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Machine Learning and The Future of Security Management

by Brian Sims

We often question what drives the success behind enormous companies like Google and Amazon. A large part of the answer is machine learning, suggests Morgan Jay. These companies have quickly adopted machine learning, finding smarter ways to apply it and change 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’ve 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 cyber security sector is now being recognised. As organisations collect increasingly more data, they’re also met with a corresponding growth in security threats with which they need to cope. Therefore, developers are turning to alternative, smarter and more efficient ways in which to protect sensitive business data.

How, though, can machine learning be applied to cyber security where it offers the most value?

Ideal use cases

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 be incorrect to a certain percentage and so cannot completely replace human beings for decisions that require 100% certainty.

Crucially, machine learning applications require large amounts of data as fuel from which to learn. This is why cyber security is such fertile ground because the datasets that cyber security systems are generating can be gargantuan at times. When we also consider the fact that the cyber security field is facing a shortfall of 1.8 million qualified professionals by 2022, it’s clear the entire sector is under pressure to find new solutions.

As machine learning applications can learn new skills much faster than humans, they can close many of the skills gaps we’re likely to face. In a security setting, machine learning enables us to detect patterns and establish baseline data access behaviour using algorithms that learn through training or observation. This is particularly useful when we consider the ever-present threat posed to critical data by careless or malicious insiders.

Machine learning enables security teams to efficiently determine whether each access behaviour is normal, and then decide whether it should still be allowed to continue. The sheer volume of data involved within data access logs would have made these decisions impossible in times gone by, but machine learning can quickly process this data to provide us with clear and contextual results.

By establishing a baseline of data access patterns across your organisation, machine learning uses pattern recognition to identify normal behaviour for individuals in specific groups. Once an organisation’s normal data access patterns are identified, it becomes a far simpler task to filter those careless or malicious behaviours that threaten to compromise enterprise data.

Avalanche of alerts

Morgan Jay

Morgan Jay

Machine learning can also help us to climb out from under the avalanche of alerts that regularly bury our security teams. Imperva recently surveyed 179 IT professionals, and 29% of them told us they receive more than a million threat alerts each day, while more than half of respondents (55%) informed us that they’re dealing with more than 10,000 alerts each and every day.

Coping with so many potential threats on a daily basis can quickly cause alert fatigue. IT professionals receive so many alerts they simply cannot investigate them all, and have little idea on how to prioritise them. Machine learning helps these teams to categorise alerts so they know the high-risk alerts to start with in order to maximise the human effort required to investigate and mitigate.

Reducing alert fatigue and efficiently monitoring user access are just some of the ways machine learning can transform the cyber security landscape. We’re just beginning to see the potential for machine learning, and more is yet to come.

As newer and more effective ways of adopting these new technologies are uncovered, the future of cyber security is looking bright. While we cannot rely on these technologies for the talent shortfall the industry is facing, our most immediate and challenging problems can be solved if we make the most of these versatile solutions.

Morgan Jay is Area Vice-President at Imperva

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