Wi-Fi is rapidly replacing Ethernet as the standard for corporate local networks, particularly with the rise of mobile devices, which can only connect wirelessly.

However, according to Forbes, 40% of users reported corporate data breaches via Wi-Fi hacks, underscoring the vulnerability of wireless connections.
The author of this article, Umal Nanumura, a seasoned security engineering expert, noted that Wi-Fi hacking often grants attackers full access to the data on devices connected to the network.
In many cases, this can include access to sensitive accounts and financial resources. Here are several common Wi-Fi-related threats.
Warjacking involves attackers driving through cities searching for vulnerable Wi-Fi networks. Once identified, they can quickly steal data or take over the network.
In a cybersecurity project led by Sophos, over 80,000 Wi-Fi networks in London were scanned, and 29% were found to use outdated or no encryption at all, such as the WEP protocol, making them easy targets for hackers.
Even networks secured by WPA2, a modern encryption protocol, are not immune to attack, as vulnerabilities in WPA2 have been widely documented.
The rapid proliferation of IoT devices in corporate environments compounds these risks. Many office appliances now communicate wirelessly via IoT technology, opening new doors for cybercriminals.
For example, in a case where an IoT-connected aquarium thermostat was hacked, attackers were granted access to the computer systems, and sensitive data about VIP clients was compromised.
Small companies are especially vulnerable, often using inexpensive routers with weak security, making them easy targets for automated hacking tools.
While the threats are significant, the rise of AI and machine learning offers promising solutions to Wi-Fi security issues.
According to recent statistics, 35% of Chief Information Security Officers already use AI-driven cybersecurity tools, and more than 60% plan to adopt them soon.
AI can help fill the gap left by a shortage of qualified cybersecurity professionals, with over 80% of executives confident that generative AI will help mitigate this issue.
AI-powered cybersecurity tools, such as Darktrace, Crowdstrike, and Sentinel One, are increasingly used to secure wireless networks.
These tools, some of which feature self-learning capabilities, can detect threats without requiring constant human input.
Self-learning AI can autonomously cluster Wi-Fi traffic data, identifying potential threats before they become significant problems. However, self-learning systems also pose risks if they malfunction, as errors may go undetected for too long.
More reliable AI-based systems are those that combine human oversight with machine learning. In this hybrid approach, experts first train the AI systems, allowing them to audit the cybersecurity landscape effectively.
In addition to AI’s threat detection capabilities, machine learning allows AI systems to continuously monitor key Wi-Fi security parameters, such as CPU utilization and media access control (MAC), using protocols like NMP and SNMP.
This enables AI systems to collect large, consistent datasets—known as “metrics”—that are essential for training cybersecurity algorithms to detect abnormalities.
AI also excels at quickly identifying and mitigating new vulnerabilities as soon as they are discovered. Historically, Wi-Fi security has evolved alongside hacking techniques, from WEP to WPA2, and most recently WPA3. However, once hackers identify a vulnerability, they attack multiple networks simultaneously.
AI, with its ability to connect to threat databases in real-time, can learn about these vulnerabilities as quickly as the hackers, reducing the risk of corporate data loss.
Using a combination of AI tools, such as deep neural networks (DNN) and support vector machines (SVMs), is the most effective approach to securing Wi-Fi networks. These systems complement each other by offering strengths in different areas of cybersecurity.
For instance, convolutional neural networks can analyze vast amounts of data, while Random Forest and Naive Bayes algorithms are excellent at categorizing potential threats based on historical data.
It is advised small businesses to leverage cloud-based Wi-Fi solutions, which offer robust security features that individual organizations may not have the resources to implement.
A Cyber Threat Prevention Action Plan is better to be developed to explore cloud-based Wi-Fi technologies and safeguard corporate networks.
As businesses continue to adopt wireless networks, investing in AI-driven cybersecurity will be critical to protecting sensitive data and maintaining operational security.
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