 Edge computing use cases in retail

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1. Security video analysis

Security cameras are crucial to loss prevention, but constantly monitoring video surveillance feeds is tedious and difficult for even the most experienced personnel. AI-powered video surveillance systems use machine learning to analyze video feeds and detect suspicious activity with greater vigilance and accuracy. Edge computing enhances AI surveillance by allowing solutions to analyze video feeds in real-time, potentially catching shoplifters in the act and preventing inventory shrinkage.

2. Localized, real-time insights

Retailers have a brief window to meet a customer’s needs before they get frustrated and look elsewhere, especially in a brick-and-mortar store. A retail store can use an edge computing application to learn about customer behavior and purchasing activity in real-time. For example, they can use this information to rotate the products featured on aisle endcaps to meet changing demand, or staff additional personnel in high-traffic departments at certain times of day. Stores can also place QR codes on shelves that customers scan if a product is out of stock, immediately alerting a nearby representative to provide assistance.

3. Enhanced inventory management

Effective inventory management is challenging even for the most experienced retail managers, but ordering too much or too little product can significantly affect sales. Edge computing applications can improve inventory efficiency by making ordering recommendations based on observed purchasing patterns combined with real-time stocking updates as products are purchased or returned. Retailers can use this information to reduce carrying costs for unsold merchandise while preventing out-of-stocks, improving overall profit margins.
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4. Building management

Using IoT devices to monitor and control building functions such as HVAC, lighting, doors, power, and security can help retail organizations reduce the need for on-site facilities personnel, and make more efficient use of their time. Data analysis software helps automatically optimize these systems for efficiency while ensuring a comfortable customer experience. Running this software at the edge allows automated processes to respond to changing conditions in real-time, for example, lowering the A/C temperature or routing more power to refrigerated cases during a heatwave.

5. Warehouse automation

The retail industry uses warehouse automation systems to improve the speed and efficiency at which goods are delivered to stores or directly to users. These systems include automated storage and retrieval systems, robotic pickers and transporters, and automated sortation systems. Companies can use edge computing applications to monitor, control, and maintain warehouse automation systems with minimal latency. These applications also remain operational even if the site loses internet access, improving resilience.

The benefits of edge computing for retail

The benefits of edge computing in a retail setting include:
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For example, the Nodegrid Gate SR integrated branch services router delivers an entire stack of edge networking, infrastructure management, and computing technologies in a single, streamlined device. The open, Linux-based Nodegrid OS supports VMs and Docker containers for third-party edge computing applications, security solutions, and more. The Gate SR is also available with an Nvidia Jetson Nano card that’s optimized for AI workloads to help retail organizations reduce the hardware overhead costs of deploying artificial intelligence at the edge.

Consolidated edge computing with Nodegrid

Nodegrid’s flexible, scalable platform adapts to all edge computing use cases in retail. Watch a demo to see Nodegrid’s retail network solutions in action.

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Edge computing deploys data analytics applications and computing resources around the edges of the network, where much of the most valuable data is created. This significantly reduces latency and mitigates many security and compliance risks. In a healthcare setting, edge computing enables real-time medical insights and interventions while keeping HIPAA-regulated data within the local security perimeter. This blog describes six potential edge computing use cases in healthcare that take advantage of the speed and security of an edge computing architecture.

6 Edge computing use cases in healthcare

Edge computing use cases for EMS

Mobile emergency medical services (EMS) teams need to make split-second decisions regarding patient health without the benefit of a doctorate and, often, with spotty Internet connections preventing access to online drug interaction guides and other tools. Installing edge computing resources on cellular edge routers gives EMS units real-time health analysis capabilities as well as a reliable connection for research and communications. Potential use cases include:
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Edge computing use cases in hospitals & clinics

Hospitals and clinics use IoT devices to monitor vitals, dispense medications, perform diagnostic tests, and much more. Sending all this data to the cloud or data center takes time, delaying test results or preventing early intervention in a health crisis, especially in rural locations with slow or spotty Internet access. Deploying applications and computing resources on the same local network enables faster analysis and real-time alerts. Potential use cases include:
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Edge computing use cases for wearable medical devices

Wearable medical devices give patients and their caregivers greater control over health outcomes. With edge computing, health data analysis software can run directly on the wearable device, providing real-time results even without an Internet connection. Potential use cases include:
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The benefits of edge computing for healthcare

Using edge computing in a healthcare setting as described in the use cases above can help organizations:

• Improve patient care in remote settings, where a lack of infrastructure limits the ability to use cloud-based technology solutions.
• Process and analyze patient health data faster and more reliably, leading to earlier interventions.
• Increase efficiency by assisting understaffed medical teams with diagnostics, patient monitoring, and communications.
• Mitigate security and compliance risks by keeping health data within the local security perimeter.

