Most organizations rely on critical IT in order to serve their essential business functions. A reliable method to maintain critical IT is to use dedicated out-of-band (OOB) management networks, which traditionally have relied on plain old telephone service (POTS) lines or dedicated telephony circuits for remote access. However, these traditional links come with high costs, lots of complexity, and slow performance, which make them difficult to deploy and maintain.

Enter Starlink, a satellite-based Internet service that offers a cost-effective and scalable alternative for out-of-band remote access. This post discusses how Starlink solves these common problems and gives you a free guide that walks you through the setup process.

Problem: POTS and Telephony Lines Are Expensive

For decades, IT professionals have relied on POTS and telephony lines for OOB management, mainly because these lines remain operational even when the primary data network goes down. A major problem is that POTS lines are increasingly expensive to install and maintain, particularly in remote or rural areas. Additionally, 4G/5G LTE options aren’t always available due to coverage limitations or large enough data plans. The shift towards VoIP (Voice over IP) and digital communications has made POTS lines even less relevant, with many service providers phasing out support. This leaves businesses with fewer options and higher costs for maintaining these legacy systems.

Solution: Starlink is Cost-Effective

Starlink offers a much more cost-effective solution. You can use off-the-shelf routers to set up an OOB management network for a fraction of the cost of traditional methods. Starlink also has a relatively low monthly subscription fee and straightforward pricing model, which make it easy to budget and plan IT expenditures. If components fail or break, you can typically repair or replace them yourself to get back up and running quickly.

Figure 1: Starlink requires only a dish, router, and few other components, making it a cost-effective alternative to expensive POTS lines.

Problem: Traditional Lines Are Difficult To Scale

Traditional POTS-based systems are notoriously difficult to scale, often requiring significant infrastructure investments and complex configurations. Copper wiring is expensive to install and maintain, and as more connections come online, switching systems must be upgraded. On top of this, POTS lines are being phased out, which means there are fewer resources being devoted to scaling and maintaining them.

Solution: Starlink is Simple to Set Up and Scale

Starlink entirely eliminates the need for telephony lines, and is a simple and scalable solution for OOB remote access. You can find the full list of components in our setup guide below, but with a Starlink terminal, compatible router, and minimal configuration, you can scale your OOB network wherever you have Starlink coverage. This ease-of-use extends to day-to-day management as well. Starlink’s satellite service offers global coverage, meaning you can manage your network devices, servers, and other critical infrastructure from virtually anywhere in the world.

Figure 2: Starlink comes with a straightforward out-of-box experience and step-by-step instructions. You can set up an out-of-band network in about one hour.

Problem: POTS Lines Lack Performance

POTS is designed primarily for voice communication and offers extremely limited bandwidth. It can’t support modern data services (such as video or high-speed internet) efficiently. As out-of-band management advances with data and video monitoring capabilities (such as AI computer vision), POTS infrastructure just doesn’t have the bandwidth to keep up.

Solution: Starlink Meets Modern Performance Requirements

Starlink provides high-speed internet, at speeds that typically range from 50 to 200Mbps. The connection handles much larger volumes of data than POTS lines are capable of, and Starlink’s low-Earth orbit satellites reduce latency to as low at 25ms compared to the typical 150ms of POTS lines. Out-of-band using Starlink means that IT teams can manage more systems and data, and have a more responsive experience, whether they’re managing edge routers across their bank branches or monitoring the cooling systems in their distributed colocations.

Figure 3: Starlink provides high-speed connectivity, with speeds ranging from 50 to 200Mbps.

Get Started With Starlink Using Our Setup Guide

What is Passive Optical Networking (PON)?

Passive optical networking (PON) is a high-speed broadband technology that enables the delivery of multiple services over a single fiber optic cable. XGS-PON – 10G Symmetrical PON –  offers speeds of up to 10 Gbps downstream and 10 Gbps upstream (hence the term ‘symmetrical’), making it ideal for applications such as video streaming, online gaming, and cloud computing.

What Problems Does PON Solve for Out-of-Band Management?

PON addresses the issue of efficiency in terms of both uplink costs and bandwidth usage. Traditional POTS lines and dedicated circuits rely on legacy infrastructure that requires regular maintenance. This infrastructure must scale as more out-of-band devices are added to the network, which increases costs and energy consumption. On top of this, using a 10G uplink for a serial console’s 10K traffic is like throwing away 99% of that high bandwidth. Per Gartner’s Market Guide for Optical Transport Systems report (Published 20 November 2023) the best way to “lower cost and energy per transported bit” is by using technologies such as passive optical networking.

Because PON uses passive optical splitters that have no moving parts or powered components between the central hub and end users, PON is much more efficient for deploying serial consoles close to target assets. These out-of-band devices can be deployed in large quantities and close to the network edge, with up to 256 devices sharing one uplink. This reduces cabling and power requirements, and is ideal for MSP and campus operators, where there are many out-of-band devices distributed over long distances. 

