Strengthen Network Security
with UEBA Cyber Security


In the world of cyber security, security teams are trending away from using prevention-only approaches, according to a 2018 Gartner report called Market Guide for User and Entity Behavior Analytics. As security teams shift toward balancing cyber threat prevention with the newer detection and incident response (IR) approaches, they are increasingly adding technologies like user and entity behavior analytics (UEBA) to their conventional SIEMs and other legacy prevention systems.

The evolution of Gartner UEBA & UEBA Meaning:

What is UEBA Security, UEBA baselines user behavior and entity behavior activities, and combines it with peer group analysis, and then searches for and analyzes anomalous activity in order to detect potential or actual intrusions and malicious activity. UEBA goes beyond fact-based security and simple correlation rules, and leverages both user behavior and entity behavior-based analytics, and models threats based on individual user behaviors.
Gartner created the UEBA acronym several years ago when it renamed “user behavior” analytics (UBA). They added the “E” to emphasize the importance of “entity behavior” other than just user behavior, such as with cloud applications or unmanaged endpoints. The E “recognizes the fact that other entities besides users are often profiled in order to more accurately pinpoint threats, in part by correlating the behavior of these other entities with user behavior,” according to Gartner.

Why UEBA Security improves UBA and legacy SIEMs :

User behavior and Entity Behavior Analytics represents an important improvement over UBA and legacy SIEM systems for a number of reasons. First, it overcomes the limitations of SIEM correlation rules—and the reality that in many cases the whole model of correlation rules is broken. Some of the problems associated with relying on SIEM correlation rules include:
  • You can’t find attacks because the rules lack context, or miss incidents that have never been seen before, thus generating false negatives.
  • Rules require too much maintenance.
  • Improperly filtered rules can make incident response execution slow because administrators need to filter the application of rules to determine which data is relevant, and which data is irrelevant in your event landscape.
UEBA also reduces false positives, helping to eliminate alert fatigue. And by enabling teams to prioritize their alerts, UEBA makes it possible for your security experts to focus on the most credible, high-risk alerts.

Why UEBA should be part of any organization’s security framework:

We all know that cyber attacks are becoming more complex and harder to find. So trying to write correlation rules for thousands of different possible scenarios is no longer practical. This is especially true in the case of insider threats. If you establish rules such as “Send an alert whenever a user sends an email attachment larger than 4MB,” you would need to consider every individual user, and then establish exceptions. For instance, graphic designers in the marketing department might routinely send large PDF files. Rather than asking your UEBA cyber security provider security experts to manually whitelist every case like this, UEBA replaces traditional Boolean alerts with probabilistic models or risk factors based on advanced analytics.

In this way, UEBA superior insider threat detection compared with conventional SIEM correlation rules. Plus, UEBA tracks anomalous user behavior and suspicious lateral movements not only within your organization/network, but it can also be associated with your cloud services, machines, mobile devices, and IoT assets. User behavior analytics can also provide dramatic time savings because teams don’t have to dig into logs in diverse locations to put together a story of the incident. A sophisticated UEBA cyber security provider system ingests data from all the different log sources—such as Windows AD, VPN, database, badge, file, proxy, and endpoints—and builds a contextual story around the incident for your security teams to analyze.

Key Features


Easily compile data from event logs deep packet inspection, and outside threat intelligence, attribute behaviours and create master database containing all communications within the network to establish baseline. Use this baseline to later pinpoint deviations indicating malicious intent.

ML Enabled

With Machine learning abilities to enable user and entity behaviors modelling and analytics based on various organizational components. With real time security events and ML, detect and prevent threats that traditional tools miss.

Reduce False

Building profiles for each entity, UEBA uses context surrounding behaviors to differentiate between attacks and false positives.

Key Benefits

Detection of Hijacked Accounts

Attackers who steal valid user credentials behave differently than real users. UEBA uses real-time detection to ascertain if something is out of norm and responds to the threat through various real-time responses such as Block, Modify, Re-authenticate or Multi-factor authentication. This ensures that the real threats are getting addressed before they try to harm the system.

Reduced Attack Surface

UEBA sends insights to the users and the security teams through interactive analytics which allows them to know about the loopholes or weak points before an incident happens. These insights help reduce the attack surface which makes it difficult for the cyber attacker to breach the network.

Privilege Abuse and Misuse

In any organization the privileged users have extensive access to the system, data and applications which is why they present a higher risk to the organization. UEBA’s algorithms ensure that the access rights are used appropriately and give an overview of what kind of privileges individual users should have.

Improved Operational Efficiency

It takes a lot of efforts to identify threats manually through alerts. UEBA can manually identify and validate threat without manual intervention through automation and security intelligence. This level of automation allows security to focus on real threats rather than alert chasing.

Data Exfiltration Detection

UEBA analytics help to detects potential data exfiltration before
it happens, thus allowing businesses time to prepare a strategic
plan to prevent data theft. It can even help identify Advanced
Persistent Threats (APT). UEBA has proved itself to be an
indispensable asset in the world of cyber security. According
to experts user and entity behavior analytics is a better model
for attack detection and maintain that it is going to enable more
accurate detection of cyber attackers threatening networks.

Data sheet

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