Edge computing can also help healthcare organizations lower their operational costs at the edge by reducing bandwidth utilization and cloud data storage expenses. Another way to reduce costs is by using consolidated, vendor-neutral solutions to host, connect, and secure edge applications and workloads.

For example, the Nodegrid Gate SR is an integrated branch services router that delivers an entire stack of edge networking, infrastructure management, and computing technologies in a single, streamlined device. Nodegrid’s open, Linux-based OS supports VMs and Docker containers for third-party edge applications, security solutions, and more. Plus, an onboard Nvidia Jetson Nano card is optimized for AI workloads at the edge, significantly reducing the hardware overhead costs of using artificial intelligence at remote healthcare sites. Nodegrid’s flexible, scalable platform adapts to all edge computing use cases in healthcare, future-proofing your edge architecture.

Streamline your edge deployment with Nodegrid

The vendor-neutral Nodegrid platform consolidates an entire edge technology stack into a unified, streamlined solution. Watch a demo to see Nodegrid’s healthcare network solutions in action.

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Edge security solutions decentralize the enterprise security stack, delivering key firewall capabilities to the network’s edges. This prevents companies from funneling all edge traffic through a centralized data center firewall, reducing latency and improving overall performance.

This guide compares the most popular edge security solutions and offers recommendations for choosing the right vendor for your use case.

Executive summary

There are six single-vendor SASE solutions offering the best combination of features and capabilities for their targeted use cases.
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The best edge security solution for Gen 3 out-of-band (OOB) management, which is critical for infrastructure isolation, resilience, and operational efficiency, is Nodegrid from ZPE Systems. Nodegrid provides secure hardware and software to host other vendors’ tools on a secure, Gen 3 OOB network. It creates a control plane for edge infrastructure that’s completely isolated from breaches on the production network and consolidates an entire edge networking stack into a single solution. Disclaimer: This comparison was written by a third party in collaboration with ZPE Systems using publicly available information gathered from data sheets, admin guides, and customer reviews on sites like Gartner Peer Insights, as of 6/09/2024. Please email us if you have corrections or edits, or want to review additional attributes, at matrix@zpesystems.com.

What are edge security solutions?

Edge security solutions primarily fall into one (or both) of two categories:

• Security Service Edge (SSE) solutions deliver core security features as a managed service. SSE does not come with any networking capabilities, so companies still need a way to securely route edge traffic through the (often cloud-based) security stack. This usually involves software-defined wide area networking (SD-WAN), which was traditionally a separate service that had to be integrated with the SSE stack.
• Secure Access Service Edge (SASE) solutions package SSE together with SD-WAN, preventing companies from needing to deploy and manage multiple vendor solutions.

All the top SSE providers now offer fully integrated SASE solutions with SD-WAN. SASE’s main tech stack is in the cloud, but organizations must install SD-WAN appliances at each branch or edge data center. SASE also typically uses software agents deployed at each site and, in some cases, on all edge devices. Some SASE vendors also sell physical appliances, while others only provide software licenses for virtualized SD-WAN solutions. A third category of edge security solutions offers a secure platform to run other vendors’ SD-WAN and SASE software. These solutions also provide an important edge security capability: management network isolation. This feature ensures that ransomware, viruses, and malicious actors can’t jump from compromised IoT devices to the management interfaces used to control vital edge infrastructure.

Comparing edge security solutions

Palo Alto Prisma SASE

Palo Alto Prisma was named a Leader in Gartner’s 2023 SSE Magic Quadrant for its ability to deliver best-in-class security features. Prisma SASE is a cloud-native, AI-powered solution with the industry’s first native Autonomous Digital Experience Management (ADEM) service. Prisma’s ADEM has built-in AIOps for automatic incident detection, diagnosis, and remediation, as well as self-guided remediation to streamline the end-user experience. Prisma SASE’s advanced feature set, high price tag, and granular controls make it well-suited to larger enterprises with highly distributed networks, complex edge operations, and personnel with previous SSE and SD-WAN experience.

