What do SIEM components actually do for enterprises? Moreover, why should your enterprise care about SIEM components and capabilities? Which SIEM components should you consider important when making cybersecurity decisions
To understand the answer to these critical cybersecurity questions, let’s take a look at some of the most crucial SIEM components for enterprises. These include AI in SIEM (machine learning), behavioral analysis, log collection and management, and managed security services.
Let’s take a look!
Critical SIEM Components for Enterprise Cybersecurity
What Do Managed Security Services for Enterprises Do?
Let’s start with managed security services, as they aren’t exactly SIEM components. Instead, it is another branch of SIEM solutions, but one that could prove crucial to your cybersecurity.
Managed security services work to alleviate the problem created by missing IT security talent through third-party services. In fact, managed security services for enterprises operate through third-parties to conduct cybersecurity monitoring and management.
Thus, it conducts incident detection and response, as well as incident containment. Importantly, these managed security services can operate twenty-four hours a day, seven days a week. If your IT security team tried to maintain that schedule, they would quickly suffer burnout.
Yet having around-the-clock monitoring proves essential for protecting your databases and servers from hackers. After all, hackers could strike at any hour and may plan their attacks to take advantage of lapses in monitoring. Moreover, active threat hunting could uncover dwelling threats lurking in your network.
Also, you can receive these services onsite or remotely through the cloud or hybrid environments. They can serve as your IT security team or work alongside them by filling in the night-shift, so to speak. As such, they can help you maintain your SIEM components.
What Does AI in SIEM Offer Your Enterprise?
Usually, AI in SIEM refers to machine learning, one of the most vital SIEM components. Machine learning about threats as it acquires threat intelligence and deflects attacks in the field. Additionally, machine learning enables easier threat detection across large data sets, alleviating some threat hunting responsibilities from your security team.
As it learns, SIEM AI begins to also learn about malicious behavior warnings beyond its initial data input. Therefore, it can stop threats your cybersecurity never saw before, helping to deepen your security layers. In fact, with the right configurations, machine learning can even make decisions and change its behavior accordingly.
Also, it helps prevent blind spots appearing as your enterprise infrastructure scales and grows.
What Does Behavioral Analysis in SIEM Offer Your Enterprise
Often, enterprises neglect these SIEM components because they fail to recognize their importance. Behavioral analytics is one example of neglected SIEM components.
Behavioral analytics examines trends, patterns, and activities among your users and applications. It looks for habits and quirks in workflows and creates profiles for each user. For example, Alice normally accesses Database B four times during the workday. With more next-generation technology, it also recognizes the endpoint she uses to make these access requests. The behavioral analytics SIEM component uses this information to establish a behavioral baseline.
In this example, Alice instead requests access to Database C, and does so ten times. Additionally, she makes this request from an endpoint thousands of miles from where she does normally. With the behavioral baseline established, your SIEM solution recognizes both of these behaviors as outside the baseline.
Then, your cybersecurity solution can put an injunction on the access requests and alert your security team to investigate. Possibly, the behaviors represent an unusual day for Alice; she could be on a business trip and in need of information not usually pertinent to her job title. Your security team can let your SIEM know about the special circumstances and allow Alice access.
However, this behavior could also indicate hackers compromising Alice’s credentials. In that case, your security team can initiate incident response and shut down the hackers and close any discovered vulnerabilities.
In short, behavioral analytics uses machine learning and statistical analysis to monitor your users’ behaviors and find anomalies. It’s among the critical SIEM components for a reason.
Why Does Log Management Matter?
As your enterprise embraces IT Infrastructure improvements, you face a problem with network scaling. This problem especially becomes relevant to cloud adoption—as your enterprise uses remote databases, keeping track of your data proves difficult.
Moreover, as your network scales, you need to take further steps to maintain the same level of visibility over all of your digital assets. Monitoring all of your cloud applications and databases can overwhelm your IT security team all on its own.
Without proper visibility, hackers can far more easily penetrate your digital perimeter, set up dwelling threats, or steal unguarded data. Thankfully, this is where log management steps in.
Put simply, log collection management refers to the collection and storage of log files from operating systems. In fact, it can also collect information from applications and multiple hosts and draw them into a single centralized location. It provides data normalization, search capabilities, and data storage and compliance.
Log management forms the heart of all other SIEM components and capabilities. It provides the groundwork for security event correlation and threat detection. Without log management which can adequately monitor your network, your cybersecurity may fall apart.
How to Learn More About SIEM Components
You can learn more about the most important SIEM components and critical capabilities in our 2019 Buyer’s Guide. We cover the top solution providers and their key capabilities. Also, we share our Bottom Line Analysis for each.
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