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7 Security Benefits of Implementing FIPS 140-3 for Out-of-Band Management

ZPE Systems -FIPS 140-3

Out-of-band (OOB) management is essential for maintaining control over critical network infrastructure, especially during outages or cyberattacks. This separate management network enables administrators to remotely access, troubleshoot, and recover production equipment. However, managing network devices outside the main data path also brings unique security challenges, as these channels often carry sensitive control data and system access credentials.

Implementing FIPS 140-3-certified encryption within OOB systems can help organizations secure this vital access path to ensure that management data can’t be intercepted or manipulated by unauthorized actors. Here’s how FIPS 140-3 certification can enhance the security, reliability, and compliance of your out-of-band management.

What is FIPS 140-3 Certification?

FIPS (Federal Information Processing Standard) 140-3 is a high-level security standard developed by the National Institute of Standards and Technology (NIST). It specifies rigorous requirements for cryptographic modules used to protect sensitive data. FIPS 140-3 certification covers everything from data encryption to user authentication and physical security. For out-of-band management, FIPS 140-3 certification ensures that cryptographic components in hardware, software, and firmware meet stringent data security standards.

By implementing FIPS-certified solutions, organizations can ensure their OOB management is resilient against modern cyber threats, protecting both the control channels and the sensitive data they carry. Here are seven security benefits of implementing FIPS 140-3 for out-of-band management.

7 Security Benefits of Implementing FIPS 140-3 for Out-of-Band Management

1. Secure Encryption of Management Traffic

OOB management often involves remote access to routers, switches, servers and other critical devices. FIPS 140-3 certification guarantees that all cryptographic modules used in these systems have been rigorously tested to secure data in transit. Encrypting management traffic is crucial to prevent interception or manipulation by unauthorized users, particularly for tasks such as command execution, configuration updates, and device monitoring.

With FIPS-certified encryption, companies can protect OOB traffic between management devices and network components, so that only authorized administrators have access to sensitive system commands and device settings.

2. Enhanced Authentication and Access Control

OOB management solutions typically support different user roles, each with its own access privileges. FIPS 140-3-certified modules, like ZPE Systems’ Nodegrid, feature multi-factor authentication (MFA) to control who can initiate OOB management sessions. Certified solutions also include secure key management practices that prevent unauthorized access, ensuring that only verified users can control and modify network devices.

These protections mean FIPS-certified solutions help mitigate the risk of unauthorized users accessing high-value assets. This is especially important during ransomware recovery efforts, when teams need to launch a secure, Isolated Recovery Environment to combat an active attack in a compromised environment.

3. Protection Against Tampering and Physical Attacks

Many organizations deploy IT infrastructure in locations where physical device security is lacking. For example, remote colocations, unmonitored drilling sites, or rural health clinics can easily expose network infrastructure to device tampering. FIPS 140-3 certification mandates tamper-evident and tamper-resistant features to protect the cryptographic modules used in OOB systems. OOB solutions like ZPE Systems’ Nodegrid provide robust protection against tampering, with features including:

  • UEFI secure boot: Prevents the execution of unauthorized software during the boot process.
  • TPM 2.0: Ensures secure key generation and storage, so only authorized software can run.
  • Secure erase: Allows for deletion of all data from storage, so no data can be recovered from devices that have been tampered with.

These features prevent unauthorized individuals from physically accessing OOB equipment to intercept or modify management traffic. In remote and edge locations, FIPS-certified cryptographic modules provide robust protection against physical attacks, making it harder for adversaries to compromise OOB management pathways.

4. Compliant and Secure Logging of Access Activities

Because OOB management systems provide access to critical equipment, organizations need transparency into OOB users and their management activities. This means logging and auditing are essential to maintaining security and compliance. FIPS 140-3-certified modules support secure logging of all management activities, creating a clear audit trail of access attempts and security events. These logs are stored securely to prevent unauthorized users from altering or erasing them, providing valuable insights for security monitoring and incident response.

Secure logging is not only critical for monitoring access but also necessary for meeting regulatory compliance. FIPS 140-3 ensures that OOB management systems can satisfy audit requirements, making compliance easier and protecting organizations from potential regulatory penalties.