Palo Alto Prisma SASE Capabilities:

• Zero Trust Network Access (ZTNA) 2.0 – Automated app discovery, fine-grained access controls, continuous trust verification, and deep security inspection.
• Cloud Secure Web Gateway (SWG) – Inline visibility and control of web and SaaS traffic.
• Next-Gen Cloud Access Security Broker (CASB) – Inline and API-based security controls and contextual policies.
• Remote Browser Isolation (RBI) – Creates a secure isolation channel between users and remote browsers to prevent web threats from executing on their devices.
• App acceleration – Application-aware routing to improve “first-mile” connection performance.
• Prisma Access Browser – Policy management for edge devices.
• Firewall as a Service (FWaaS) – Advanced threat protection, URL filtering, DNS security, and other next-generation firewall (NGFW) features.
• Prisma SD-WAN – Elastic networks, app-defined fabric, and Zero Trust security.

Zscaler Zero Trust SASE

Zscaler is another 2023 SSE Magic Quadrant Leader offering a robust single-vendor SASE solution based on its Zero Trust ExchangeTM platform. Zscaler SASE uses artificial intelligence to boost its SWG, firewall, and DEM capabilities. It also offers IoT device management and OT privileged access management, allowing companies to secure unmanaged devices and provide secure remote access to industrial automation systems and other operational technology. Zscaler offers fewer security features than some of the other vendors on the list, but its capabilities and future roadmap align well with the requirements of many enterprises, especially those with large IoT and operational technology deployments.

Zscaler Zero Trust SASE Capabilities:

• Zscaler Internet Access^TM (ZIA) – SWG cyberthreat protection and zero-trust access to SaaS apps and the web.
• Zscaler Private Access^TM (ZPA) – ZTNA connectivity to private apps and OT devices.
• Zscaler Digital Experience^TM (ZDX) –  DEM with Microsoft Copilot AI to streamline incident management.
• Zscaler Data Protection – CASB/DLP secures edge data across platforms.
• IoT device visibility – IoT device, server, and unmanaged user device discovery, monitoring, and management.
• Privileged OT access – Secure access management for third-party vendors and remote user connectivity to OT systems.
• Zero Trust SD-WAN – Works with the Zscaler Zero Trust Exchange platform to secure edge and branch traffic.

Netskope ONE

Netskope is the only 2023 SSE Magic Quadrant Leader to offer a single-vendor SASE targeted to mid-market companies with smaller budgets as well as larger enterprises. The Netskope ONE platform provides a variety of security features tailored to different deployment sizes and requirements, from standard SASE offerings like ZTNA and CASB to more advanced capabilities such as AI-powered threat detection and user and entity behavior analytics (UEBA). Netskope ONE’s flexible options allow mid-sized companies to take advantage of advanced SASE features without paying a premium for the services they don’t need, though the learning curve may be a bit steep for inexperienced teams.

Netskope ONE Capabilities:

• Next-Gen SWG – Protection for cloud services, applications, websites, and data.
• CASB – Security for both managed and unmanaged cloud applications.
• ZTNA Next –  ZTNA with integrated software-only endpoint SD-WAN.
• Netskope Cloud Firewall (NCF) – Outbound network traffic security across all ports and protocols.
• RBI – Isolation for uncategorized and risky websites.
• SkopeAI – AI-powered threat detection, UEBA, and DLP
• Public Cloud Security – Visibility, control, and compliance for multi-cloud environments.
• Advanced analytics – 360-degree risk analysis.
• Cloud Exchange – Multi-cloud integration tools.
• DLP – Sensitive data discovery, monitoring, and protection.
• Device intelligence – Zero trust device discovery, risk assessment, and management.
• Proactive DEM – End-to-end visibility and real-time insights.
• SaaS security posture management – Continuous monitoring and enforcement of SaaS security settings, policies, and best practices.
• Borderless SD-WAN – Zero trust connectivity for edge, branch, cloud, remote users, and IoT devices.

Cisco

Cisco is one of the only edge security vendors to offer SASE as a managed service for companies with lean IT operations and a lack of edge networking experience. Cisco Secure Connect SASE-as-a-service includes all the usual SSE capabilities, such as ZTNA, SWG, and CASB, as well as native Meraki SD-WAN integration and a generative AI assistant. Cisco also provides traditional SASE by combining Cisco Secure Access SSE – which includes the Cisco Umbrella Secure Internet Gateway (SIG) – with Catalyst SD-WAN. Cisco Secure Connect makes SASE more accessible to smaller, less experienced IT teams, though its high price tag could be prohibitive to these companies. Cisco’s unmanaged SASE solutions integrate easily with existing Cisco infrastructures, but they offer less flexibility in the choice of features than other options on this list.