More About PON: GPON and XGS-PON Technologies

Passive Optical Networking (PON) leverages time-division multiplexing (TDM) and different wavelengths of light to transmit and receive data on a single fiber strand. This allows efficient communication among up to 256 devices over a single fiber. Initially developed for fiber-to-the-home (FTTH) deployments, PON technology has evolved to facilitate the addition of network nodes with minimal infrastructure changes. GPON (gigabit-capable PON) and XGS-PON use different frequencies for upstream and downstream data transmission. The upstream headend, known as the Optical Line Terminal (OLT), manages and coordinates the time slots allocated to downstream Optical Network Units (ONUs) for data transmission.

GPON and XGS-PON Support on ZPE Systems’ Nodegrid SR Gateway

ZPE Systems’ Nodegrid SR appliances, which are used as out-of-band access nodes or complete branch gateways, now support GPON and XGS-PON technology (patent pending) via SFP and SFP+ ports. The Nodegrid SR family is offered in multiple form factors to be right-sized for deployments in branch offices, factories, smart buildings, and industrial environments (such as for SCADA).

Having support for GPON and XGS-PON means network engineers now have a flexible choice of high-speed uplink technologies. This versatility makes the Nodegrid SR gateway suitable for edge deployments, where it can establish an OOBI-WAN (out-of-band infrastructure WAN) link, and for data centers, where it enhances uplink efficiency. Given the low bandwidth requirements of serial console and out-of-band communications, PON technology is well-suited for these applications. A single fiber strand can be shared among hundreds of out-of-band and serial console devices using passive optical splitters. Organizations can deploy out-of-band devices close to the racks and edges of the network in a cost- and energy-efficient manner. Additionally, ZPE devices support ONU SFPs compatible with third-party OLT headends, ensuring broad interoperability and integration.

Benefits of Using XGS-PON with ZPE Systems’ Nodegrid SR Gateway

The benefits of using XGS-PON with ZPE Systems’ Nodegrid SR gateway include:

• High-Speed Connectivity: XGS-PON delivers symmetrical speeds of up to 10 Gbps, making it ideal for high-bandwidth applications like video streaming, online gaming, and cloud computing. This ensures consistent and high-quality service for end-users.
• Cost-Effectiveness: Deploying XGS-PON is a cost-effective solution for delivering high-speed broadband services, especially in scenarios where upgrading existing infrastructure may be challenging.
• Scalability: The Nodegrid SR Gateway, acting as an ONU, can connect up to 256 serial consoles through a single fiber strand. PON’s use of asymmetric wavelengths and TDM enables multiple devices to share the same fiber strand efficiently. Optical splitters, which require no external power, facilitate the sharing of fiber between multiple ONUs, which makes scaling much more cost and energy efficient.
• Reliability: The Nodegrid SR gateway is proven by service providers worldwide. Its robust design and compatibility with various network configurations make it a reliable choice for delivering high-quality broadband services.

Figure 1: ZPE Nodegrid SR gateway with XGS-PON ONU support

XGS-PON Enhances Efficiency of Out-of-Band

XGS-PON is a significant advancement over traditional, copper-based uplinks. The integration of XGS-PON support in the ZPE Systems Nodegrid SR Gateway allows network architects to deploy a dedicated out-of-band ring that is not only high-speed but also cost-effective, energy-efficient, and capable of covering longer distances. PON technology, with its ability to handle the lower data rates of out-of-band transmissions, is an ideal uplink medium for serial console transmission. The combination of XGS-PON and the Nodegrid SR Gateway provides a powerful and flexible solution for modern network infrastructure.

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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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The edge computing market is huge and continuing to grow. A recent study projected that spending on edge computing will reach $232 billion in 2024. Organizations across nearly every industry are taking advantage of edge computing’s real-time data processing capabilities to get immediate business insights, respond to issues at remote sites before they impact operations, and much more. This blog discusses some of the applications of edge computing for industries like finance, retail, and manufacturing, and provides advice on how to get started.

What is edge computing?

Edge computing involves decentralizing computing capabilities and moving them to the network’s edges. Doing so reduces the number of network hops between data sources and the applications that process and use that data, which mitigates latency, bandwidth, and security concerns compared to cloud or on-premises computing.

Learn more about edge computing vs cloud computing or edge computing vs on-premises computing.

Edge computing often uses edge-native applications that are built from the ground up to harness edge computing’s unique capabilities and overcome its limitations. Edge-native applications leverage some cloud-native principles, such as containers, microservices, and CI/CD. However, unlike cloud-native apps, they’re designed to process transient, ephemeral data in real time with limited computational resources. Edge-native applications integrate seamlessly with the cloud, upstream resources, remote management, and centralized orchestration, but can also operate independently as needed.
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Applications of edge computing

Financial services

The financial services industry collects a lot of edge data from bank branches, web and mobile apps, self-service ATMs, and surveillance systems. Many firms feed this data into AI/ML-powered data analytics software to gain insights into how to improve their services and generate more revenue. Some also use AI-powered video surveillance systems to analyze video feeds and detect suspicious activity. However, there are enormous security, regulatory, and reputational risks involved in transmitting this sensitive data to the cloud or an off-site data center.

