Edge security challenges

Edge computing is no longer just a buzzword but is now widely adopted by enterprises of all sizes to support their remote workforce and automate organizational operations. Industries such as manufacturing, smart cities, oil and gas, energy and utilities, and even automotive have particularly experienced massive adoption of edge computing technology. This includes deploying thousands of edge computing devices, ranging from remote desktop and mobile devices to smart cameras and sensors.

Gartner's report predicts that roughly 75% of enterprise-generated data will be processed outside of traditional data centers or clouds, citing the increasing number of IoT and edge devices being used by organizations. However, as these devices continue to rise, so do the security concerns surrounding them. Deploying hundreds of IoT edge devices to an organization's network creates numerous potential entry points for various cyberattacks and security breaches, highlighting the critical need for edge security.

Edge security has emerged as a primary solution to address the growing problem of security breaches, as each breach can have severe consequences for an enterprise's operations. Investing in an advanced edge security solution that incorporates artificial intelligence capabilities can be far more valuable than the cost of a security breach or the loss of sensitive data for a business.

In our previous coverage of edge security, we discussed the concept of the edge security strategy to safeguard organizational networks against possible points of failure and cyberattacks. We also explained a holistic cybersecurity approach opted by AI EdgeLabs’ advanced security solution that includes cyber artificial intelligence.

Challenges of edge computing

IoT edge devices are designed with limited onboard resources, but enough computing power to extract actionable insights from surrounding data. This has led to significant investment in adopting edge computing technology, as it can process vast amounts of data at the source and reduce latency. However, enterprises have faced serious repercussions due to the absence of edge security for their distributed edge infrastructure. 

The security challenges in edge computing arise from various sources, including hardware, software, and network infrastructure. Cyber threats originating from these sources can potentially undermine the entire edge computing infrastructure to an unprecedented extent. These are some of the identified security challenges in edge computing environments: 

1. Lack of security in hardware and software: The edge infrastructure is susceptible to various forms of attacks due to insufficient security measures in both hardware and software. One type of attack is remote code execution, where the attacker injects malicious code into the device or system, taking advantage of its software and hardware vulnerabilities. Once the attacker successfully executes the code, they can gain unauthorized access and take over the device or even the entire network.

2. Vulnerable communication between device and network: The communication between the IoT edge devices and the connected network is vulnerable and faces security challenges. One well-known form of attack is the Man-in-the-Middle attack, which involves intercepting and altering the communication between the device and the network it is connected to. These attacks can result in the theft of sensitive information or facilitate other types of attacks. 

3. Lack of network visibility: The edge computing approach allows IoT edge devices to remain constantly connected to the internet, making them easy targets for attackers due to their limited resources and capabilities. However, the absence of complete visibility and control over all network assets poses a significant challenge in detecting and responding to security breaches.

In the next section of this article, we will delve into how AI EdgeLabs' security solution addresses these challenges and assists large-scale businesses in securing their distributed edge network.

AI EdgeLabs can solve these challenges

Cybersecurity experts who investigate these challenges without a comprehensive understanding of the edge computing system tend to overlook security challenges that arise from the interaction boundaries between different domains. Furthermore, integrating security measures into the overall edge computing pipeline can create compatibility and redundancy issues.

To address the security vulnerabilities and threats unique to edge computing systems, a comprehensive approach is needed. At AI EdgeLabs, we have developed a security solution that considers the entire edge infrastructure, including both the network and endpoint devices. These features of the AI EdgeLabs security platform can solve the above-mentioned edge computing challenges:

1. AI EdgeLabs is an edge-first engine: AI EdgeLabs is an edge-centric platform that takes a holistic approach to cybersecurity solutions by designing them for specific technologies within various industries. Through advanced AI capabilities and automated response solutions, our team of security experts and engineers has developed AI EdgeLabs with the purpose of securing enterprise-grade edge infrastructure and deploying remote devices.

2. Support for advanced machine learning capabilities: AI EdgeLabs empowers enterprises with intelligent security measures that offer smart decision-making and autonomous actions to protect against ongoing attacks. Our advanced security platform is designed to quickly detect new vulnerabilities and provide protection against emerging threats within minutes by utilizing intuitive and responsive protocols for immediate remediation. 

3. Advanced network visibility: AI EdgeLabs security platform comes with advanced network visibility that utilizes machine learning and reinforcement learning algorithms for real-time threat prediction and risk assessment, providing early detection of potential threats.

Using AI technology, the sensor container-based application has the ability to detect threats at an advanced level with faster response times and increased accuracy. The AI model stacks use self-learning neural networks to combat multi-layered and complex threats. With a real-time approach to threat detection, potential threats can be stopped before they become serious breaches.

To learn more about our security solution, check out ‘AI EdgeLabs security platform.`