Security Operations | Security Analytics/UEBA | ProVision
Security Operations | Security Analytics/UEBA | ProVision
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What is UEBA technology?

UEBA stands for User and Entity Behavior Analytics and was previously known as user behavior analytics (UBA).


UEBA uses large datasets to model typical and atypical behaviors of humans and machines within a network. By defining such baselines it can identify suspicious behavior, potential threats and attacks that traditional antivirus may not detect. This means UEBA can detect non-malware-based attacks, because it analyzes various behavioral patterns.


UEBA also uses these models to assess the threat level, creating a risk score that can help guide the appropriate response. Increasingly, UEBA uses machine learning to identify normal behavior and alert to risky deviations that suggest insider threats, lateral movement, compromised accounts and attacks.


Cyber attacks is on the tenacious rise and hackers continue to target the vulnerabilities in your system. Even a small loophole in the system can serve as an entry point for the attackers. Organisations of all sizes are spending more money than ever to protect their network and assets due to the increasing threat landscape. Hackers can break into firewalls, send you emails with malicious attachments, or gain access into your firewalls by compromising your system.

How can this technology help you?

UEBA monitors the behavior of users and entities of an organization. It processes this information and decides whether a particular activity or behavior could result in a cyberattack. It is able to know what is a threat or attack and what is normal use because while a hacker might be able to steal an employee’s password to log in, once inside, the hacker will not be able to mimic ‘normal’ behavior and UEBA can detect this anomalous behavior.


The analytics component detects anomalies using a variety of analytics approaches including statistical models, machine learning, rules and threat signatures. More than just tracking events and devices, UEBA uses machine learning to monitor possible threats from insiders.


Because UEBA can detect anomalous behaviors in real-time, it can issue an alert and request for a response to security analysts quickly, allowing them to react to potential threats before they become breaches. Normally security teams would have to sift through alerts to see which are real threats, but with UEBA this analysis is automated, only prioritizing genuine threats.



Detect insider threats

Detect anomalous behaviors in real-time

Detect compromised accounts

Detect brute-force attacks

Detect changes in permissions and creation of super users

Detect breach of protected data

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