Financial institutions can use edge computing to move data analytics applications to branches and remote PoPs (points of presence) to help mitigate the risks of transmitting data off-site. Additionally, edge computing enables real-time data analysis for more immediate and targeted insights into customer behavior, branch productivity, and security. For example, AI surveillance software deployed at the edge can analyze live video feeds and alert on-site security personnel about potential crimes in progress.

Industrial manufacturing

Many industrial manufacturing processes are mostly (if not completely) automated and overseen by operational technology (OT), such as supervisory control and data acquisition systems (SCADA). Logs from automated machinery and control systems are analyzed by software to monitor equipment health, track production costs, schedule preventative maintenance, and perform quality assurance (QA) on components and products. However, transferring that data to the cloud or centralized data center increases latency and creates security risks.

Manufacturers can use edge computing to analyze OT data in real time, gaining faster insights and catching potential issues before they affect product quality or delivery schedules. Edge computing also allows industrial automation and monitoring processes to continue uninterrupted even if the site loses Internet access due to an ISP outage, natural disaster, or other adverse event in the region. Edge resilience can be further improved by deploying an out-of-band (OOB) management solution like Nodegrid that enables control plane/data plane isolation (also known as isolated management infrastructure), as this will give remote teams a lifeline to access and recover OT systems.

Retail operations

In the age of one-click online shopping, the retail industry has been innovating with technology to enhance the in-store experience, improve employee productivity, and keep operating costs down. Retailers have a brief window of time to meet a customer’s needs before they look elsewhere, and edge computing’s ability to leverage data in real time is helping address that challenge. For example, some stores place QR codes on shelves that customers can scan if a product is out of stock, alerting a nearby representative to provide immediate assistance.

Another retail application of edge computing is enhanced inventory management. An edge computing solution can make ordering recommendations based on continuous analysis of purchasing patterns over time combined with real-time updates as products are purchased or returned. Retail companies, like financial institutions, can also use edge AI/ML solutions to analyze surveillance data and aid in loss prevention.

Healthcare

The healthcare industry processes massive amounts of data generated by medical equipment like insulin pumps, pacemakers, and imaging devices. Patient health data can’t be transferred over the open Internet, so getting it to the cloud or data center for analysis requires funneling it through a central firewall via MPLS (for hospitals, clinics, and other physical sites), overlay networks, or SD-WAN (for wearable sensors and mobile EMS devices). This increases the number of network hops and creates a traffic bottleneck that prevents real-time patient monitoring and delays responses to potential health crises.

Edge computing for healthcare allows organizations to process medical data on the same local network, or even the same onboard chip, as the sensors and devices that generate most of the data. This significantly reduces latency and mitigates many of the security and compliance challenges involved in transmitting regulated health data offsite. For example, an edge-native application running on an implanted heart-rate monitor can operate without a network connection much of the time, providing the patient with real-time alerts so they can modify their behavior as needed to stay healthy. If the app detects any concerning activity, it can use multiple cellular and ATT FirstNet connections to alert the cardiologist without exposing any private patient data.

Oil, gas, & mining

Oil, gas, and other mining operations use IoT sensors to monitor flow rates, detect leaks, and gather other critical information about equipment deployed in remote sites, drilling rigs, and offshore platforms all over the world. Drilling rigs are often located in extremely remote or even human-inaccessible locations, so ensuring reliable communications with monitoring applications in the cloud or data center can be difficult. Additionally, when networks or systems fail, it can be time-consuming and expensive – not to mention risky – to deploy IT teams to fix the issue on-site.

The energy and mining industries can use edge computing to analyze data in real time even in challenging deployment environments. For example, companies can deploy monitoring software on cellular-enabled edge computing devices to gain immediate insights into equipment status, well logs, borehole logs, and more. This software can help establish more effective maintenance schedules, uncover production inefficiencies, and identify potential safety issues or equipment failures before they cause larger problems. Edge solutions with OOB management also allow IT teams to fix many issues remotely, using alternative cellular interfaces to provide continuous access for troubleshooting and recovery.

AI & machine learning

Artificial intelligence (AI) and machine learning (ML) have broad applications across many industries and use cases, but they’re all powered by data. That data often originates at the network’s edges from IoT devices, equipment sensors, surveillance systems, and customer purchases. Securely transmitting, storing, and preparing edge data for AI/ML ingestion in the cloud or centralized data center is time-consuming, logistically challenging, and expensive. Decentralizing AI/ML’s computational resources and deploying them at the edge can significantly reduce these hurdles and unlock real-time capabilities.

