With the world moving towards a future that relies heavily on data, edge computing has become an important and robust technology to facilitate this rapid expansion. In order to accommodate this growth, there has been a significant increase in the number of IoT edge devices that can analyze data on the device itself, thus reducing latency and enhancing efficiency. Nevertheless, as this technology advances and edge infrastructure becomes more widely adopted, enterprise customers have become increasingly concerned about security at the edge.
Edge security is a cybersecurity strategy that operates at the periphery of an organization's network to safeguard it against possible points of failure or cyberattacks. By focusing on securing the edge computing infrastructure, which includes both the network and endpoint devices, this approach helps to prevent unauthorized access to sensitive organizational data. Edge security solutions like AI EdgeLabs include advanced features such as AI-based firewalls, real-time threat detection, automated remediation and response, and malware protection.
According to the latest Worldwide Edge Spending Guide report from IDC, it has been projected that edge computing will experience significant growth, reaching $208 billion by 2023, representing a 13.1 percent increase from the previous year. Experts argue that the robust performance and security features of edge computing infrastructure are compelling reasons for organizations to increase spending and establish new infrastructure.
We are of the opinion that with organizations increasingly allocating resources toward their edge infrastructure and leveraging edge computing to bolster their supply chain operations, it is critical that adopters prioritize the security of their edge network. Specifically, the exponential growth of mobile devices has created an environment in which hackers can easily exploit vulnerabilities and infiltrate targeted computer networks.
“The edge brings new capabilities and performance benefits to the enterprise when secure connectivity is established,” says Paul Hughes, research director of Future of Connectedness at IDC.”
The market for edge security solutions is highly heterogenous, referring to the fact that there is a complex and diverse landscape with its own unique features and capabilities. While these solutions may effectively address specific vulnerabilities or endpoints, they often do not provide comprehensive coverage for the entire edge infrastructure. In particular, the complexity of edge computing ecosystems, which may combine a diverse range of endpoint devices, applications, and network connectivity options, makes it challenging to implement a unified edge security strategy.
Akamai Technology provides diverse security solutions that can address various aspects of edge security, such as DNS security, DDoS (Distributed Denial of Service) protection, bot management, and scrubbing centers. While each service addresses a specific aspect of edge security, this approach may not provide comprehensive protection for the entire edge infrastructure.
Imperva's data security fabric (DSF) focuses on securing data in multi-cloud and hybrid environments by allowing organizations to protect their sensitive data and manage digital risks, securely moving strategic data to the cloud.
AI EdgeLabs takes a different approach by keeping an organization's data secure within its host infrastructure. There is no need for outside data transitions with AI EdgeLabs, as the data is processed and secured within the enterprise environment at the edge network.
Palo Alto Networks provides a cybersecurity solution called Nebula PAN-OS software, which utilizes cloud computing to secure enterprise infrastructure. This solution is specifically designed to address zero-day threats by using the processing power of the cloud. The PA-3400 series is an example of a device that uses the PAN-OS software as its control element, which is the same software that runs on all Palo Alto Networks devices.
In a previous article, we discussed how AI EdgeLabs has implemented a unique approach to enterprise-level zero-trust edge security. This approach equips CISOs with the essential tools to accommodate new technologies, including proactive edge security techniques and automated AI.
As edge computing continues to evolve and organizations invest more resources into this distributed computing environment, it is becoming increasingly important to take a comprehensive approach to secure the edge network. In the following section of this article, we will discuss how AI EdgeLabs has adopted a holistic approach to edge security that focuses on the entire edge infrastructure rather than securing individual entities within the layered edge infrastructure.
A holistic cybersecurity approach considers the entire edge infrastructure, including the network and endpoint devices, to secure them. It also recognizes the complex landscape of the edge security solution and provides a comprehensive security strategy capable of addressing vulnerability.
AI EdgeLabs is an “edge-first engine” unlike other edge security platforms, which are heterogeneous in nature. The security solution is designed to be seamlessly deployed directly on edge computing across distributed infrastructure within an hour, which provides several cybersecurity advantages to enterprises. The platform can detect and prevent cyberattacks in real time, even in offline mode or unstable connectivity, while safeguarding sensitive information.
With AI EdgeLabs, businesses get complete visibility of IoT assets and edge environment, allowing them to understand all the endpoint devices in their network. This capability helps organizations to effectively manage and secure their network by detecting security incidents.
Discovering hidden assets has been a key lesson for organizations to consider after the release of Log4j zero-day vulnerability. Our platform is able to monitor hidden IoT assets inside the edge network, which may not be regularly maintained by an organization. By detecting these hidden assets, our platform helps you proactively measure to secure your network and safeguard against potential threats.
A holistic approach to edge security becomes very important in a distributed edge ecosystem where many moving parts of the system must be monitored and protected.