Cisco Secure Connect SASE-as-a-Service Capabilities:

• Clientless ZTNA
• Client-based Cisco AnyConnect secure remote access
• SWG
• Cloud-delivered firewall
• DNS-layer security
• CASB
• DLP
• SAML user authentication
• Generative AI assistant
• Network interconnect intelligent routing
• Native Meraki SD-WAN integration
• Unified management

Cisco Secure Access SASE Capabilities

• ZTNA 
• SWG
• CASB
• DLP
• FWaaS
• DNS-layer security
• Malware protection
• RBI
• Catalyst SD-WAN

Forcepoint ONE

Forcepoint ONE is a cloud-native single-vendor SASE solution placing a heavy emphasis on edge and multi-cloud visibility. Forcepoint ONE aggregates live telemetry from all Forcepoint security solutions and provides visualizations, executive summaries, and deep insights to help companies improve their security posture. Forcepoint also offers what they call data-first SASE, focusing on protecting data across edge and cloud environments while enabling seamless access for authorized users from anywhere in the world. Forcepoint’s data-focused platform and deep visibility make it well-suited for organizations with complicated data protection needs, such as those operating in the heavily regulated healthcare, finance, and defense industries. However, Forcepoint ONE has a steep learning curve, and integrating other services can be challenging.

Forcepoint ONE Capabilities:

• CASB – Access control and data security for over 800,000 cloud apps on managed and unmanaged devices.
• ZTNA – Secure remote access to private web apps.
• SWG – Includes RBI, content disarm & reconstruction (CDR), and a cloud firewall.
• Data Security – A cloud-native DLP to help enforce compliance across clouds, apps, emails, and endpoints.
• Insights – Real-time analysis of live telemetry data from Forcepoint ONE security products.
• FlexEdge SD-WAN – Secure access for branches and remote edge sites.

Fortinet FortiSASE

Fortinet’s FortiSASE platform combines feature-rich, AI-powered NGFW security functionality with SSE, digital experience monitoring, and a secure SD-WAN solution. Fortinet’s SASE offering includes the FortiGate NGFW delivered as a service, providing access to FortiGuard AI-powered security services like antivirus, application control, OT security, and anti-botnet protection. FortiSASE also integrates with the FortiMonitor DEM SaaS platform to help organizations optimize endpoint application performance. FortiSASE provides comprehensive edge security functionality for large enterprises hoping to consolidate their security operations with a single platform. However, the speed of some dashboards and features – particularly those associated with the FortiMonitor DEM software – could be improved for a better administrative experience.

Fortinet FortiSASE Capabilities:

• Antivirus – Protection from the latest polymorphic attacks, ransomware, viruses, and other threats.
• DLP – Prevention of intentional and accidental data leaks.
• AntiSpam – Multi-layered spam email filtering.
• Application Control – Policy creation and management for enterprise and cloud-based applications.
• Attack Surface Security – Security Fabric infrastructure assessments based on major security and compliance frameworks.
• CASB – Inline and API-based cloud application security.
• DNS Security – DNS traffic visibility and filtering.
• IPS – Deep packet inspection (DPI) and SSL inspection of network traffic.
• OT Security – IPS for OT systems including ICS and SCADA protocols.
• AI-Based Inline Malware Prevention – Real-time protection against zero-day exploits and sophisticated, novel threats.
• URL Filtering – AI-powered behavior analysis and correlation to block malicious URLs.
• Anti-Botnet and C2 – Prevention of unauthorized communication attempts from compromised remote servers.
• FortiMonitor DEM – SaaS-based digital experience monitoring.
• Secure SD-WAN – On-premises and cloud-based SD-WAN integrated into the same OS as the SSE security solutions.

Edge isolation and security with ZPE Nodegrid

The Nodegrid platform from ZPE Systems is a different type of edge security solution, providing secure hardware and software to host other vendors’ tools on a secure, Gen 3 out-of-band (OOB) management network. Nodegrid integrated branch services routers use alternative network interfaces (including 5G/4G LTE) and serial console technology to create a control plane for edge infrastructure that’s completely isolated from breaches on the production network. It uses hardware security features like secure boot and geofencing to prevent physical tampering, and it supports strong authentication methods and SAML integrations to protect the management network. Nodegrid’s OOB also ensures remote teams have 24/7 access to manage, troubleshoot, and recover edge deployments even during a major network outage or ransomware infection. Plus, Nodegrid’s ability to host Guest OS, including Docker containers and VNFs, allows companies to consolidate an entire edge networking stack in a single platform. Nodegrid devices like the Gate SR with Nvidia Jetson Nano can even run edge computing and AI/ML workloads alongside SASE. .