For example, instead of deploying AI on a whole rack of GPUs (graphics processing units) in a central data center to analyze equipment monitoring data for all locations, a manufacturing company could use small edge computing devices to provide AI-powered analysis for each individual site. This would reduce bandwidth costs and network latency, enabling near-instant insights and providing an accelerated return on the investment into artificial intelligence technology.

AIOps can also be improved by edge computing. AIOps solutions analyze monitoring data from IT devices, network infrastructure, and security solutions and provide automated incident management, root-cause analysis, and simple issue remediation. Deploying AIOps on edge computing devices enables real-time issue detection and response. It also ensures continuous operation even if an ISP outage or network failure cuts off access to the cloud or central data center, helping to reduce business disruptions at vital branches and other remote sites.

Getting started with edge computing

The edge computing market has focused primarily on single-use-case solutions designed to solve specific business problems, forcing businesses to deploy many individual applications across the network. This piecemeal approach to edge computing increases management complexity and risk while decreasing operational efficiency.

The recommended approach is to use a centralized edge management and orchestration (EMO) platform to monitor and control edge computing operations. The EMO should be vendor-agnostic and interoperate with all the edge computing devices and edge-native applications in use across the organization. The easiest way to ensure interoperability is to use vendor-neutral edge computing platforms to run edge-native apps and AI/ML workflows.

For example, the Nodegrid platform from ZPE Systems provides the perfect vendor-neutral foundation for edge operations. Nodegrid integrated branch services routers like the Gate SR with integrated Nvidia Jetson Nano use the open, Linux-based Nodegrid OS, which can host Docker containers and edge-native applications for third-party AI, ML, data analytics, and more. These devices use out-of-band management to provide 24/7 remote visibility, management, and troubleshooting access to edge deployments, even in challenging environments like offshore oil rigs. Nodegrid’s cloud-based or on-premises software provides a single pane of glass to orchestrate operations at all edge computing sites.

Streamline your edge computing deployment with Nodegrid

The vendor-neutral Nodegrid platform can simplify all applications of edge computing with easy interoperability, reduced hardware overhead, and centralized edge management and orchestration. Schedule a Nodegrid demo to learn more.
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The edge computing market is growing fast, with experts predicting edge computing spending to reach almost $350 billion in 2027. Companies use edge computing to leverage data from Internet of Things (IoT) sensors and other devices at the periphery of the network in real-time, unlocking faster insights, accelerating ROIs for artificial intelligence and machine learning investments, and much more. This blog highlights 7 edge computing examples from across many different industries and provides tips and best practices for each use case.

What is edge computing?

Edge computing involves moving compute capabilities – processing units, RAM, storage, data analysis software, etc. – to the network’s edges. This allows companies to analyze or otherwise use edge data in real-time, without transmitting it to a central data center or the cloud.

Edge computing shortens the physical and logical distance between data-generating devices and the applications that use that data, which reduces bandwidth costs and network latency while simplifying many aspects of data security and compliance.

7 Edge computing examples

Below are 7 examples of how organizations use edge computing, along with best practices for overcoming the typical challenges involved in each use case. Click the links in the table for more information about each example.

1. Monitoring and managing inaccessible equipment in the oil and gas industry

The oil and gas industry uses IoT sensors to monitor flow rates, detect leaks, and gather other critical information about human-inaccessible equipment and operations. With drilling rigs located offshore and in extremely remote locations, ensuring reliable internet access to communicate with cloud-based or on-premises monitoring applications can be tricky. Dispatching IT teams to diagnose and repair issues is also costly, time-consuming, and risky. Edge computing allows oil and gas companies to process data on-site and in real-time, so safety issues and potential equipment failures are caught and remediated as soon as possible, even when Internet access is spotty.

2. Remotely managing and securing fully automated Smart buildings

Smart buildings use IoT sensors to monitor and control building functions such as HVAC, lighting, power, and security. Property management companies and facilities departments use data analysis software to automatically determine optimal conditions, respond to issues, and alert technicians when emergencies occur. Edge computing allows these automated processes to respond to changing conditions in real-time, reducing the need for on-site personnel and improving operational efficiency.

3. Analyzing patient health data generated by mobile devices in the healthcare industry

Healthcare organizations use data analysis software, including AI and machine learning, to analyze patient health data generated by insulin pumps, pacemakers, imaging devices, and other IoT medical technology. Keeping that data secure is critical for regulatory compliance, so it must be funneled through a firewall on its way to cloud-based or data center applications, increasing latency and preventing real-time response to potentially life-threatening health issues. Edge computing for healthcare moves patient monitoring and data analysis applications to the same local network (or even the same onboard chip) as the sensors generating most of the data, reducing security risks and latency. Some edge computing applications for healthcare can operate without a network connection most of the time, using built-in cellular interfaces and ATT FirstNet connections to send emergency alerts as needed without exposing any private patient data.