5. Meeting Regulatory Requirements in Sensitive Environments

Many industries handle sensitive data, especially government, healthcare, and finance. For organizations in these industries, it’s often mandatory to use FIPS-certified cryptographic solutions. FIPS 140-3 certification helps OOB management systems align with federal security regulations and standards like HIPAA and PCI-DSS. By deploying FIPS-certified encryption, organizations can comply with these standards, streamline audits, reduce the risk of regulatory penalties, and reinforce trust with customers.

6. Consistent Security Across Main and OOB Networks

It’s easy for organizations to focus mostly on securing the main network, while overlooking the security protections that they employ on their out-of-band network. FIPS-certified solutions help establish consistent security standards across both paths. This is especially important in protecting against lateral attacks, where hackers infiltrate one network and are then able to jump to the other. In cases where attackers gain access to one segment of the network, matching security protocols across the main and OOB networks prevents them from moving laterally into sensitive management channels.

Using FIPS 140-3-certified encryption across both networks also strengthens the organization’s ability to monitor, manage, and control devices, even when the primary network is under threat.

7. Securing Remote and Edge Devices

For organizations with remote infrastructure, such as telecom and retail, OOB management is critical for managing network devices in distant locations. However, these environments often lack the physical security of centralized data centers, making them vulnerable to tampering. FIPS-certified solutions ensure that all communication with remote OOB devices is encrypted, which protects management data from unauthorized access.

FIPS 140-3 certification also supports the resilience of IoT and edge devices, which often require OOB management for secure monitoring, patching, and configuration.

Implement the Most Secure Out-of-Band Management with ZPE Systems

Security in Layers

ZPE Systems’ Nodegrid is the industry’s most secure out-of-band management solution. Not only do we carry FIPS 140-3, SOC 2 Type 2, and ISO27001 certifications, but we also feature a Synopsys-validated codebase and dozens of security features across the hardware, software, and cloud layers. These are all part of a multi-layered, secure-by-design approach that ensures the strongest physical and cyber safeguards.

Download our pdf to explore more of our security assurance.

See FIPS-Certified Out-of-Band in Action

Our engineers are ready to walk you through our industry-leading out-of-band management. Use the button below to set up a 15-minute demo and explore FIPS 140-3 security features first-hand.

Edge Computing Platforms: Insights from Gartner’s 2024 Market Guide

Interlocking cogwheels containing icons of various edge computing examples are displayed in front of racks of servers

Edge computing allows organizations to process data close to where it’s generated, such as in retail stores, industrial sites, and smart cities, with the goal of improving operational efficiency and reducing latency. However, edge computing requires a platform that can support the necessary software, management, and networking infrastructure. Let’s explore the 2024 Gartner Market Guide for Edge Computing, which highlights the drivers of edge computing and offers guidance for organizations considering edge strategies.

What is an Edge Computing Platform (ECP)?

Edge computing moves data processing close to where it’s generated. For bank branches, manufacturing plants, hospitals, and others, edge computing delivers benefits like reduced latency, faster response times, and lower bandwidth costs. An Edge Computing Platform (ECP) provides the foundation of infrastructure, management, and cloud integration that enable edge computing. The goal of having an ECP is to allow many edge locations to be efficiently operated and scaled with minimal, if any, human touch or physical infrastructure changes.

Before we describe ECPs in detail, it’s important to first understand why edge computing is becoming increasingly critical to IT and what challenges arise as a result.

What’s Driving Edge Computing, and What Are the Challenges?

Here are the five drivers of edge computing described in Gartner’s report, along with the challenges that arise from each:

1. Edge Diversity

Every industry has its unique edge computing requirements. For example, manufacturing often needs low-latency processing to ensure real-time control over production, while retail might focus on real-time data insights to deliver hyper-personalized customer experiences.

Challenge: Edge computing solutions are usually deployed to address an immediate need, without taking into account the potential for future changes. This makes it difficult to adapt to diverse and evolving use cases.

2. Ongoing Digital Transformation

Gartner predicts that by 2029, 30% of enterprises will rely on edge computing. Digital transformation is catalyzing its adoption, while use cases will continue to evolve based on emerging technologies and business strategies.

Challenge: This rapid transformation means environments will continue to become more complex as edge computing evolves. This complexity makes it difficult to integrate, manage, and secure the various solutions required for edge computing.

