By Morgan Jay, Area Vice President at Imperva.
We often question what drives the success behind enormous companies like Google and Amazon. A large part of the answer is machine learning. These companies have quickly adopted machine learning, finding smarter ways to apply it and changing the dynamic of how they work. With the extra analytical muscle that machine learning affords, they’re able to drive more intelligent and innovative projects which – let’s be honest – just work.
The result of the dominance of these companies is that we have become more familiar with the capabilities of machine learning than ever before. With mobile phones knowing us better than we know ourselves, and enterprise technologies predicting every next step, machine learning is clearly going to be a key part of our future.
It should come as no surprise, then, that the potential of adopting machine learning in the cybersecurity sector is now being recognised. As organisations collect increasingly more data, they are also met with a corresponding growth in security threats that they need to cope with. Therefore, developers are turning to alternative, smarter and more efficient ways to protect sensitive business data. So how can machine learning be applied to cybersecurity where it offers the most value?
The ideal use cases for machine learning are those that involve large data sets that would have been too time consuming to analyse in the past. These systems adapt and grow from experience, in a similar way to how humans hone their skills over time. Also like humans, machine learning will also be incorrect to a certain percentage, so they can’t completely replace human beings for decisions that require 10 ..
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