4. Reducing latency for live streaming events and online gaming

Streaming live content requires low-latency processing for every user regardless of their geographic location, which is hard to deliver from a few large, strategically placed data centers. Edge computing decentralizes computing resources, using relatively small deployments in many different locations to bring services closer to audience members and gamers. Edge computing reduces latency for streaming sports games, concerts, and other live events, as well as online multiplayer games where real-time responses are critical to the customer experience.

5. Improving performance and business outcomes for artificial intelligence/machine learning

Artificial intelligence and machine learning applications provide enhanced data analysis capabilities for essentially any use case, but they must ingest vast amounts of data to do so. Securely transmitting and storing edge and IoT data and preparing it for ingestion in data lakes or data warehouses located in the cloud or data center takes significant time and effort, which may prevent companies from getting the most out of their AI investment. Edge computing for AI/ML eliminates transmission and storage concerns by processing data directly from the sources. Edge computing lets companies leverage their edge data for AI/ML much faster, enabling near-real-time insights, improving application performance, and providing accelerated business value from AI investments.

6. Enhancing remote surveillance capabilities at banks and ATMs

Constantly monitoring video surveillance feeds from banks and ATMs is very tedious for people, but machines excel at it. AI-powered video surveillance systems use advanced machine-learning algorithms to analyze video feeds and detect suspicious activity with far greater vigilance and accuracy than human security teams. With edge computing, these solutions can analyze surveillance data in real-time, so they could potentially catch a crime as it’s occurring. Edge computing also keeps surveillance data on-site, reducing bandwidth costs, network latency, and the risk of interception.

7. Extending data analysis to agriculture sites with limited Internet access

The agricultural sector uses IoT technology to monitor growing conditions, equipment performance, crop yield, and much more. Many of these devices use cellular connections to transmit data to the cloud for analysis which, as we’ve already discussed ad nauseam, introduces latency, increases bandwidth costs, and creates security risks. Edge computing moves this data processing on-site to reduce delays in critical applications like livestock monitoring and irrigation control. It also allows farms to process data on a local network, reducing their reliance on cellular networks that aren’t always reliable in remote and rural areas.

Edge computing for any use case

The potential uses for edge computing are nearly limitless. A shift toward distributed, real-time data analysis allows companies in any industry to get faster insights, reduce inefficiencies, and see more value from AI initiatives.

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Both edge computing and cloud computing involve moving computational resources – such as CPUs (central processing units), GPUs (graphics processing units), RAM (random access memory), and data storage – out of the centralized, on-premises data center. As such, both represent massive shifts in enterprise network designs and how companies deploy, manage, secure, and use computing resources. Edge and cloud computing also create new opportunities for data processing, which is sorely needed as companies generate more data than ever before, thanks in no small part to an explosion in Internet of Things (IoT) and artificial intelligence (AI) adoption. By 2025, IoT devices alone are predicted to generate 80 zettabytes of data, much of it decentralized around the edges of the network. AI, machine learning, and other data analytics applications, meanwhile, require vast quantities of data (and highly scalable infrastructure) to provide accurate insights. This guide compares edge computing vs cloud computing to help organizations choose the right deployment model for their use case.

Defining edge computing vs cloud computing

Edge computing involves deploying computing capabilities to the network’s edges to enable on-site data processing for Internet of Things (IoT) sensors, operational technology (OT), automated infrastructure, and other edge devices and services. Edge computing deployments are highly distributed across remote sites far from the network core, such as oil & gas rigs, automated manufacturing plants, and shipping warehouses. Ideally, organizations use a centralized (usually cloud-based) orchestrator to oversee and conduct operations across the distributed edge computing architecture.

Diagram showing an example edge computing architecture controlled by a cloud-based edge orchestrator.

Reducing the number of network hops between edge devices and the applications that process and use edge data enables real-time data processing, reduces MPLS bandwidth costs, improves performance, and keeps private data within the security micro-perimeter. Cloud computing involves using remote computing resources over the Internet to run applications, process and store data, and more. Cloud service providers manage the physical infrastructure and allow companies to easily scale their virtual computing resources with the click of a button, significantly reducing operational costs and complexity over on-premises and edge computing deployments.

Examples of edge computing vs cloud computing

Edge computing works best for workloads requiring real-time data processing using fairly lightweight applications, especially in locations with inconsistent or unreliable Internet access or where privacy/compliance is a major concern. Example edge computing use cases include:

• Orchestrating operational technology (OT) in remote manufacturing plants
• Monitoring safety and production data from sensors inside gas lines
• Analyzing patient data from wearable health monitors to provide real-time emergency alerts

Cloud computing is well-suited to workloads requiring extensive computational resources that can scale on-demand, but that aren’t time-sensitive. Example use cases include:

• Running large artificial intelligence and machine learning solutions
• Centralizing collaboration tools like source code version control
• Orchestrating distributed edge operations from a single platform

The advantages of edge computing over cloud computing

Using cloud-based applications to process edge device data involves transmitting that data from the network’s edges to the cloud provider’s data center, and vice versa. Transmitting data over the open Internet is too risky, so most organizations route the traffic through a security appliance such as a firewall to encrypt and protect the data. Often these security solutions are off-site, in the company’s central data center, or, best-case scenario, a SASE point-of-presence (PoP), adding more network hops between edge devices and the cloud applications that service them.  This process increases bandwidth usage and introduces latency, preventing real-time data processing and negatively affecting performance.