3. Data Growth

The amount of data generated at the edge is increasing exponentially due to digitalization. Initially, this data was often underutilized (referred to as the “dark edge”), but businesses are now shifting towards a more connected and intelligent edge, where data is processed and acted upon in real time.

Challenge: Enormous volumes of data make it difficult to efficiently manage data flows and support real-time processing without overwhelming the network or infrastructure.

4. Business-Led Requirements

Automation, predictive maintenance, and hyper-personalized experiences are key business drivers pushing the adoption of edge solutions across industries.

Challenge: Meeting business requirements poses challenges in terms of ensuring scalability, interoperability, and adaptability.

5. Technology Focus

Emerging technologies such as AI/ML are increasingly deployed at the edge for low-latency processing, which is particularly useful in manufacturing, defense, and other sectors that require real-time analytics and autonomous systems.

Challenge: AI and ML make it difficult for organizations to determine how to strike a balance between computing power and infrastructure costs, without sacrificing security.

What Features Do Edge Computing Platforms Need to Have?

To address these challenges, here’s a brief look at three core features that ECPs need to have according to Gartner’s Market Guide:

  1. Edge Software Infrastructure: Support for edge-native workloads and infrastructure, including containers and VMs. The platform must be secure by design.
  2. Edge Management and Orchestration: Centralized management for the full software stack, including orchestration for app onboarding, fleet deployments, data storage, and regular updates/rollbacks.
  3. Cloud Integration and Networking: Seamless connection between edge and cloud to ensure smooth data flow and scalability, with support for upstream and downstream networking.

A simple diagram showing the computing and networking capabilities that can be delivered via Edge Management and Orchestration.

Image: A simple diagram showing the computing and networking capabilities that can be delivered via Edge Management and Orchestration.

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How ZPE Systems’ Nodegrid Platform Addresses Edge Computing Challenges

ZPE Systems’ Nodegrid is a Secure Service Delivery Platform that meets these needs. Nodegrid covers all three feature categories outlined in Gartner’s report, allowing organizations to host and manage edge computing via one platform. Not only is Nodegrid the industry’s most secure management infrastructure, but it also features a vendor-neutral OS, hypervisor, and multi-core Intel CPU to support necessary containers, VMs, and workloads at the edge. Nodegrid follows isolated management best practices that enable end-to-end orchestration and safe updates/rollbacks of global device fleets. Nodegrid integrates with all major cloud providers, and also features a variety of uplink types, including 5G, Starlink, and fiber, to address use cases ranging from setting up out-of-band access, to architecting Passive Optical Networking.

Here’s how Nodegrid addresses the five edge computing challenges:

1. Edge Diversity: Adapting to Industry-Specific Needs

Nodegrid is built to handle diverse requirements, with a flexible architecture that supports containerized applications and virtual machines. This architecture enables organizations to tailor the platform to their edge computing needs, whether for handling automated workflows in a factory or data-driven customer experiences in retail.

2. Ongoing Digital Transformation: Supporting Continuous Growth

Nodegrid supports ongoing digital transformation by providing zero-touch orchestration and management, allowing for remote deployment and centralized control of edge devices. This enables teams to perform initial setup of all infrastructure and services required for their edge computing use cases. Nodegrid’s remote access and automation provide a secure platform for keeping infrastructure up-to-date and optimized without the need for on-site staff. This helps organizations move much of their focus away from operations (“keeping the lights on”), and instead gives them the agility to scale their edge infrastructure to meet their business goals.

3. Data Growth: Enabling Real-Time Data Processing

Nodegrid addresses the challenge of exponential data growth by providing local processing capabilities, enabling edge devices to analyze and act on data without relying on the cloud. This not only reduces latency but also enhances decision-making in time-sensitive environments. For instance, Nodegrid can handle the high volumes of data generated by sensors and machines in a manufacturing plant, providing instant feedback for closed-loop automation and improving operational efficiency.

4. Business-Led Requirements: Tailored Solutions for Industry Demands

Nodegrid’s hardware and software are designed to be adaptable, allowing businesses to scale across different industries and use cases. In manufacturing, Nodegrid supports automated workflows and predictive maintenance, ensuring equipment operates efficiently. In retail, it powers hyperpersonalization, enabling businesses to offer tailored customer experiences through edge-driven insights. The vendor-neutral Nodegrid OS integrates with existing and new infrastructure, and the Net SR is a modular appliance that allows for hot-swapping of serial, Ethernet, computing, storage, and other capabilities. Organizations using Nodegrid can adapt to evolving use cases without having to do any heavy lifting of their infrastructure.