ZPE Nodegrid Edge Security Capabilities

• Vendor-neutral platform – Hosting for third-party applications and services, including Docker containers and virtualized network functions.
• Gen 3 OOB – Management interface isolation and 24/7 remote access during outages and breaches.
• Branch networking – Routing and switching, VNFs, and software-defined branch networking (SD-Branch).
• Secure boot – Password-protected BIO/Grub and signed software.
• Latest kernel & cryptographic modules – 64-bit OS with current encryption and frequent security patches.
• SSO with SAML, 2FA, & remote authentication – Support for Duo, Okta, Ping, and ADFS.
• Geofencing – GPS tracking with perimeter crossing detection.
• Fine-grain authorization – Role-based access control.
• Firewall – Native IPSec & Fail2Ban intrusion prevention and third-party extensibility.
• Tampering protection – Configuration checksum and change detection with a configuration ‘reset’ button.
• TPM encrypted storage – Software encryption for SSD hardware storage.

Deploy edge security solutions on the vendor-neutral Nodegrid OOB platform

Nodegrid’s secure hardware and vendor-neutral OS make it the perfect platform for hosting other vendors’ SSE, SD-WAN, and SASE solutions. Reach out today to schedule a free demo.

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This approach creates challenges that impact the efficiency and safety of edge operations. As edge data volumes grow, so do MPLS bandwidth costs. Large data transmissions to and from the edge are also at risk of interception by malicious actors. The best way to solve this problem is with edge computing, which moves data processing applications and systems to the edges of the network to run alongside the devices that generate most of the edge data.

This guide defines edge computing vs on-premises computing in detail before analyzing the advantages and challenges involved with each approach.

Defining edge computing vs on-premises computing

On-premises computing systems are physical or virtual resources that live in a traditional data center. Despite the name, these systems don’t necessarily reside in the same physical premises as the main business, with many companies using colocation data centers owned by third parties. Organizations have complete control over the physical and virtual infrastructure, unlike in private or public cloud deployments. The defining characteristic of on-premises computing is that most or all enterprise applications and digital services reside in a centralized location, with most network traffic and data transmissions flowing through it.

Edge computing systems are physical and virtual data processing resources that companies deploy alongside the edge devices that generate the most data. Examples include installing machine learning software at a remote manufacturing site to gain maintenance insights into remote SCADA (supervisory control and data acquisition) systems, or running a data analytics app on a chip installed in a wearable medical sensor to provide patients with real-time health feedback. Edge computing has many potential use cases and deployment models, but the defining characteristic is proximity to the sources of edge-generated data.

The advantages of edge computing vs on-premises

The benefits of edge computing compared to on-premises include:

• Improved workload efficiency – Edge computing reduces network traffic bottlenecks and latency because data stays on the local network or even on the same device. This improves the overall speed, performance, and efficiency of all enterprise applications and services.

• Bandwidth cost reduction – Edge computing reduces the volume of data transmitted over MPLS links between edge sites and the central data center. The cost for MPLS bandwidth is typically very high, so edge computing decreases operational costs at branch offices and other edge business sites.

• Better data security – Any time companies transmit data off-site, there’s a risk of interception by cybercriminals. Edge computing reduces the attack surface by keeping valuable data on the local network, which improves data security and simplifies data privacy compliance.

The challenges of edge computing vs on-premises

The challenges of edge computing compared to on-premises include:

• Data storage restraints – The typical edge deployment is much smaller than a centralized data center and has fewer data storage resources, making it difficult to hold on to data long enough to process it with edge applications.

• Fewer security controls – Edge deployments often lack the robust physical security controls utilized by data centers, such as security guards and biometric door locks, creating the need for edge-specific security solutions to protect data and devices.

• Edge management and orchestration – Edge sites are difficult for centralized IT operations teams to monitor and troubleshoot, especially if an equipment failure, ransomware attack, or natural disaster takes down the network.

