Automating the Edge with Robotics, AI EdgeLabs on the convergence of edge security and distributed edge management’s edge infrastructure platform and AI EdgeLabs’ edge security offer a robust edge computing solution
Categories Brand Focus  |  Edge Computing News, AI EdgeLabs on the convergence of edge security and distributed edge management

Edge computing is an emerging technology that aims to bring computing and data storage close to the point of data generation, which is at the edge of the network. This technology offers several benefits to organizations such as lower latency, reduced network bandwidth usage, and improved reliability. However, as the number of IoT devices and edge computing nodes increases, it becomes increasingly difficult to manage and secure these devices. Autonomous AI capabilities will be needed to manage and secure edge environments. 

One of the biggest challenges with edge computing infrastructure is the presence of a large number of distributed edge locations, which may be more vulnerable and easier for hackers to access. This creates a new level of complexity and broadens the threat surface, as the data generated at the edge is often sensitive for the organization. To realize the potential for new business opportunities and competitive advantage, it is crucial to ensure that these edge devices and networks are protected against cyber threats. Unfortunately, there is limited discussion on developing edge security, which increases the risk of security breaches and loss of sensitive data.

It is important for businesses adopting edge computing to manage distributed edge locations and integrate edge security features that include autonomous AI capabilities. This article focuses on examining a collaborative solution between and AI Edgelabs that enables businesses to derive significant value from their edge computing solutions while simultaneously safeguarding their edge infrastructure and data. and AI EdgeLabs collaborate on securing edge devices has collaborated with AI EdgeLabs to offer businesses a comprehensive and advanced security solution for edge computing. The integration of their technologies includes incorporating AI EdgeLabs’ edge security with’s edge infrastructure platform, which provides scalability and security for businesses seeking to undertake digital transformation through edge computing.

As part of the collaboration,’s focus is on building highly secure managed infrastructure for the edge. The solution ensures that the infrastructure layer is highly available, enabling customers to deploy two or more nodes in an edge location for full fail-over and redundancy for mission-critical applications at the edge, thus mitigating risks caused by failures or power outages. has a small footprint that can deploy on robust and ruggedized edge devices to ensure reliability and availability — no matter how harsh the conditions are. The Sunlight NexCenter provides centralized management from hundreds to thousands of remote edge locations —  including the ability to provide automated updates, deploy applications with a single click, set fault tolerance and perform backups. provides untrusted application isolation capabilities at the application execution level, which results in enhanced security when untrusted applications run alongside mission-critical ones. One of the key benefits of Sunlight NexCenter infrastructure management is the fine-grained user controls, allowing companies to define user access levels for managing the infrastructure.

“We are thrilled to partner with to provide an integrated solution that enhances the security of Sunlight HyperConverged Edge infrastructure. autonomous cybersecurity AI is deployed as a solution in the Sunlight AppLibrary, and can easily protect 100s to 1000s of edge clusters at the click of a button,” says Tom Flink, CEO of

AI EdgeLabs’ security solution is integrated with the platform. It can be pre-configured on a Sunlight-powered edge device, shipped to a site and deployed with a single click. It provides AI-powered edge security for applications running on distributed edge locations. The solution has real-time threat detection and continuous monitoring of security properties, enabling instantaneous insights to combat security attacks. The distributed insights are used by AI algorithms to respond and prevent such security attacks that may start in one location but can quickly scale out to other locations.

AI EdgeLabs uses a multi-layered architecture to integrate artificial intelligence in its intrusion detection and prevent systems; these systems help detect and prevent distributed denial-of-service, botnets, malware, ransomware, brute force, and man-in-the-middle attacks. 

Traditional rule-based firewall security measures can be ineffective against novel malware and other types of attacks that have not previously been encountered in the edge infrastructure. However, AI EdgeLabs has developed an AI-based non-deterministic L2/L3 firewall that uses advanced machine learning models to detect and respond to even zero-day attacks that were previously unknown. By integrating with’s platform, users can have complete visibility of their fleet of edge devices and secure them with an end-to-end AI-powered edge security solution.

“We are excited to partner with to offer organizations a next-generation edge security solution enabling AI and automation, essential to defending an expanding Edge/IoT attack surface and responding to the massive increase in security events, which humans and traditional solutions cannot address,” says Inna Ushakova, CEO of

AI EdgeLabs offers a service level agreement to ensure that their edge security solution does not consume more than 4 percent CPU on average hardware, such as Raspberry Pi 4. This SLA is significant because customers can be sure that the edge device can handle other tasks efficiently without being overloaded by the AI applications. In addition to the CPU usage, AI EdgeLabs’ security solution consumes 50 Mb to 200 Mb of memory (depending on network condition) in runtime. This memory usage is also optimized to ensure the application can run efficiently on edge devices with limited resources.


Securing edge computing devices is a difficult task because of the limited resources available onboard, such as processing power and memory, and they may be deployed in distributed, remote and unmanned locations. With effective security and edge computing partners, like and AI EdgeLabs, these IoT edge devices can be secured against the vulnerability of attacks that could compromise the integrity and confidentiality of data. 

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