Edge computing moves data processing resources closer to the source, eliminating the need to transmit this data over the Internet. This improves performance by reducing (or even removing) network hops and preventing network bottlenecks at the centralized firewall. Edge computing also lets companies use their valuable edge data in real time, enabling faster insights and greater operational efficiencies.

Edge computing mitigates the risk involved in storing and processing sensitive or highly regulated data in a third-party computing environment, giving companies complete control over their data infrastructure. It can also help reduce bandwidth costs by eliminating the need to route edge data through VPNs or MPLS links to apply security controls.

Edge computing advantages:

• Improves network and application performance
• Enables real-time data processing and insights
• Simplifies security and compliance
• Reduces MPLS bandwidth costs

The disadvantages of edge computing compared to cloud computing

Cloud computing resources are highly scalable, allowing organizations to meet rapidly changing requirements without the hassle of purchasing, installing, and maintaining additional hardware and software licenses. Edge computing still involves physical, on-premises infrastructure, making it far less scalable than the cloud. However, it’s possible to improve edge agility and flexibility by using vendor-neutral platforms to run and manage edge resources. An open platform like Nodegrid allows teams to run multiple edge computing applications from different vendors on the same box, swap out services as business needs evolve, and deploy automation to streamline multi-vendor edge device provisioning from a single orchestrator.

Diagram showing how the Nodegrid Mini SR combines edge computing and networking capabilities on a small, affordable, flexible platform.

Organizations often deploy edge computing in less-than-ideal operating environments, such as closets and other cramped spaces that lack the strict HVAC controls that maintain temperature and humidity in cloud data centers. These environments also typically lack the physical security controls that prevent unauthorized individuals from tampering with equipment, such as guarded entryways, security cameras, and biometric locks. The best way to mitigate this disadvantage is with an environmental monitoring system that uses sensors to detect temperature and humidity changes that could cause equipment failures as well as proximity alarms to notify administrators when someone gets too close. It’s also advisable to use hermetically sealed edge computing devices capable of operating in extreme temperatures and with built-in security features making them tamper-proof.

Cloud computing is often more resilient than edge computing because cloud service providers must maintain a certain level of continuous uptime to meet service level agreements (SLAs). Edge computing operations could be disrupted by network equipment failures, ISP outages, ransomware attacks, and other adverse events, so it’s essential to implement resilience measures that keep services running (if in a degraded state) and allow remote teams to fix problems without having to be on site. Edge resilience measures include Gen 3 out-of-band management, control plane/data plane separation (also known as isolated management infrastructure or IMI), and isolated recovery environments (IRE).

Edge computing disadvantages:

• Less scalable than cloud infrastructure
• Lack of environmental and security controls
• Requires additional resilience measures

Edge-native applications vs cloud-native applications

Edge-native applications and cloud-native applications are similar in that they use containers and microservices architectures, as well as CI/CD (continuous integration/continuous delivery) and other DevOps principles.

Cloud-native applications leverage centralized, scalable resources to perform deep analysis of long-lived data in long-term hot storage environments. Edge-native applications are built to leverage limited resources distributed around the network’s edges to perform real-time analysis of ephemeral data that’s constantly moving. Typically, edge-native applications are highly contextualized for a specific use case, whereas cloud-native applications offer broader, standardized capabilities. Another defining characteristic of edge-native applications is the ability to operate independently when needed while still integrating seamlessly with the cloud, upstream resources, remote management, and centralized orchestration.

Choosing edge computing vs cloud computing

Both edge computing and cloud computing have unique advantages and disadvantages that make them well-suited for different workloads and use cases. Factors like increasing data privacy regulations, newsworthy cloud provider outages, greater reliance on human-free IoT and OT deployments, and an overall trend toward decentralizing business operations are pushing organizations to adopt edge computing. However, most companies still rely heavily on cloud resources and will continue to do so, making it crucial to ensure seamless interoperability between the edge and the cloud.

The best way to ensure integration is by using vendor-neutral platforms. For example, Nodegrid integrated services routers like the Gate SR provide multi-vendor out-of-band serial console management for edge infrastructure and devices, using an embedded Jetson Nano card to support edge computing and AI workloads. The ZPE Cloud management platform unifies orchestration for the entire Nodegrid-connected architecture, delivering 360-degree control over complex and highly distributed networks. Plus, Nodegrid easily integrates – or even directly hosts – other vendors’ solutions for edge data processing, IT automation, SASE, and more, making edge operations more cost-effective. Nodegrid also provides the complete control plane/data plane separation needed to ensure edge resilience.