5. Technology Focus: Supporting Advanced AI/ML Applications

Emerging technologies such as AI/ML require robust edge platforms that can handle complex workloads with low-latency processing. Nodegrid excels in environments where real-time analytics and autonomous systems are crucial, offering high-performance infrastructure designed to support these advanced use cases. Whether processing data for AI-driven decision-making in defense or enabling real-time analytics in industrial environments, Nodegrid provides the computing power and scalability needed for AI/ML models to operate efficiently at the edge.

Read Gartner’s Market Guide for Edge Computing Platforms

As businesses continue to deploy edge computing solutions to manage increasing data, reduce latency, and drive innovation, selecting the right platform becomes critical. The 2024 Gartner Market Guide for Edge Computing Platforms provides valuable insights into the trends and challenges of edge deployments, emphasizing the need for scalability, zero-touch management, and support for evolving workloads.

Click below to download the report.

Get a Demo of Nodegrid’s Secure Service Delivery

Our engineers are ready to walk you through the software infrastructure, edge management and orchestration, and cloud integration capabilities of Nodegrid. Use the form to set up a call and get a hands-on demo of this Secure Service Delivery Platform.

PDU Remote Management

PDU Remote Management

The Hive SR PDU remote management solution from ZPE Systems.

PDUs (power distribution units) and busways are critical network infrastructure devices that control and optimize how power flows to equipment like servers, routers, firewalls, and switches. They’re difficult to manage remotely, so configuring and updating new devices or fixing problems typically requires tedious, on-site work. This difficulty is magnified in complex, distributed networks with hundreds of individual power devices that must be managed one at a time. What’s needed is a PDU remote management solution that unifies control over distributed devices. It should also streamline infrastructure management with an open architecture that supports third-party power software and automation.

The problem: PDU management is cumbersome for large, distributed networks

PDUs and busways are deployed across remote and distributed locations beyond the central data center, including edge computing sites, automated manufacturing plants, and colocations. They typically aren’t network-connected and do not come with up-to-date firmware at deployment time, requiring on-site technicians for maintenance. Upgrading and managing thousands of PDUs and busways requires hundreds of work hours from on-site IT teams who must manually connect to each unit.

The current solution: PDU remote management with jump boxes or serial consoles

Since most PDUs and busways can’t connect to the network, the only way to remotely manage them is to physically connect them via serial (a.k.a., RS-232) cable to a device that can be remotely accessed, such as an Intel NUC jump box or a serial console.

Unfortunately, jump boxes usually aren’t set up to manage more than one serial connection at a time, so they only solve the remote access problem without providing any centralized management of multiple PDUs or multiple sites. Jump boxes are often deployed without antivirus or other security software installed and with insecure, unpatched operating systems containing potential vulnerabilities, leaving branch networks exposed.

On the other hand, serial consoles can manage multiple serial devices at once and provide remote access, but they often don’t integrate with PDU/busway software and only support a few chosen vendors, which limits their control capabilities and may prevent remote firmware updates. They’re also usually single-purpose devices that take up valuable rack space in remote sites with limited real estate and don’t interoperate with third-party software for automation, monitoring, and security.

The Hive SR + ZPE Cloud: A next-gen PDU remote management solution

The ZPE Cloud and Nodegrid Hive SR solutions for PDU remote management.
The Hive SR is an integrated branch services router from the Nodegrid family of vendor-neutral infrastructure management solutions offered by ZPE Systems. The Hive automatically discovers power devices and provides secure remote access, eliminating the need to manage PDUs and busways on-site. The ZPE Cloud management platform gives IT teams centralized control over power devices and other infrastructure at all distributed locations so they can update or roll-back firmware, configure and power-cycle equipment, and see monitoring alerts.

The ZPE Cloud PDU remote management solution from ZPE Systems.