Comparing edge computing vs on-premises

ZPE Systems provides edge network solutions to help you overcome your biggest challenges. Nodegrid integrated edge routers support VM and Docker hosting for your choice of third-party edge computing and security applications, allowing you to devote more hardware budget (and rack space) to data storage and other critical infrastructure. Robust onboard security features like TPM and geofencing defend Nodegrid hardware from tampering and compromise for better edge security coverage.

All Nodegrid devices provide OOB management to give teams continuous remote access to edge infrastructure, allowing them to quickly recover from outages, equipment failures, and cyberattacks. Plus, our vendor-neutral management software seamlessly integrates all your edge solutions to create a unified EMO platform that streamlines edge operations.

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As today’s enterprises race toward digital transformation with cloud-based applications, software-as-a-service (SaaS), and artificial intelligence (AI), data center architectures are evolving. Organizations rely less on traditional server-based infrastructures, preferring the scalability, speed, and cost-efficiency of cloud and hybrid-cloud architectures using major platforms such as AWS and Google. These digital services are supported by an underlying infrastructure comprising thousands of servers, GPUs, and networking devices in what’s known as a hyperscale data center.

The size and complexity of hyperscale data centers present unique management, scaling, and resilience challenges that providers must overcome to ensure an optimal customer experience. This blog explains what a hyperscale data center is and compares it to a normal data center deployment before discussing the unique challenges involved in managing and supporting a hyperscale deployment.

What is a hyperscale data center?

As the name suggests, a hyperscale data center operates at a much larger scale than traditional enterprise data centers. A typical data center houses infrastructure for dozens of customers, each containing tens of servers and devices. A hyperscale data center deployment supports at least 5,000 servers dedicated to a single platform, such as AWS. These thousands of individual machines and services must seamlessly interoperate and rapidly scale on demand to provide a unified and streamlined user experience.

The biggest hyperscale data center challenges

Operating data center deployments on such a massive scale is challenging for several key reasons.

This guide to healthcare network design describes the core technologies comprising a resilient network architecture before discussing how to take resilience engineering to the next level with automation, edge computing, and isolated recovery environments.

Core healthcare network resilience technologies

A resilient healthcare network design includes resilience systems that perform critical functions while the primary systems are down. The core technologies and capabilities required for resilience systems include:

• Full-stack networking – Routing, switching, Wi-Fi, voice over IP (VoIP), virtualization, and the network overlay used in software-defined networking (SDN) and software-defined wide area networking (SD-WAN)

• Full compute capabilities – The virtual machines (VMs), containers, and/or bare metal servers needed to run applications and deliver services

• Storage – Enough to recover systems and applications as well as deliver content while primary systems are down

• Automation – Tools like zero-touch provisioning (ZTP) that facilitate speedy recovery while minimizing human error

These are the main technologies that allow healthcare IT teams to reduce disruptions and streamline recovery. Once organizations achieve this base level of resilience, they can evolve by adding more automation, edge computing, and isolated recovery infrastructure.

Extending automated control over healthcare networks

Automation is one of the best tools healthcare teams have to reduce human error, improve efficiency, and ensure network resilience. However, automation can be hard to learn, and scripts take a long time to write, so having systems are easily deployable with low technical debt is critical. Tools like ZTP (zero-touch provisioning), and the integration of technology like Infrastructure as Code (IaC), accelerate recovery by automating device provisioning. Healthcare organizations can use automation technologies such as AIOps with resilience systems technologies like out-of-band (OOB) management to monitor, maintain, and troubleshoot critical infrastructure.

Using automation to observe and control healthcare networks helps prevent failures from occuring, but when trouble does actually happen, resilience systems ensure infrastructure and services are quickly returned to health or rerouted when needed.

Improving performance and security with edge computing

The healthcare industry is one of the biggest adopters of IoT (Internet of Things) technology. Remote, networked medical devices like pacemakers, insulin pumps, and heart rate monitors collect a large volume of valuable data that healthcare teams use to improve patient care. Transmitting that data to a software application in a data center or cloud adds latency and increases the chances of interception by malicious actors. Edge computing for healthcare eliminates these problems by relocating applications closer to the source of medical data, at the edges of the healthcare network. Edge computing significantly reduces latency and security risks, creating a more resilient healthcare network design.

Note that teams also need a way to remotely manage and service edge computing technologies. Find out more in our blog Edge Management & Orchestration.

Increasing resilience with isolated recovery environments

Ransomware is one of the biggest threats to network resilience, with attacks occurring so frequently that it’s no longer a question of ‘if’ but ‘when’ a healthcare organization will be hit.