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What is edge computing?

The Open Glossary of Edge Computing defines it as deploying computing capabilities to the edges of a network to improve performance, reduce operating costs, and increase resilience. Edge computing reduces the number of network hops between data-generating devices and the applications that process and use that data, mitigating latency, bandwidth, and security concerns compared to cloud or on-premises computing.

Image: A diagram showing the migration path from on-premises computing to edge computing, along with the associated level of security risk.

Edge-native applications

Edge-native applications are built from the ground up to harness edge computing’s unique capabilities while mitigating the limitations. They leverage some cloud-native principles, such as containers, microservices, and CI/CD (continuous integration/continuous delivery), with several key differences.

Edge-Native vs. Cloud-Native Applications

Source: Gartner

Edge-native applications integrate seamlessly with the cloud, upstream resources, remote management, and centralized orchestration, but can also operate independently as needed. Crucially, they allow organizations to actually leverage their edge data in real-time, rather than just collecting it for later processing.

Edge computing use cases

Nearly every industry has potential use cases for edge computing, including:

To learn more about the specific benefits and uses of edge computing for each industry, read Distributed Edge Computing Use Cases.

Edge computing architecture design

An edge computing architecture consists of six major components:

Click here to learn more about the infrastructure, networking, management, and security components of an edge computing architecture.

How to build an edge computing architecture with Nodegrid

Nodegrid is a Gen 3 out-of-band management platform that streamlines edge computing with vendor-neutral solutions and a centralized, cloud-based orchestrator.

Image: A diagram showing all the edge computing and networking capabilities provided by the Nodegrid Gate SR.

Nodegrid integrated services routers deliver all-in-one edge computing and networking functionality while taking up 1RU or less. A Nodegrid box like the Gate SR provides Ethernet and Serial switching, serial console/jumpbox management, WAN routing, wireless networking, and 5G/4G cellular for network failover or out-of-band management. It includes enough CPU, memory, and encrypted SSD storage to run edge computing workflows, and the x86-64bit Linux-based Nodegrid OS supports virtualized network functions, VMs, and containers for edge-native applications, even those from other vendors. The new Gate SR also comes with an embedded NVIDIA Jetson Orin NanoTM module featuring dual CPUs for EMO of AI workloads and infrastructure isolation.

Nodegrid SRs can also host SASE, SSE, and other security solutions, as well as third-party automation from top vendors like Redhat and Salt. Remote teams use the centralized, vendor-neutral ZPE Cloud platform (an on-premises version is available) to deploy, monitor, and orchestrate the entire edge architecture. Management, automation, and orchestration workflows occur over the Gen 3 OOB control plane, which is separated and isolated from the production network. Nodegrid OOB uses fast, reliable network interfaces like 5G cellular to enable end-to-end automation and ensure 24/7 remote access even during major outages, significantly improving edge resilience.

Streamline your edge deployment

The Nodegrid platform from ZPE Systems reduces the cost and complexity of building an edge computing architecture with vendor-neutral, all-in-one devices and centralized EMO. Request a free Nodegrid demo to learn more.

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Who does NIS2 apply to, and what are the consequences for noncompliance?

NIS2 applies to organizations providing services deemed “essential” or “important” to the European economy and society. Essential Entities (EE) generally have at least 250 employees, annual turnover of €50 million, or balance sheets of €43 million. Essential sectors include:

• Energy
• Transport
• Finance
• Public administration
• Health
• Space
• Water supply (drinking & wastewater)
• Digital infrastructure (e.g., cloud computing providers)

Important Entities (IE) generally have at least 50 employees, annual turnover of €10 million, or balance sheets of €10 million. Important sectors include:

• Postal services
• Waste management
• Chemicals
• Research
• Food
• Manufacturing (e.g., medical devices and other equipment)
• Digital providers (e.g., social networks, online marketplaces)

The NIS2 Directive outlines three types of penalties for noncompliance: non-monetary remedies, administrative fines, and criminal sanctions. Non-monetary remedies include things like compliance orders, binding instructions, security audit orders, and customer threat notification orders. Financial penalties for Essential Entities max out at €10 million or 2% of the global annual revenue, whichever is higher; for Important Entities, the maximum is €7 million or 1.4% of the global annual revenue, whichever is higher. NIS2 also directs member states to hold top management personally responsible for gross negligence in a cybersecurity incident, which could involve:

• Ordering organizations to notify the public of compliance violations
• Publicly identifying the people and/or entities responsible for the violation
• Temporarily banning an individual from holding management positions (EEs only)

Even the nonfinancial penalties of NIS2 noncompliance can affect revenue by causing reputational damage and potential lost business, so it’s crucial for IEs and EEs to be prepared when this directive takes effect in their state.

