Cybersecurity is a continuous race in which there is no rest. Adversaries and CISOs from companies and organizations are in a constant struggle to gain the upper hand, so there is never a minute to lose, as any unguarded flank can be instantly exploited. And, as this is a battle with no truces, it is critical that organizations adopt a proactive strategy against threats and leave aside reactive strategies and solutions which are obsolete today. By taking the initiative, organizations can save themselves the considerable time and resources that would be consumed in the aftermath of a cyberattack. This is where UEBA detection comes in: these proactive processes give the initiative to the organization, as they focus on the analysis of the behaviors of users and their devices.
The term, which stands for ‘User and Entity Behavior Analysis’, was coined by Gartner, and is based on a simple principle. Although corporate defense systems are prepared to detect and repel a cyberattack, what happens when the attack is not detected? Once a malicious program has infiltrated an organization, it can remain latent for months before it is finally detected and eliminated.
UEBA also emerged in response to a specific need. Traditional cybersecurity solutions based on logs and SIEM can generate a large number of incident alerts, but they do not manage to establish relationships with all events, nor do they ‘learn’ to prioritize threats on the basis of their patterns of behavior. This leaves cybersecurity professionals having to devote excessive manual resources to investigate and remedy them.
Algorithms for behavior analytics_
In such a scenario, two serious events could occur. Firstly, the malware could act without being detected in the organization for a long time and, secondly, the effort required to remediate the incidents generated would be considerable. The latter can also lead to alert fatigue among cybersecurity managers, impacting their performance when reacting to other threats. Mitigating and avoiding this fatigue plays a key role in the concept of managed detection and response (MDR) services.
This term refers to a group of technologies that help reduce the workload of CISOs, allowing them to focus on more proactive tasks rather than always acting reactively. And it is precisely in the MDR that UEBA detection comes into play, dealing with the mass of data that security systems generate and detecting the real threats among all this information. Unlike conventional cybersecurity technologies, focused on scanning devices, UEBA looks at the internal behavior of employees on their devices. That’s why it’s especially important in the face of threats posed by insiders.
The principles of UEBA detection did not originally stem from the field of cybersecurity, but from marketing, where it was used to analyze the behavior of consumers. Big data always offers a wider picture and, with it, it is easier to identify patterns. By moving this approach to the realm of cybersecurity, UEBA does just that, it analyses the behavior of an organization’s employees in relation to their devices and when it detects that something strays from the norm, the alarm bells ring.
This potential has led to UEBA detection systems being increasingly integrated into cybersecurity solutions, and Cytomic offers just that with its MDR services. An approach with an internal vision and using algorithms along with statistical analysis of human behavior ensures that many malicious items, which would otherwise go unnoticed, can be picked up. But, as an added benefit for cybersecurity, it also avoids the potential fatigue of CISOs and the consequent lack of control over threats.