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5 biggest cybersecurity risks in edge computing | #hacking | #cybersecurity | #infosec | #comptia | #pentest | #ransomware


Edge computing circuit diagram explained by IT expert

Edge computing is quickly gaining popularity because it enables low-latency, high-efficiency, real-time operations by moving storage and processing to the network’s boundary. If you are like many people, going from a centralized data center to a solution like this is promising. Unfortunately, significant cybersecurity concerns exist.

The Major Cybersecurity Risks of Edge Computing

While this technology is promising, its deployment comes with these five cybersecurity risks. 

1. IoT-specific vulnerabilities

Internet-connected devices are notoriously vulnerable to man-in-the-middle attacks and botnets because they have few built-in security controls. The number of IoT attacks surpassed 112 million in 2022, up from 32 million in 2018. They pose the most significant cybersecurity risk since they are fundamental to most edge computing solutions. 

2. Overabundance of logs

When you manage hundreds or thousands of endpoints, staying on top of security logs can be challenging. Considering over 50% of chief information security officers believe the number of daily alerts their teams receive is overwhelming, the additional responsibility of monitoring a decentralized framework would undoubtedly pose a cybersecurity risk. 

Fifty-six percent of security professionals already spend at least 20% of the workday reviewing and addressing security alerts. Moving your storage and processing to the network’s boundary would likely inundate you with dozens extra daily, making you more likely to overlook critical risks and waste time on false positives.

3. Data compromises

You cannot secure every IoT device in the same way as a centralized data center because a decentralized framework is not built for it. The data collected, stored and shared at the edge is more easily compromised by man-in-the-middle and ransomware attacks. 

Take sniffing attacks, for example, where attackers can intercept and steal unencrypted data during transmission using a packet sniffer. Edge devices are particularly vulnerable because encryption is resource-intensive and often lacks enough processing power. Moreover, turning plaintext into ciphertext is slow, whereas speed is the whole point of this technology.

4. Expansive attack surface

If you are like most people, you use edge computing to reduce latency, increase bandwidth and improve performance — meaning you have to place devices as close to the network’s boundary as possible. Consequently, you have an expansive, distributed attack surface, with each machine a potential entry point for attackers.

5. New budget limitations 

Edge computing is technically complex, requiring extensive telecommunications and IT infrastructure investments. Even if you can afford such a significant upfront investment, device maintenance and labor expenses leave less room in the budget for failures, recovery or the deployment of other defenses. 

Mitigation strategies for edge computing risks

You can overcome numerous cybersecurity risks with strategic planning and investments. 

1. Utilize authentication controls

Authentication controls like multi-factor authentication, one-time passcodes and biometrics prevent unauthorized access, preventing attackers from manipulating devices or stealing information. Since human error accounts for 27% of data breaches, you should leverage this technology even if you trust your team. 

2. Automate log monitoring with AI

Automating log monitoring with artificial intelligence (AI) can help you identify indicators of compromise (IOCs) before they develop into full-blown threats. Common examples of suspicious activity include unusual network activity and failed login attempts. Once you train your algorithm to detect them, you can let it work without human intervention. 

Research shows AI consistently outperforms humans in this area. One study reported their algorithm had a 99.6% recall rate for high-priority notifications, meaning it missed almost no critical alerts. Moreover, it had a 0.001% false positive rate — an impressive figure, considering even 1% translates into 10 extra alerts if you review 10,000 daily.

3. Authenticate devices and users

Edge device authentication verifies every endpoint before it can access networks or systems. With this tool, you can prevent people from connecting vulnerable, potentially compromised machines, stopping infiltration. It also helps you identify IOCs, as you can trace unusual activity to specific machines. 

4. Encrypt network traffic 

While encryption is an essential cybersecurity best practice, it is too resource-intensive for widespread deployment in most edge computing applications. To get around this, leverage data classification to decide which endpoints and information to prioritize. Then, you should encrypt at rest and in transit — internally and externally — using minimum key sizes. 

5. Deploy an intrusion detection AI

You need a purpose-built intrusion detection mechanism since limitations like energy, processing power and memory hold back this computing technology. Consider using a deep learning algorithm, which you can tailor to your applications and autonomously adapt over time.

A deep-learning AI can learn to recognize and classify previously unknown attack patterns and cyber threats. Since it can process tremendous amounts of information, it can manage most — if not all — endpoints without having to be integrated into each one. Its scalability and ease of deployment make it an ideal solution for these computing environments.

Managing edge-related cybersecurity risks is possible

Shrugging off edge computing because of its security weaknesses could put you at a competitive disadvantage and block you from highly efficient operations. That would be unacceptable for use cases like self-driving vehicle development, remote monitoring and service delivery. If you want the benefits without the risks, consider leveraging mitigation strategies.

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