Artificial intelligence has proven to be useful in finding patterns that humans can't otherwise connect. By shuffling through huge amounts of data, AI can find the information it needs in seconds and take action much faster than a real human could. The problem is that AI relies on the data provided by humans, and unlike humans, it can't use empathy to tell when a detail is wrong.
This phenomenon is often called “bias.” Incomplete data pools or algorithms can result in false positives or false positives that can impact your results. Hackers are becoming more sophisticated each year, and this bias could well pose a threat to cyber security.
Security threats may be overlooked in the future
Security threats can come from many directions. However, China, Russia, and India top the list of countries with the highest number of cybercriminals. This marks those countries as “dangerous” and means the AI defense system will keep the majority of its traffic in these countries.
The problem is that the problem of cybercrime is steadily but surely worsening in many countries that we often consider to be a low priority. For example, Japan was previously considered a country with few cyberattacks and a low priority. However, in 2012, cyberattacks in the country increased by 8.3 percent, the highest number in more than 15 years.
Humans know this, but AI is not yet fully trained to stay focused on these emerging countries. These can cause malware detection systems to overlook certain threats simply because they originate from places not originally thought to be a problem. Not updating databases and algorithms regularly can seriously jeopardize your cybersecurity efforts.
Hackers are learning to take advantage
As more companies rely on AI systems to detect threats, hackers will learn to exploit this flaw. Many hackers have started using VPNs to hide the source of their attacks, choosing to appear in countries with low crime rates. This can bias AI defense systems and not consider threats until it's too late.
The biggest problem here is that development teams may not be aware that their systems have this bias. If you decide to rely solely on AI systems to detect these threats, malware can easily sneak into your system unnoticed. This is one of the main reasons why combining AI and human intelligence is recommended. Such collaboration helps minimize bias.
Increased risk of false positives
So far, we've discussed how bias in AI can lead to false negatives, incorrectly classifying real threats as non-issues. However, the opposite can also happen. AI bias can cause false positives in your reports, meaning you might find a problem when there isn't.
This factor is often overlooked, especially now that many companies are using AI detection tools to reduce false positives. However, this can also lead to overclassification. Especially since the detection system may not make any difference once you train it on the data. This is very problematic because social media has made slang and code words much more popular.
For example, those developing AI threat detection algorithms can associate slang or word abbreviations with phishing. This can result in important emails being classified as spam, causing production delays. When an employee casually communicates through email or chat, phishing alerts can be triggered unnecessarily and tickets sent to her cybersecurity team.
This may seem like a good thing, since the system is “at least detecting it.” However, these false positives can distract attention from the actual threat. AI is biased and cannot distinguish between spam and actual communication between teams, putting an unnecessary burden on security departments. These are the moments that hackers are likely to take advantage of to launch attacks.
The ever-evolving landscape of cybersecurity
Perhaps the biggest threat of AI bias to cybersecurity is its inability to adapt to changing threat landscapes. As technology continues to develop faster than ever, cybersecurity threats are also increasing. Hacker attacks are also becoming more sophisticated, with more than 150,000 attacks occurring per hour. Some of these attacks have patterns, while others try to find new ways to circumvent security.
Training an AI model can take months or even years before it can recognize new threats. This can create blind spots in a company's security systems, preventing malware detection systems from detecting attacks and leading to more breaches. This can be a big problem, especially when people rely on the fast capabilities of AI systems that can see large amounts of data. Human error can be a significant cybersecurity threat, but so is relying on systems that change slowly.
AI technology is continually evolving, especially when it comes to deep learning models. They are very opaque and complex, making them very difficult to manipulate. In this case, it can be very difficult to find out where the bias is ingrained and difficult to mitigate. Completely removing all bias is also not an ideal approach, as clear threats still exist. Therefore, it should not be ignored. This requires the use of hybrid models of human intelligence and AI. This prevents bias from growing out of control.
conclusion
Addressing bias in AI can be challenging, especially as the landscape evolves on multiple fronts. However, frequent testing can help mitigate bias and prevent attacks from spreading disproportionately. While bias can never be completely eliminated, controlled With appropriate human involvement.
