6 Questions About Machine Learning in SIEM (Answered!)

• Updated on: 2019-06-19
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So what exactly can machine learning in SIEM offer your enterprise? How can this critical capability make or break your overall cybersecurity? And where does machine learning prove insufficient?

We answer these questions and other vital inquiries on machine learning in SIEM below!

Yesterday, at the Gartner Security and Risk Management Summit, Katell Thielemann—Research Vice President atGartner—noted the importance of automation:

“We are no longer asking the singular question of how we’re managing risk and providing security to our organization. We’re now being asked how we’re helping the enterprise realize more value while assessing and managing risk, security and even safety. The best way to bring value to your organizations today is to leverage automation.”

Of course, as befits the nature of the conference, Thielemann’s observations focused on security and risk management. However, her points apply equally to SIEM, a major branch of modern enterprise cybersecurity.   

Indeed, SIEM provides many critical capabilities which enterprises outright require to best protect their sensitive digital assets. SIEM solutions can aggregate data generated from through your enterprise’s network, including from applications and user behaviors. Additionally, SIEM can normalize the data and correlate security event data to discover hidden data breaches and alert your security team.

In a cybersecurity climate which prioritizes detection over prevention, you can’t afford to ignore these capabilities. Unfortunately, SIEM can present challenges to enterprise IT security teams unprepared for its demands. For example, SIEM requires considerable monitoring for proper rule correlation and alert investigations.

Obviously, these kinds of requirements can become overwhelming and thus generate cybersecurity burnout. This is where machine learning steps in. But we get ahead of ourselves.

We can’t begin to explore machine learning in SIEM without first defining the term. To wit, machine learning refers to a branch of artificial intelligence (AI). Broadly, machine learning uses AI algorithms to learn from its experiences over time after an initial data input.

Therefore, machine learning in SIEM takes cybersecurity rules and data to help facilitate security analytics. As a result, it can reduce the effort or time spent on rote tasks or even more sophisticated duties. With the right configurations, machine learning can actually make decisions based on the data it receives and change its behavior accordingly.   

Put simply? Machine learning in SIEM can enable threat analytics and create notifications of risk in real time.

Of course, we can drill down deeper into machine learning. Some potential offerings include:

Part of the appeal of machine learning algorithms lies in its ability to predict future data from previous patterns. As an example, it can use patterns from previous breaches to detect activities indicative of potential infiltration.

Any AI program enables the classification of data. However, few programs can group data points and event information that it doesn’t recognize. This is where clustering capabilities enter the equation: it allows machine learning to identify unknown values and group them together based on detected similarities.

Every enterprise, regardless of size, needs a comprehensive incident response plan. With this in hand, your enterprise should have the reflexes to identify and mitigate a data breach promptly.

While your employees must remain of your incident response plan to work optimally, ideally your network security should also stay informed. Machine learning can provide recommendations based on previous incident response efforts to facilitate future efforts.

With breaches becoming near ubiquitous, strong incident response plans take on special importance.

A good question! Indeed, we can absolutely dive much deeper into machine learning in SIEM. Some of More specifically, machine learning in SIEM can:

Machine learning in SIEM can benefit you in ways you’d never expect.

Cybersecurity and SIEM, in particular, constitute a boon to your business processes. For example, automating certain processes through machine learning frees your IT security team; they can focus on other IT infrastructure issues and help facilitate business processes rather than dealing with SIEM solutions.

Moreover, cybersecurity must become a critical consideration in any digital process. The costs of a data breach alone should deter any attempts to mitigate the importance of enforcing full security standards. After all, you would neglect physical security in the analog world. Why would you neglect your users’ digital safety? 

As such, machine learning in SIEM standardizes the workflows within your business network. Put another way, it eliminates most of the mundane tasks which come with SIEM, allowing you to more effectively innovate.    

Unfortunately, no. Having strong detection capabilities constitutes a major step in better stronger cybersecurity. In fact, it can help deter hackers from targeting your business in the first place; plenty of weaker targets exist.

However, cybersecurity can never become a set-it-and-forget-it affair. You can’t just trust the solution to work—you will still need a human to monitor the SIEM solution. Human intelligence ultimately beats AI every time, especially specialized human intelligence.

You need to feed your machine learning in SIEM with new threat intelligence and potential trends to most effectively function.

If you want to learn more about machine learning in SIEM, check out our 2019 SIEM Buyer’s Guide. We explore the top vendors in the field and their key capabilities!