The promise of machine learning in cybersecurity, specifically inside the security operations center, is vast, but let’s not get ahead of ourselves. Machine learning can’t solve all your problems. Yet if you’re using the Siemplify Security Operations Platform, machine learning is playing an increasingly prominent role.

Find the Needle in the Haystack

What’s worse than having your organization breached? OK, maybe nothing. But imagine if after you have recovered from the breach, you realize that your security controls did generate an alert indicating a potential security issue but it was never investigated. With the amount of alert traffic flowing into a SOC every day, it is near impossible for analysts and managers to be sure a critical security issue does not get lost in the sea of mundane, useless alerts.

That’s where the Siemplify Security Operations Platform comes in: Using historical data and machine learning, the platform automatically ensures those critical cases – you know, the ones that could lead to significant damage to the organization if they go unresolved – bubble to the top.

Assign the Right Analyst to the Right Case

We all know one of the biggest issues in a typical SOC is the lack of resources. Skilled security analysts are in such high demand that not only is it hard to find them, but it is incredibly hard to keep them.

Since senior analysts are the lifeblood of most SOCs, we need to ensure they are being used properly. So, Siemplify machine learning automatically uses historical analyst/case results to recommend which cases should be assigned to which analyst, meaning a senior analyst doesn’t end up being assigned an “easy” case while a junior analyst gets a complex one.

Now, over time, as the junior analysts’ skills develop, the machine learning in the platform will adapt, potentially recommending different types of cases to the junior analyst. The key here is that the case assignment recommendation is data informed, eliminating the complaint of SOC management playing favorites.

Avoid Having to Reinvent the Wheel

Attacks, while varied, are not usually unique. In other words, if your organization has, for example, seen one brute-force attack you have seen hundreds, thousands or more. While the particulars may different, the essence of the attack is the same. While you could certainly go digging for other attacks to use as a reference, wouldn’t it be better if your SOAR solution could automatically find cases that are most similar to the one you are working? We think so too, that is why we built that capability into the platform.

Machine learning, when applied appropriately, can be a force multiplier for your security team. In the Siemplify Security Operations Platform, that is the intent. We use machine learning to augment the security teams ability to close cases faster and help SOC management get the most out of its team.

So, that’s it. Want to learn more? Check out this short video to see machine learning in the Siemplify Security Operations platform in action.

Steve Salinas is director of product marketing at Siemplify.