Managing a large number of endpoints poses considerable challenges, especially in handling security logs. Over half of chief information security officers find the volume of daily alerts overwhelming, and monitoring a decentralized framework further heightens cybersecurity risks.

Currently, 56% of security professionals dedicate at least 20% of their workday to reviewing and addressing security alerts. Moving storage and processing to the network's edge is likely to increase daily alerts, raising the risk of missing critical threats and wasting time on false positives.

1.Data Vulnerabilities

Securing every IoT device in a decentralized setup is less practical than in a centralized data center. Data at the edge is more susceptible to man-in-the-middle and ransomware attacks, such as sniffing attacks where unencrypted data is intercepted. Edge devices often lack the processing power for robust encryption, and encrypting data can slow down operations, conflicting with edge technology's primary goal.

2.Expanded Attack Surface

Edge computing, aimed at reducing latency, increasing bandwidth, and improving performance, requires placing devices near the network's edge, expanding the attack surface. Each device becomes a potential entry point for attackers. Research shows AI outperforms humans in this area, with one study noting an algorithm achieving a 99.6% recall rate for high-priority notifications and a 0.001% false positive rate, which is significant given the typical volume of alerts.

3.Device and User Authentication

Authenticating edge devices is crucial to ensure each endpoint is verified before accessing networks, preventing compromised machines from connecting and helping trace unusual activity back to specific devices.

4. Encrypting Network Traffic

While encryption is essential for cybersecurity, it can be too resource-intensive for widespread use in edge computing. To mitigate this, data classification should be employed to prioritize which endpoints and data require encryption. Encrypting data both at rest and in transit, using suitable key sizes, can balance security and performance. Edge computing's appeal lies in its ability to enable low-latency, high-efficiency, real-time operations by moving storage and processing to the network's boundary. However, this shift from centralized data centers comes with significant cybersecurity concerns.

Despite its benefits, edge computing brings five primary cybersecurity risks.

1. IoT-specific vulnerabilities: Internet-connected devices are prone to man-in-the-middle attacks and botnets due to limited built-in security controls. In 2022, IoT attacks surpassed 112 million, up from 32 million in 2018, posing significant risks as encryption is resource-intensive and often insufficiently supported by these devices. The process of encrypting data also slows operations, countering the primary advantage of edge technology.

2. Expansive attack surface: To reduce latency, increase bandwidth, and improve performance, edge devices must be placed near the network's edge, expanding the attack surface. Each device becomes a potential entry point for attackers.

3. New budget limitations: Edge computing's complexity requires substantial investments in telecommunications and IT infrastructure. Even with a significant upfront investment, maintenance and labor costs can strain budgets, leaving less room for handling failures, recovery, or deploying additional defenses.

Strategic planning and investments can help overcome numerous cybersecurity risks associated with edge computing.

1. Utilize authentication controls: Multi-factor authentication, one-time passcodes, and biometrics can prevent unauthorized access, reducing the risk of data breaches caused by human error, which accounts for 27% of such incidents.

2. Deploy an intrusion detection AI: A purpose-built intrusion detection system using deep learning algorithms can recognize and classify unknown attack patterns and cyber threats. Such AI can manage most endpoints without integration into each one, offering scalability and ease of deployment, making it ideal for edge computing environments.