UEBA software monitors corporate networks for suspicious user actions but also keeps a close eye on routers, servers and endpoints. UEBA solutions introduce an additional layer of security within a distributed IT ecosystem by detecting complex attacks or improper activity across multiple users, devices and IP addresses that make the core elements of remote workforce.

Many UEBA solutions take advantage of machine learning and deep learning to detect actions pointing at unusual and possibly harmful activity across an organization’s networked systems.

How the Three Pillars of UEBA Work

UEBA software can protect both in-house shared systems and systems that connect to outside networks. Detecting anomalies across the various uses of shared resources is getting increasingly important with a growing number of employees working remotely.

Corporate firewalls and point-to-point VPNs (Virtual Private Networks) provide the means to defend against unauthorized connections and secure communications between on-premises company servers and remote locations.

UEBA software provides the analytical capabilities to look beyond a typical cyberattack, detecting threats in which valid credentials and legitimate connections are exploited to penetrate your perimeter.

A UEBA system, as defined by Gartner, spots potential threats and assigns a risk score to any cyber-risk by taking advantage of three fundamental capabilities:

• Data Analytics: Collects data that represents “normal” user and entity behavior and then applies statistical models to detect unusual actions
• Data Integration: Pertains to a UEBA system’s ability to collect and compare data from multiple sources that include system logs, data packets and any other dataset
• Data Presentation: Refers to UEBA systems’ functionality to alert system administrators and security analysts about unusual behavior

These three functional components of a UEBA system transform into the three pillars of UEBA:

1. Use Cases - UEBA solutions cover multiple use cases as they monitor the behavior of entities and users in a complex network and compare it to established models. This makes UEBA systems different when compared to antivirus suites that use heuristic methods to detect threats.
2. Data sources - UEBA software does not reside locally on an endpoint. UEBA systems work with a general data repository such as a data warehouse, data lake or Security Information and Event Management (SIEM) system.
3. Analytics - UEBA software applies data analytics methods such as statistical models, machine learning, artificial intelligence, rules and threat signatures to detect abnormal behavior across a network.

These analytical capabilities of a UEBA system enable the security software to identify possible threats by correlating the behavior of multiple entities with a specific user behavior.

For at-home employees, this means organizations must contend with unregulated equipment and access from sources like home routers, mobile endpoints, changing IP addresses, personal devices, and more.

By implementing a UEBA system, organizations protect their data inside out by setting a pool of acceptable user behavior patterns and then comparing remote users’ behavior to patterns that are deemed secure and normal.

How UEBA Benefits Remote Work Setups

Remote workforce environments create internal and external security threats alike. A remote workforce use-case scenario complicates an organization’s cybersecurity strategy by introducing a variety of non-standard equipment, or entities, for the security analysts to monitor and control.

Specific benefits of adopting a UEBA system to monitor a corporate network comprising multiple remote locations and endpoints include:

• Detect insider threats. UEBA software is good for detecting insider threats, as it tracks unusual behavior while being able to spot improper use of valid login credentials or user access privileges. An employee accessing sensitive data that he or she needs only occasionally when performing his duties and who starts accessing such data on a daily basis represents a valid risk, which UEBA’s algorithms can spot. The same is true for attempts to abuse user privileges or connect through unknown communication tools/channels.
• Spot compromised accounts. Remote working scenarios increase risks for compromised user accounts. An employee could accidentally install malware on a remote machine, leave a connected machine unattended, or by sharing user credentials. UEBA systems can detect compromised accounts early and alert an organization’s security team of abnormal account usage.
• Detect abnormal permissions. For an attack on corporate systems to be successful, an attacker usually needs to obtain elevated user access privileges. UEBA software can detect unusual permissions and mark suspicious user privileges changes as risky.
• Identify data breaches and targeted attacks. UEBA systems can detect and warn when brute-force attacks target corporate systems or networks. They can also generate alerts when a user has no valid reasons to access sensitive business data.

Organizations planning to adopt a UEBA system should be aware that UEBA systems are generating more complex reports if compared to a traditional UBA system. The proper use of UEBA software requires experts who are able to configure different behavior tracking algorithms, tune software to reduce false positives, and understand an organization’s behaviors across departments. These configurations should follow several best practices if a UEBA implementation is to produce actionable results.

UEBA Best Practices for Remote Workforce

Organizations should implement UEBA software within a wide cybersecurity framework with tools such as next generation firewalls, point-to-point VPNs and a SIEM strategy. Securing a remote workforce environment requires proactive measures and adoption of a working combination of UEBA implementations and traditional monitoring software.

Major best practices will work in most use-case scenarios where UEBA implementation is involved but certain customizations should be adopted depending on an organization’s specific industry and remote work specifics.

• Proactively look for insider threats. Remote workers pose a variety of security risks, so organizations need to protect all endpoints and plan for use cases where remote employees are using public or unsecured networks for work.
• Establish feasible models for ‘normal’ activity. Establish reliable baselines for what is ‘normal’ working activity of remote employees and pay attention to any abnormal user behavior. If an organization employs AI and machine learning within a UEBA platform, it would take less than a month for the software to establish a feasible baseline.
• Restrict access across business systems. Make sure each remote employee has access only to digital assets and resources he or she needs to perform their immediate job and set up alerts for escalating privileges across all business systems.
• Use additional monitoring tools and have a response plan. Combine the use of UEBA software with other monitoring tools and draft a detailed response plan on how to address any deviations from a remote employee’s baseline behavior.

Whatever specific measures an organization adopts within its UEBA implementation, security teams should always bear in mind that a UEBA software is never a replacement for an Intrusion Detection System (IDS) and managed security team.

Conclusion

Organizations should look at UEBA systems as part of a broader IT security framework in which traditional IDS, SIEM and intrusion prevention systems play significant role. UEBA systems can perform threat detection and complement intrusion protection systems. Companies must also recognize that they are analytical tools whose primary function is to generate alerts about unusual user and entity behavior.

A UEBA software cannot stop an intrusion, but it can flag activities indicating a disturbance and thus mitigate the risks of a bad actor penetrating business-critical systems or leaking sensitive data.

By employing machine learning and AI capabilities, UEBA systems can play a major role in early detection of threats across IT infrastructures that comprise multiple and varied remote endpoints.