In addition to integrated branch networking capabilities like gateway routing, switching, firewall, Wi-Fi access point, 5G/4G cellular WAN failover, and centralized infrastructure control, the Hive SR and ZPE Cloud also deliver vendor-neutral out-of-band (OOB) management. ZPE’s Gen 3 OOB solution creates an isolated management network that doesn’t rely on production resources and, as such, remains remotely accessible during major outages, ransomware infections, and other adverse events. This gives IT teams a lifeline to perform remote recovery actions, including rolling-back PDU firmware updates, power-cycling hung devices, and rebuilding infected systems, without the time and expense of an on-site visit.

A diagram showing how the Nodegrid Hive SR can be deployed for PDU remote management.

The Hive and ZPE Cloud have open architectures that can host or integrate other vendors’ software for PDU/busway management, NetOps automation, zero-trust and SASE security, and more. Administrators get a single, unified, cloud-based platform to orchestrate both automated and manual workflows for PDUs, busways, and any other Nodegrid-connected infrastructure at all distributed business sites. Plus, all ZPE solutions are frequently patched and protected by industry-leading security features to defend your critical branch infrastructure.

 

 

Download our Automated PDU Provisioning and Configuration solution guide to learn more about vendor-neutral PDU remote management with Nodegrid devices like the Hive SR.
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Download our Centralized IT Infrastructure Management and Orchestration solution guide to learn how ZPE Cloud can improve your operational efficiency and resilience.
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Edge Computing Use Cases in Banking

financial services

The banking and financial services industry deals with enormous, highly sensitive datasets collected from remote sites like branches, ATMs, and mobile applications. Efficiently leveraging this data while avoiding regulatory, security, and reliability issues is extremely challenging when the hardware and software resources used to analyze that data reside in the cloud or a centralized data center.

Edge computing decentralizes computing resources and distributes them at the network’s “edges,” where most banking operations take place. Running applications and leveraging data at the edge enables real-time analysis and insights, mitigates many security and compliance concerns, and ensures that systems remain operational even if Internet access is disrupted. This blog describes four edge computing use cases in banking, lists the benefits of edge computing for the financial services industry, and provides advice for ensuring the resilience, scalability, and efficiency of edge computing deployments.

4 Edge computing use cases in banking

1. AI-powered video surveillance

PCI DSS requires banks to monitor key locations with video surveillance, review and correlate surveillance data on a regular basis, and retain videos for at least 90 days. Constantly monitoring video surveillance feeds from bank branches and ATMs with maximum vigilance is nearly impossible for humans, but machines excel at it. Financial institutions are beginning to adopt artificial intelligence solutions that can analyze video feeds and detect suspicious activity with far greater vigilance and accuracy than human security personnel.

When these AI-powered surveillance solutions are deployed at the edge, they can analyze video feeds in real time, potentially catching a crime as it occurs. Edge computing also keeps surveillance data on-site, reducing bandwidth costs and network latency while mitigating the security and compliance risks involved with storing videos in the cloud.

2. Branch customer insights

Banks collect a lot of customer data from branches, web and mobile apps, and self-service ATMs. Feeding this data into AI/ML-powered data analytics software can provide insights into how to improve the customer experience and generate more revenue. By running analytics at the edge rather than from the cloud or centralized data center, banks can get these insights in real-time, allowing them to improve customer interactions while they’re happening.

For example, edge-AI/ML software can help banks provide fast, personalized investment advice on the spot by analyzing a customer’s financial history, risk preferences, and retirement goals and recommending the best options. It can also use video surveillance data to analyze traffic patterns in real-time and ensure tellers are in the right places during peak hours to reduce wait times.

3. On-site data processing

Because the financial services industry is so highly regulated, banks must follow strict security and privacy protocols to protect consumer data from malicious third parties. Transmitting sensitive financial data to the cloud or data center for processing increases the risk of interception and makes it more challenging to meet compliance requirements for data access logging and security controls.

Edge computing allows financial institutions to leverage more data on-site, within the network security perimeter. For example, loan applications contain a lot of sensitive and personally identifiable information (PII). Processing these applications on-site significantly reduces the risk of third-party interception and allows banks to maintain strict control over who accesses data and why, which is more difficult in cloud and colocation data center environments.