10 Minimum requirements for NIS2 compliance

The NIS2 directive requires essential and important entities to take “appropriate and proportional” measures to manage security and resilience risks and minimize the impact of incidents. It mandates an “all-hazards approach,” which means creating a comprehensive business continuity framework that accounts for any potential disruptions, whether they be natural disasters, ransomware attacks, or anything in between. Organizations must implement “at least” the following requirements as a baseline for NIS2 compliance (click links for more info):

1. Risk analysis and information system security policies

Organizations must create and update comprehensive policies covering cybersecurity risk analysis and overall IT system security practices. These policies should cover all the topics listed below and include specific consequences and/or corrective measures for failing to follow the outlined processes.

2. Security incident handling

Entities must implement incident-handling tools and practices to help accelerate resolution and minimize the impact on end users and other essential or important services. This includes mechanisms for identifying problems, triaging according to severity, remediating issues, and notifying relevant parties. NIS2 outlines a specific timeline for reporting significant security incidents to the relevant authorities:

• Within 24 hours – Entities must provide an early warning indicating whether they suspect an unlawful or malicious attack or whether it could have a cross-border impact.

• Within 72 hours – Entities must update the relevant authorities with an assessment of the attack, including its severity, impact, and indicators of compromise.

• Within one month – Organisations must submit a final report including a detailed description of the incident, the most likely root cause or type of threat, what mitigation measures were taken, and, if applicable, the cross-border impact. If the incident is still ongoing, entities must submit an additional report within one month of resolution.

3. Business continuity and crisis management

Essential and important entities must establish comprehensive business continuity and crisis management strategies to minimize service disruptions. These strategies should include redundancies and backups as part of a resilience system that can keep operations running, if in a degraded state, during major cybersecurity incidents. It’s also crucial to maintain continuous access to management, troubleshooting, and recovery infrastructure during an attack.

4. Supply chain security

Organizations must implement supply chain security risk management measures to limit the risk of working with third-party suppliers. These include performing regular risk assessments based on the supplier’s security and compliance history, applying zero-trust access control policies to third-party accounts, and keeping third-party software and dependencies up-to-date.

5. Secure network and IT system acquisition, development, and maintenance

Entities must ensure the security of network and IT systems during acquisition, development, and maintenance. This involves, among other things, inspecting hardware for signs of tampering before deployment, changing default settings and passwords on initial startup, performing code reviews on in-house software to check for vulnerabilities, and applying security patches as soon as vulnerabilities are discovered.

6. Cybersecurity and risk-management assessments

Organizations must have a way to objectively assess their cybersecurity and risk-management practices and remediate any identified weaknesses. These assessments involve identifying all the physical and logical assets used by the company, scanning for potential threats, determining the severity or potential impact of any identified threats, taking the necessary mitigation steps, and thoroughly documenting everything to streamline any reporting requirements.

7. Cybersecurity training

Essential and important entities must enforce cybersecurity training and basic security hygiene policies for all staff. This training should include information about the most common social engineering attacks, such as email phishing or vishing (voice phishing), compliant data handling practices, and how to securely create and manage account credentials.

8. Cryptography and encryption

NIS2 requires organizations to use cryptography to protect systems and data from tampering. This includes encrypting sensitive data and communications when necessary.

9. User access control and asset management

Entities must establish policies and procedures for employees accessing sensitive data, including least-privilege access control and secure asset management. This also includes mechanisms for revoking access and locking down physical assets when users violate safe data handling policies, or malicious outsiders compromise privileged credentials.

10. Multi-factor authentication (MFA) and encrypted communications

The final minimum requirement for NIS2 compliance is using multi-factor authentication (MFA) and continuous authentication solutions to verify identities, as described above. Additionally, entities must be able to encrypt voice, video, text, and internal emergency communications when needed.

How ZPE streamlines NIS2 compliance

EU-based entities classified as essential or important have limited time to implement all the security policies, practices, and tools required for NIS2 compliance. Using vendor-neutral, multi-purpose hardware platforms to deploy new security controls can help reduce the hassle and expense, making it easier to meet the October deadline. For example, a Nodegrid serial console from ZPE Systems combines out-of-band management, routing, switching, cellular failover, SSL VPN and secure tunnel capabilities, and environmental monitoring in a single device. The vendor-neutral Nodegrid OS supports GuestOS and containers for any third-party software, including next-generation firewalls (NGFWs), Secure Access Service Edge (SASE), automation tools like Puppet and Ansible, and UEBA. Nodegrid devices have strong hardware Roots of Trust with TPM 2.0, selectable encrypted cryptographic protocols and cipher suite levels, and configuration checksumTM. Plus, Nodegrid’s Gen 3 OOB creates the perfect foundation for infrastructure isolation, resilience systems, and isolated recovery environments.

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