Edge computing is rapidly gaining popularity because it enables low-latency, high-efficiency, real-time operations by moving storage and processing to the edge of the network. If you're like many people, moving away from a centralized data center to a solution like this is promising. Unfortunately, there are significant cybersecurity concerns.
Key cybersecurity risks of edge computing
While this technology is promising, its deployment poses five cybersecurity risks:
1. IoT-specific vulnerabilities
Internet-connected devices have few built-in security controls and are notoriously vulnerable to man-in-the-middle attacks and botnets. The number of IoT attacks has increased from 32 million in 2018 to over 112 million in 2022. They pose the most significant cybersecurity risks because they are the basis of most edge computing solutions.
2. Excessive logs
When managing hundreds or thousands of endpoints, it can be difficult to stay on top of security logs. Considering that more than 50% of chief information security officers believe the number of daily alerts their teams receive is overwhelming, there is an additional responsibility to monitor distributed frameworks. It definitely poses a cybersecurity risk.
56% of security professionals already spend at least 20% of their time reviewing and responding to security alerts. Moving storage and processing to the edge of your network will likely inundate you with dozens of additional operations each day, increasing your chances of missing critical risks or wasting time on false positives.
3. data breach
Because a decentralized framework is not built, it is not possible to protect all IoT devices in the same way as a centralized data center. Data collected, stored, and shared at the edge is vulnerable to compromise through man-in-the-middle and ransomware attacks.
For example, consider a sniffing attack. An attacker can use a packet sniffer to intercept and steal data that is not encrypted in transit. Edge devices are particularly vulnerable because encryption is resource-intensive and often lacks sufficient processing power. Additionally, converting plaintext to ciphertext is slow, whereas speed is the key to this technology.
Four. Vast attack surface area
If you're like most people, you use edge computing to reduce latency, increase bandwidth, and improve performance. This means that devices should be placed as close to the network perimeter as possible. As a result, there is a vast distributed attack surface where each machine is a potential point of entry for an attacker.
Five. new budget limit
Edge computing is technically complex and requires significant communications and IT infrastructure investments. Even if you can afford this large upfront investment, device maintenance and labor costs leave less room in your budget for disaster, recovery, and other defenses.
Edge computing risk mitigation strategies
With strategic planning and investment, many cybersecurity risks can be overcome.
1. Use authentication control
Authentication controls such as multi-factor authentication, one-time passcodes, and biometrics prevent unauthorized access and prevent attackers from controlling your device or stealing your information. 27% of data breaches are caused by human error, so even if you trust your team, you should leverage this technology.
2. Automate log monitoring with AI
Automated log monitoring with artificial intelligence (AI) can help identify indicators of compromise (IOCs) before they develop into full-blown threats. Common examples of suspicious activity include unusual network activity or failed login attempts. Once you train your algorithm to detect them, you can make it work without human intervention.
Research shows that AI consistently outperforms humans in this area. In one study, he reported that the algorithm's recall rate for high-priority notifications was 99.6%, meaning it rarely missed important alerts. Additionally, the false positive rate was 0.001%. That's an impressive number when you consider that if you check 10,000 items every day, even 1% results in 10 additional alerts.
3. Authenticate devices and users
Edge device authentication validates all endpoints before accessing your network or system. This tool helps prevent intrusions by preventing people from connecting to vulnerable and potentially compromised machines. It can also help identify IOCs by allowing you to track anomalous activity against specific machines.
Four. Encrypt network traffic
Encryption is an essential cybersecurity best practice, but it is resource-intensive for widespread deployment in most edge computing applications. To avoid this, leverage data classification to determine which endpoints and information to prioritize. Second, it must be encrypted at rest and in transit (internally and externally) using a minimum key size.
Five. Deploy intrusion detection AI
Limitations such as energy, processing power, and memory hinder this computing technology and require dedicated intrusion detection mechanisms. Consider using deep learning algorithms that can be tailored to your application and adapt autonomously over time.
Deep learning AI can learn to recognize and classify previously unknown attack patterns and cyber threats. Because it can handle vast amounts of information, it can manage most, if not all, endpoints without having to integrate with each endpoint. Its scalability and ease of deployment make it an ideal solution for these computing environments.
Manage edge-related cybersecurity risks
Ignoring edge computing due to security weaknesses can put you at a competitive disadvantage and prevent you from operating efficiently. This is unacceptable for use cases such as self-driving car development, remote monitoring, and service delivery. If you want the benefits without the risks, consider leveraging mitigation strategies.
