Cybersecurity for edge computing

Cybersecurity for edge computing Inna Ushakova is the founder and CEO of AI EdgeLabs and Scalarr where she develops AI-powered cybersecurity solutions to help customers stay protected in a hyper-connected world.

Opportunities for edge computing in different industries are developing and adopting edge-native cybersecurity solutions should be integral.

As businesses strive to remain competitive and drive growth through digital transformation, enterprises increasingly incorporate edge computing into their operations.

The potential of edge computing as a catalyst for digital transformation is significant – the capability of edge computing to distribute applications and data to field locations plays a crucial role in driving digital transformation initiatives forward. 

Amidst the rapid growth and substantial investments in edge computing, there has been a tendency to overlook the critical aspect of ensuring edge security in these solutions. 

In this article, we will review the leading industries and use cases undergoing digital transformation. We will also address the primary challenges concerning edge security and provide suitable solutions.

What are the edge security challenges?

As organisations embrace digital transformation and leverage edge computing for their operations, they must ensure that the security strategy effectively addresses its critical aspects of scalability, performance, and interoperability. 


Scalability of edge security becomes a critical factor because businesses are dealing with many distributed edge devices. Given the large-scale deployment of these devices across diverse geographic locations, it is crucial to ensure that the selected security solution can seamlessly scale to meet the increasing device count and address the complexities inherent in various edge environments. 

In this context, the scalability of edge security pertains to the solution’s capability to manage growing workloads while ensuring optimal performance effectively. The edge security solution should be resource-efficient, protecting the infrastructure without negatively impacting the functionality of the edge application within the distributed edge environment. 

Another challenge in distributed edge computing environments involves the complexities of multi-tiered networks, encompassing diverse interfaces and connectivity options. A cyberattack targeting such systems can disrupt the connectivity of edge devices, posing a significant obstacle to the operability of commercial security solutions.

The edge security solution needs to be software-defined and specifically designed for the edge infrastructure to address this issue. By being software-defined, the organisation can easily deploy edge security across the entire edge infrastructure with a single click, even in remote areas.

The security solution must have a multi-layered approach to cybersecurity, which includes distributed edge-based firewalls, intrusion detection and prevention systems, endpoint security solutions, network traffic analysis, and regular software updates. This comprehensive approach ensures that even if one layer is compromised, the remaining layers can continue to provide robust protection.


As the threat landscape of IoT edge infrastructure continues to evolve, it is essential to thoroughly assess the performance implications to determine how well the security solution can protect against evolving threat sources to ensure the smooth operation of the distributed edge infrastructure. Organisations must have AI-powered security solutions capable of real-time threat detection and response that can quickly and accurately identify potential threats and provide timely alerts. With such capabilities, organisations can effectively mitigate risks and protect their IoT edge infrastructure from emerging threats.

To improve the accuracy of edge security solutions, they must integrate AI models to provide automated responses to cyber threats and isolate containerised applications that are compromised by cyber attacks, such as malware, ransomware, botnets, man-in-the-middle attacks, and unauthorised access to edge devices. The self-learning neural networks are integrated into edge security to deal with complex multi-layered threats and attacks.

Another challenge associated with performance is the ability of edge solutions to keep an organisation’s data secure within its host infrastructure without the need for data to be transferred outside of the edge to the cloud. It reduces the risks associated with data transfers and external dependencies. 

When thousands of edge devices are deployed in remote locations with unreliable internet connectivity, cloud-based solutions prove ineffective as they rely on constant internet access. Therefore, it becomes imperative to have an edge native security solution that operates seamlessly, even offline, guaranteeing optimal network security accuracy.


Ensuring the interoperability of edge security technologies with the distributed edge infrastructure is crucial for businesses to establish a cohesive security framework, as it can be challenging to achieve a unified approach that covers all edge devices. Therefore, organisations must thoroughly evaluate and select edge security technologies that meet their specific requirements and integrate smoothly with their existing systems, ensuring compatibility and interoperability.

The easy integration of the edge security solution into the host infrastructure is crucial. It should be completed within a few hours without causing system downtime or requiring significant modifications to the existing setup. This is particularly important due to the diverse devices, such as sensors and IoT devices. Kubernetes-based integration based in containers is one of the solutions for efficient deployment.

The chosen security solution should seamlessly adjust to the changing requirements of organisations, allowing for the integration of additional devices without compromising security measures or introducing vulnerabilities. In addition, the heterogeneous nature of these devices necessitates a security solution that can autonomously function on edge servers or IoT gateways, effectively securing the distributed resource environment. 

The self-adaptable feature of the autonomous AI-powered model stack enables it to effectively handle the constantly changing nature of the edge environment by seamlessly adjusting to variations in traffic topology within any infrastructure.

When selecting a security solution for their edge infrastructure, organisations need to prioritise compatibility with industry-level communication protocols such as Modbus, OPC UA, MQTT, and LoRaWAN. 

In addition to compatibility, resource consumption is a vital consideration when evaluating edge security solutions. The security solution should be designed lightweight, ensuring that it minimally impacts the overall resource utilisation of the system. 

While organisations are adopting edge computing as a primary driver for digital transformation due to its numerous benefits, businesses must prioritise addressing security issues, as they can have significant consequences on the business continuity, entire infrastructure and value proposition. To equip the organisation with efficient cyber tools that can effectively mitigate the evolving cyber threats, decision-makers should make ROI analysis to evaluate each criterion and ensure it will contribute to specific cyber security goals and strengthen the overall cyber resilience.

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