3 Ways Predictive Analytics Can Boost Your Cybersecurity

It’s finally happened: the age of evil bots, intent on bringing down organizations through technological warfare, is finally here. Well,…

It’s finally happened: the age of evil bots, intent on bringing down organizations through technological warfare, is finally here.

Well, okay, that’s a slight exaggeration. We’re not quite in Terminator territory just yet. Our technology hasn’t become self-aware enough to take us on by itself – but their scope for destruction without human intervention has vastly increased.

That’s because, these days, the biggest threats to your cybersecurity aren’t individual geeks poking at the chinks in your company’s code. They’re extremely sophisticated attacks by large-scale criminal enterprises or even state actors, designed to exploit their target’s specific weaknesses, and incorporating elements of automation and machine learning to keep firing bigger, faster and more effective threats until they overload your system and burrow their way through the cracks.

What’s more, hacking styles evolve all the time. Simply installing a solid but static security solution is no longer enough: you need an excellent system for cyber security data analysis, or cybersecurity analytics, to make sure you’re agile enough to keep up, tracking new trends and figuring out how to counter them in real time… or even before.

The Challenge

Bring-Your-Own-Device (BYOD) and remote working policies are helping workplaces to become increasingly flexible and productive, but they can also create new weak spots that are hard for a centralized IT department to secure.

This, coupled with the sheer volume of data, drawn from all different sources, that today’s organizations process on a daily basis, makes it incredibly difficult to monitor where threats are coming from, or to identify the signals of a cyber attack while it’s underway. Unless they have a rock solid, supremely speedy BI solution, few companies have the capacity to analyze mammoth data sets in near to real time.

When it comes to cybersecurity, delays like these are disastrous. Even if they’re able to lock the door, by the time they’ve figured out it’s open, the horse has well and truly bolted.

And then there’s the sneaky nature of many cyber attacks today. Whereas in the past, hackers often breached a system just to show they could, many attacks today are designed to go undetected for as long as possible. Just look at nefarious threats like APTs: agents that quietly infiltrate your IT system and then feed out tiny pieces of sensitive data to an external server over time. In a situation like this, the change in pattern is marginal and blends into the organizational noise around it.

Perfecting an algorithm capable of dealing with these subtleties is very hard: you need it to pick up on tiny changes, without throwing out false positives that confuse things even more.

How Threats Are Detected

Typically, cyber attacks are detected by gathering up enough information about known breaches, phishing campaigns and existing malware as possible and tracking the “signatures” of these attacks so that systems can identify them on arrival. In other words, these dangerous elements are like wanted criminals – once they’ve committed or attempted enough crimes, their mugshot is circulated to all the security systems so that they can spot them as soon as they turn up.

This is a useful system, but it’s far from perfect. It doesn’t stop incredibly smart and dangerous hackers from getting to the goods on their first attempt, for example. It tells you the most popular tactics cybercriminals were using last month, but it does nothing to prepare you for what they might use next week.

This is why the savviest cybersecurity brains are moving from purely descriptive cyber security analytics to using predictive analytics in cyber security.

How Predictive Analytics Helps

Here are three major ways that predictive analytics is raising the bar in cyber security dashboards and data analysis.

1. Forewarned is Forearmed

Cyber security demands an ever more proactive approach. You need to be able to crunch your data, identify patterns and detect anomalies in near-real time so that you can close the floodgates before your data is stolen.

Predictive analytics doesn’t just tell you where cybercriminals have tried to attack in the past, it helps you to see where they are likely to hit next, where your weak points are, and how well prepared you are to counter an attack before it’s too late.

Leveraging predictive analytics successfully means mapping patterns in your IT system and drilling right down to the details so that, the moment something happens that seems out of the ordinary, you can jump in and investigate. It means keeping on top of where attempts are being made so that you can spot a trend and steel yourself well in advance.

2. It Can Handle Huge Volumes of Data

As we said above, one of the biggest challenges for a strong cybersecurity solution is that you’re dealing with enormous pools of data, which can be very hard to wade through, process, and analyze for useful insights. These data streams might come from a huge range of programs, databases, and devices, meaning you need a powerful BI tool to keep them all in sync.

Once you have a system capable of doing this, though, predictive analytics thrives on huge pools of data. In fact, the more inputs you have to work with, the more complete the picture you get – and the more accurate you can make your predictions.

3. It Automates Much of the Workload

Predictive analytics and machine learning can help you to manipulate your data and tease out crucial insights extremely quickly while taking much of the pressure off your IT department.

Of course, you’ll still need someone with an expert eye to accurately interpret findings and patterns, but much of the hard work of collecting, collating, and compiling reports is done automatically. This means that your IT team can concentrate their brainpower on identifying potential threats and moving fast to protect the system, rather than getting bogged down in day to day queries and reporting.

What’s more, as these systems get smarter and smarter, they’ll be able to take action to block new threats or fix weaknesses themselves… meaning you can fight those evil bot armies with a fearsome one of your own.

How Sisense Deploys Predictive Analytics in Cybersecurity

At Sisense, our BI system is designed to work with huge, disparate data sets rapidly, and to give users plenty of control over elements like running their own queries, visualizing results to highlight patterns, and drilling down to get the underlying details.

This speed, scope, and granularity means that many leading cybersecurity companies have begun using Sisense not only for their descriptive analytics but also to predict what’s coming next.

They do this by using the software to pinpoint potentially suspicious patterns as well as the performance of their system in testing. This involves monitoring bespoke dashboards that can be continually tweaked to reflect new trends, metrics, and indicators, and which work with multi-terabyte datasets drawn from all different sources and streams. Using these insights, they can then investigate whether they’re open to potential attack, or where there could be vulnerabilities in the system.

This means they’re able to create a complete data modeling, analytics, and visualization solution that helps steel them against attack, without having to sink huge amounts of money or resources into data warehousing, harmonizing data streams, or generating reports.

In other words, by leveraging the BI platform for data analytics, cybersecurity companies can pick up on the first signs of an intruder around the stable, fit all the right locks in all the right places, and make sure their horses stay firmly locked inside.

Security is just one aspect you should take into account when evaluating a BI tool. Read our whitepaper, “4 Steps to Successfully Evaluating BI Software,” for more.

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