• Read about the high-profile ransomware attacks affecting MOVEit customers and MGM

Recovering from ransomware is especially difficult because of how easily malicious code can spread from the production network into backup data and systems. The best way to protect your resilience systems and speed up ransomware recovery is with an isolated recovery environment (IRE) that’s fully separated from the production infrastructure.

An IRE ensures that IT teams have a dedicated environment in which to rebuild and restore critical services during a ransomware attack, as well as during other disruptions or disasters. An IRE does not replace a traditional backup solution, but it does provide a safe environment that’s inaccessible to attackers, allowing response teams to conduct remediation efforts without being detected or interrupted by adversaries. Isolating your recovery architecture improves healthcare network resilience by reducing the time it takes to restore critical systems and preventing reinfection.

To learn more about how to recover from ransomware using an isolated recovery environment, download our whitepaper, 3 Steps to Ransomware Recovery.

Resilient healthcare network design with Nodegrid

A resilient healthcare network design is resistant to failures thanks to resilience systems that perform critical functions while the primary systems are down. Healthcare organizations can further improve resilience by implementing additional automation, edge computing, and isolated recovery environments (IREs).

Nodegrid healthcare network solutions from ZPE Systems simplify healthcare resilience engineering by consolidating the technologies and services needed to deploy and evolve your resilience systems. Nodegrid’s serial console servers and integrated branch/edge routers deliver full-stack networking, combining cellular, Wi-Fi, fiber, and copper into software-driven networking that also includes compute capabilities, storage, vendor-neutral application & automation hosting, and cellular failover required for basic resilience. Nodegrid also uses out-of-band (OOB) management to create an isolated management and recovery environment without the cost and hassle of deploying an entire redundant infrastructure.

Ready to see how Nodegrid can improve your network’s resilience?

Nodegrid streamlines resilient healthcare network design with consolidated, vendor-neutral solutions. Request a free demo to see Nodegrid in action.

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Organizations prioritizing digital transformation by adopting IoT (Internet of Things) technologies generate and process an unprecedented amount of data. Traditionally, the systems used to process that data live in a centralized data center or the cloud. However, IoT devices are often deployed around the edges of the enterprise in remote sites like retail stores, manufacturing plants, and oil rigs. Transferring so much data back and forth creates a lot of latency and uses valuable bandwidth. Edge computing solves this problem by moving processing units closer to the sources that generate the data.

IBM estimates there are over 15 billion edge devices already in use. While edge computing has rapidly become a vital component of digital transformation, many organizations focus on individual use cases and lack a cohesive edge computing strategy. According to a recent Gartner report, the result is what’s known as “edge sprawl”: many individual edge computing solutions deployed all over the enterprise without any centralized control or visibility. Organizations with disjointed edge computing deployments are less efficient and more likely to hit roadblocks that stifle digital transformation.

The report provides guidance on building an edge computing strategy to combat sprawl, and the foundation of that strategy is edge management and orchestration (EMO). Below, this post summarizes the key findings from the Gartner report and discusses some of the biggest edge computing challenges before explaining how to solve them with a centralized EMO platform.

Key findings from the Gartner report

Many organizations already use edge computing technology for specific projects and use cases – they have an individual problem to solve, so they deploy an individual solution. Since the stakeholders in these projects usually aren’t architects, they aren’t building their own edge computing machines or writing software for them. Typically, these customers buy pre-assembled solutions or as-a-service offerings that meet their specific needs.

However, a piecemeal approach to edge computing projects leaves organizations with disjointed technologies and processes, contributing to edge sprawl and shadow IT. Teams can’t efficiently manage or secure all the edge computing projects occurring in the enterprise without centralized control and visibility. Gartner urges I&O (infrastructure & operations) leaders to take a more proactive approach by developing a comprehensive edge computing strategy encompassing all use cases and addressing the most common challenges.

Edge computing challenges

Gartner identifies six major edge computing challenges to focus on when developing an edge computing strategy:

Let’s discuss these challenges and their solutions in greater depth.

• Enabling extensibility – Many organizations deploy purpose-built edge computing solutions for their specific use case and can’t adapt when workloads change or grow.  The goal is to attempt to predict future workloads based on planned initiatives and create an edge computing strategy that leaves room for that growth. However, no one can really predict the future, so the strategy should account for unknowns by utilizing common, vendor-neutral technologies that allow for expansion and integration.