4. Enhanced AIOps capabilities

Financial institutions use AIOps (artificial intelligence for IT operations) to analyze monitoring data from IT devices, network infrastructure, and security solutions and get automated incident management, root-cause analysis (RCA), and simple issue remediation. Deploying AIOps at the edge provides real-time issue detection and response, significantly shortening the duration of outages and other technology disruptions. It also ensures continuous operation even if an ISP outage or network failure cuts a branch off from the cloud or data center, further helping to reduce disruptions and remote sites.

Additionally, AIOps and other artificial intelligence technology tend to use GPUs (graphics processing units), which are more expensive than CPUs (central processing units), especially in the cloud. Deploying AIOps on small, decentralized, multi-functional edge computing devices can help reduce costs without sacrificing functionality. For example, deploying an array of Nvidia A100 GPUs to handle AIOps workloads costs at least $10k per unit; comparable AWS GPU instances can cost between $2 and $3 per unit per hour. By comparison, a Nodegrid Gate SR costs under $5k and also includes remote serial console management, OOB, cellular failover, gateway routing, and much more.

The benefits of edge computing for banking

Edge computing can help the financial services industry:

  • Reduce losses, theft, and crime by leveraging artificial intelligence to analyze real-time video surveillance data.
  • Increase branch productivity and revenue with real-time insights from security systems, customer experience data, and network infrastructure.
  • Simplify regulatory compliance by keeping sensitive customer and financial data on-site within company-owned infrastructure.
  • Improve resilience with real-time AIOps capabilities like automated incident remediation that continues operating even if the site is cut off from the WAN or Internet
  • Reduce the operating costs of AI and machine learning applications by deploying them on small, multi-function edge computing devices. 
  • Mitigate the risk of interception by leveraging financial and IT data on the local network and distributing the attack surface.

Edge computing best practices

Isolating the management interfaces used to control network infrastructure is the best practice for ensuring the security, resilience, and efficiency of edge computing deployments. CISA and PCI DSS 4.0 recommend implementing isolated management infrastructure (IMI) because it prevents compromised accounts, ransomware, and other threats from laterally moving from production resources to the control plane.

IMI with Nodegrid(2)

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.

Edge-Management-980×653

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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AI Orchestration: Solving Challenges to Improve AI Value

AI Orchestration(1)
Generative AI and other artificial intelligence technologies are still surging in popularity across every industry, with the recent McKinsey global survey finding that 72% of organizations had adopted AI in at least one business function. In the rush to capitalize on the potential productivity and financial gains promised by AI solution providers, technology leaders are facing new challenges relating to deploying, supporting, securing, and scaling AI workloads and infrastructure. These challenges are exacerbated by the fragmented nature of many enterprise IT environments, with administrators overseeing many disparate, vendor-specific solutions that interoperate poorly if at all.

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.

A diagram showing isolated management infrastructure with the Nodegrid Serial Console Plus.

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

Edge Computing Use Cases in Telecom

This blog describes four edge computing use cases in telecom before describing the benefits and best practices for the telecommunications industry.
Telecommunications networks are vast and extremely distributed, with critical network infrastructure deployed at core sites like Internet exchanges and data centers, business and residential customer premises, and access sites like towers, street cabinets, and cell site shelters. This distributed nature lends itself well to edge computing, which involves deploying computing resources like CPUs and storage to the edges of the network where the most valuable telecom data is generated. Edge computing allows telecom companies to leverage data from CPE, networking devices, and users themselves in real-time, creating many opportunities to improve service delivery, operational efficiency, and resilience.

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.

IMI with Nodegrid(2)
To ensure the scalability and flexibility of edge architectures, the best practice is to use vendor-neutral platforms to host, connect, and secure edge applications and workloads. Moving away from dedicated device stacks and taking a “platformization” approach allows organizations to easily deploy, update, and swap out functions and services on demand. For example, Nodegrid edge networking solutions have a Linux-based OS that supports third-party VMs, Docker containers, and NFVs. Telecom companies can use Nodegrid to run edge computing workloads as well as asset management software, customer experience analytics, AIOps, and edge security solutions like SASE.

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.

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Finally, to ensure teams have holistic oversight of the distributed edge computing architecture, the best practice is to use a centralized, cloud-based edge management and orchestration (EMO) platform. This platform should also be vendor-neutral to ensure complete coverage and should use out-of-band management to provide continuous management access to edge infrastructure even during a major service outage.

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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