{"id":2623,"date":"2019-06-18T16:07:01","date_gmt":"2019-06-18T20:07:01","guid":{"rendered":"https:\/\/solutionsreview.com\/security-information-event-management\/?p=2623"},"modified":"2021-05-13T09:42:54","modified_gmt":"2021-05-13T13:42:54","slug":"6-questions-about-machine-learning-in-siem-answered","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/security-information-event-management\/6-questions-about-machine-learning-in-siem-answered\/","title":{"rendered":"6 Questions About Machine Learning in SIEM (Answered!)"},"content":{"rendered":"

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So what exactly can machine learning in SIEM<\/a><\/span> offer your enterprise? How can this critical capability make or break your overall cybersecurity? And where does machine learning prove insufficient? <\/strong><\/em><\/p>\n

We answer these questions and other vital inquiries on machine learning in SIEM<\/span><\/strong><\/a> below!<\/span><\/p>\n


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The 6 Questions About Machine In SIEM<\/b><\/h2>\n

1. What is the Context for Machine Learning In SIEM?<\/b><\/h3>\n

Yesterday, at the Gartner Security and Risk Management Summit, Katell Thielemann\u2014Research Vice President at <\/span>Gartner<\/span><\/a>\u2014noted the importance of automation:<\/span><\/p>\n

\u201cWe are no longer asking the singular question of how we\u2019re managing risk and providing security to our organization. We\u2019re now being asked how we\u2019re 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.\u201d <\/span><\/p>\n

Of course, as befits the nature of the conference, Thielemann\u2019s observations focused on security and risk management. However, her points apply equally to SIEM, a major branch of modern enterprise cybersecurity. \u00a0\u00a0<\/span><\/p>\n

Indeed, SIEM provides many critical capabilities which enterprises outright require to best protect their sensitive digital assets. SIEM solutions can aggregate data generated throughout your enterprise\u2019s 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. <\/span><\/p>\n

In a cybersecurity climate that prioritizes detection over prevention, you can\u2019t 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.<\/span><\/p>\n

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. <\/span><\/p>\n

2. What is Machine Learning in SIEM?<\/b><\/h3>\n

We can\u2019t 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. <\/span><\/p>\n

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. \u00a0\u00a0<\/span><\/p>\n

3. So What Can Machine Learning Enable You to Do? <\/b><\/h3>\n

Put simply? Machine learning in SIEM can enable threat analytics and create notifications of risk in real time. <\/span><\/p>\n

Of course, we can drill down deeper into machine learning. Some potential offerings include: <\/span><\/p>\n

Prediction <\/b>\u00a0<\/span><\/h4>\n

Part of the appeal of machine learning algorithms lies in their 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. <\/span><\/p>\n

Clustering<\/b><\/h4>\n

Any AI program enables the classification of data. However, few programs can group data points and event information that it doesn\u2019t 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. <\/span><\/p>\n

Indeed, clustering proves essential to successful forensic analysis efforts. <\/span><\/p>\n

Incident Response Learning<\/b><\/h4>\n

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. <\/span><\/p>\n

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. <\/span><\/p>\n

With breaches becoming near-ubiquitous, strong incident response plans take on special importance. <\/span><\/p>\n

4. How Does All This Relate to SIEM?<\/b> \u00a0<\/span><\/h3>\n

A good question! Indeed, we can absolutely dive much deeper into machine learning in SIEM. More specifically, machine learning in SIEM can: <\/span><\/p>\n