Seven Common Cybersecurity Mistakes Made With AI

Seven Common Cybersecurity Mistakes Made With AI
Your company has started to use artificial intelligence (AI), but are you effectively managing the risks involved? It's a new growth channel with the potential to boost productivity and improve customer service. However, particular management risks need to be assessed in cybersecurity. Start by considering AI trends to put this risk in context.

Why is AI an emerging cybersecurity threat?


Artificial intelligence is a booming industry right now with large corporations, researchers, and startups all scrambling to make the most of the trend. From a cybersecurity perspective, there are a few reasons to be concerned about AI. Your threat assessment models need to be updated based on the following developments.


Early cybersecurity AI may create a false sense of security.


Most machine-learning methods currently in production require users to provide a training data set. With this data in place, the application can make better predictions. However, end-user judgment is a major factor in determining which data to include. This supervised learning approach is subject to compromise if hackers discover how the supervised process works. In effect, hackers could evade detection by machine learning by mimicking safe code.


AI-based cybersecurity creates more work for humans.


Few companies are willing to trust their security to machines. As a result, machine learning in cybersecurity has the effect of creating more work. WIRED magazine summarized this capability as follows: "Machine learning's most common role, then, is additive. It acts as a sentry, rather than a cure-all." As AI and machine learning tools flag more and more problems for review, human analysts will need to ..

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