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Five Main Differences between SIEM and UEBA
Corporate IT security professionals are bombarded every week with information about the capabilities and benefits of various products and services. One of the most commonly mentioned security products in recent years has been Security Information and Event Management (SIEM) tools.
And for good reason.
SIEM products provide significant value as a log collection and aggregation platform, which can identify and categorize incidents and events. Many also provide rules-based searches on data.
While often compared to user and entity behavior analytics (UEBA) products, SIEMs are a blend of security information management (SIM) and security event management (SEM). This makes SIEMs adept at providing aggregated security event logs analysts can query for known security threats.
In contrast, UEBA products utilize machine learning algorithms to analyze patterns of human and entity behavior in real time to uncover anomalies indicative of known and unknown threats.
Let's consider the five ways in which SIEM and UEBA technology differs.
Point-in-time vs. Real-time Analysis
SIEM provides point-in-time analyses of event data, and is generally limited by the number of events that can be processed in a particular time frame. They also do not correlate physical security events with logical security events.
UEBA, meanwhile, operates in real-time, using machine learning, behavior-based security analytics and artificial intelligence. It can detect threats based on contextual information, and enforce immediate remediation actions.
"While SIEM is a core security technology it has not been successful at providing actionable security intelligence in time to avert loss or damage," wrote Mike Small, a KuppingerCole analyst in a research note.
Manual vs. Automated Threat Hunting
SIEM does a very good job of providing IT pros with the data they need to manually hunt for threats, including details on what happened, when and where it happened. However, manual effort is needed to analyze the data, particularly to detect anomalies and threats.
UEBA performs real-time analysis using machine learning models and algorithms. These provide the machine speed needed to respond to security threats as they happen, while also offering predictive capabilities that anticipate what will or might happen in the future.
Logs vs. Multiple Data Types
SIEM ingests structured logs. Adding new data types often requires upgrading existing data stores and human intervention. In addition, SIEM does not correlate data on users and their activities, or make connections across applications, over time or user behavior patterns.
UEBA is built to process huge volumes of data from various sources, including structured and unstructured data sets. It can analyze data relationships over time, across applications and networks, and pore over millions of bits to find "meanings" that may help in detecting, predicting, and preventing threats.
Short vs. Long-Term Analysis
SIEM does a very good job of helping IT security staff compile valuable, short-term snapshots of events. It is less effective when it comes to storing, finding and analyzing data over time. For example, SIEM provides limited options for searching historical data.
UEBA is designed for real-time visibility into virtually any data type, both short-term and long-term. This generates insights that can be applied to various use cases such as risk-based access control, insider threat detection and entity-based threat detection associated with IoT, medical, and other devices.
Alerts vs. Risk Scores
SIEM, as the name implies, centralizes and manages security events from host systems, applications, and network and security devices such as firewalls, antivirus filters, etc. They deliver alerts based on events that may or may not be malicious threats. As a result, SIEMs generate a high proportion of false positive alerts which cannot all be investigated. This can lead to "actual" cyber threats going undetected.
UEBA provides risk scoring, which offers granular ranking of threats. By ranking risk for all users and entities in a network, UEBA enables enterprises to apply different controls to different users and entities, based on the level of threat they pose. One of the major advantages of risk scoring is it greatly eliminates the number of false positives.
Both SIEM and UEBA provide value for security operations teams. Each excels at specific use cases. When comparing these two technologies, it's helpful to consider how they diverge. Namely, SIEM is oriented on point-in-time analyses of known threats. UEBA, meanwhile, provides real-time analysis of activity that can detect unknown threats as they happen and even predict a security incident based on anomalous behavior by a user or entity.
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