• Extracting value from edge data – The generation of so much IoT and sensor data gives organizations the opportunity to extract additional value in the form of business insights, predictive analysis, and machine learning training. Quickly extracting that value is challenging when most data analysis and AI applications still live in the cloud. To effectively harness edge data, organizations should look for ways to deploy artificial intelligence training and data analytics solutions alongside edge computing units.

• Governing edge data – Edge computing deployments often have more significant data storage constraints than central data centers, so quickly distinguishing between valuable data and destroyable junk is critical to edge ROIs. With so much data being generated, it’s often challenging to make this determination on the fly, so it’s important to address data governance during the planning process. There are automated data governance solutions that can help, but these must be carefully configured and managed to avoid data loss.

• Supporting edge-native applications – Edge applications aren’t just data center apps lifted and shifted to the edge; they’re designed for edge computing from the bottom up. Like cloud-native software, edge apps often use containers, but clustering and cluster management are different beasts outside the cloud data center. The goal is to deploy platforms that support edge-native applications without increasing the technical debt, which means they should use familiar container management technologies (like Docker) and interoperate with existing systems (like OT applications and VMs).

• Securing the edge – Edge deployments are highly distributed in locations that may lack many physical security features in a traditional data center, such as guarded entries and biometric locks, which adds risk and increases the attack surface. Organizations must protect edge computing nodes with a multi-layered defense that includes hardware security (such as TPM), frequent patches, zero-trust policies, strong authentication (e.g., RADIUS and 2FA), and network micro-segmentation.

• Edge management and orchestration – Moving computing out of the climate-controlled data center creates environmental and power challenges that are difficult to mitigate without an on-site technical staff to monitor and respond. When equipment failure, configuration errors, or breaches take down the network, remote teams struggle to meet resilience requirements to keep business operations running 24/7. The sheer number and distribution area of edge computing units make them challenging to manage efficiently, increasing the likelihood of mistakes, issues, or threat indicators slipping between the cracks. Addressing this challenge requires centralized edge management and orchestration (EMO) with environmental monitoring and out-of-band (OOB) connectivity.

  A centralized EMO platform gives administrators a single-pane-of-glass view of all edge deployments and the supporting infrastructure, streamlining management workflows and serving as the control panel for automation, security, data governance, cluster management, and more. The EMO must integrate with the technologies used to automate edge management workflows, such as zero-touch provisioning (ZTP) and configuration management (e.g., Ansible or Chef), to help improve efficiency while reducing the risk of human error. Integrating environmental sensors will help remote technicians monitor heat, humidity, airflow, and other conditions affecting critical edge equipment’s performance and lifespan. Finally, remote teams need OOB access to edge infrastructure and computing nodes, so the EMO should use out-of-band serial console technology that provides a dedicated network path that doesn’t rely on production resources.

Gartner recommends focusing your edge computing strategy on overcoming the most significant risks, challenges, and roadblocks. An edge management and orchestration (EMO) platform is the backbone of a comprehensive edge computing strategy because it serves as the hub for all the processes, workflows, and solutions used to solve those problems.

Edge management and orchestration (EMO) with Nodegrid

Nodegrid is a vendor-neutral edge management and orchestration (EMO) platform from ZPE Systems. Nodegrid uses Gen 3 out-of-band technology that provides 24/7 remote management access to edge deployments while freely interoperating with third-party applications for automation, security, container management, and more. Nodegrid environmental sensors give teams a complete view of temperature, humidity, airflow, and other factors from anywhere in the world and provide robust logging to support data-driven analytics.

The open, Linux-based Nodegrid OS supports direct hosting of containers and edge-native applications, reducing the hardware overhead at each edge deployment. You can also run your ML training, AIOps, data governance, or data analytics applications from the same box to extract more value from your edge data without contributing to sprawl.

In addition to hardware security features like TPM and geofencing, Nodegrid supports strong authentication like 2FA, integrates with leading zero-trust providers like Okta and PING, and can run third-party next-generation firewall (NGFW) software to streamline deployments further.

The Nodegrid platform brings all the components of your edge computing strategy under one management umbrella and rolls it up with additional core networking and infrastructure management features. Nodegrid consolidates edge deployments and streamlines edge management and orchestration, providing a foundation for a Gartner-approved edge computing strategy.

Want to learn more about how Nodegrid can help you overcome your biggest edge computing challenges?

Contact ZPE Systems for a free demo of the Nodegrid edge management and orchestration platform.

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