What Is UEBA?
UEBA stands for user and entity behavior analytics. It’s a type of cybersecurity solution that uses machine learning algorithms to detect suspicious activity in user and machine behaviors.
First, user behavior analytics is collected from system logs to establish a baseline of user behavior patterns. Then, UEBA can further analyze user behavior to detect anomalies and irregularities across an organization.
There are many ways that organizations can benefit from UEBA, but in a rapidly evolving security environment, it can be confusing to make sense of where UEBA fits into an existing cybersecurity instance.
What Are the Benefits of UEBA?
UEBA systems analyze millions of user and machine actions in only a matter of seconds. That’s faster than a human security officer could ever achieve. AI algorithms used in UEBA increase the chances of detecting malicious threats and speed up security processes.
Prevents Data Exfiltration
UEBA systems can detect user activity anomalies that could indicate a data breach. This allows security officers to be notified as soon as suspicious activity is detected so that threats can be contained and managed before data can leave your network.
Detects Compromised Accounts
Hackers often penetrate enterprise systems via stolen credentials obtained through social engineering activities.
According to the 2022 Verizon Data Breach Investigations Report, this was the case for 60% of all breaches last year. UEBA can help eliminate stolen credential threats by detecting suspicious activity associated with specific users.
Other benefits of UEBA include reduced security costs and lower overall cybersecurity risks. Companies that run UEBA require fewer analysts to do the work that UEBA can do automatically. And it helps mitigate risks quickly so that costly cyber attacks can be avoided.
Administrators suffering from alert fatigue will also be pleased that UEBA has the ability to prioritize threats and route them to the appropriate security level.
What Are the Three Pillars of UEBA?
The three pillars of UEBA refer to the three main components of a UEBA system:
Analytics is the first pillar of UEBA and is how the system identifies threats. Analytics collects and organizes data so that UEBA can determine what behaviors are normal and which are not. The system can then create statistical models to apply across areas such as application usage, communications, download activity, and network connectivity.
Integration is also a critical component of the innovative security system. UEBA integrates with other security systems and protocols that an organization has to create an air-tight cybersecurity environment. Then, the systems can work together to compare data from various sources to improve their ability to detect user behavior anomalies.
The final pillar of UEBA is presentation. This is how the UEBA system communicates its findings and activates the appropriate response. The response will vary depending on the needs of the organization. For example, some systems will generate an alert for IT administrators to investigate. Others may be automated to take immediate action against potential threats. It all depends on how companies intend to approach anomalies detected by UEBA.
UEBA Use Cases
Traditional security products are less effective against bad actors for many reasons. For one, attackers are often on the cutting edge of technology and know how to circumvent common security controls and protocols such as secure web gateways, firewalls, and other prevention tools. But even encryption methods like VPNs are unable to protect organizations from attackers. Plus, less than 35% of desktop users activate a VPN on a daily basis.
How UEBA Can be Used to Mitigate and Prevent Cyber Attacks
Detect Insider Threats
UEBA can identify normal vs. irregular activity based on specific user keys. In addition to identifying threats from compromised credentials, UEBA can also identify behaviors that indicate instances of malicious insiders who purposefully intend to perform a cyber attack. And it can even identify negligent insider threats like not applying security patches, not logging out after a session, or forgetting to change a default password.
UEBA can also play a role in prioritizing cyber incidents. They help systems and analysts understand which threats are particularly suspicious or dangerous compared to non-threatening irregularities. UEBA systems also work well with DLP tools used to prevent data exfiltration, consolidating alerts and prioritizing them accordingly.
IoT Vulnerability Monitoring
UEBA systems integrated with IoT devices can track all connected devices on a network and establish a behavioral baseline for each device or group of devices. UEBA systems can detect abnormalities like connections to unusual addresses or running certain features at irregular times.
For example, healthcare is a highly regulated industry due to the sheer amount of personal data collected and stored. Unfortunately, this also makes medical and dental offices a target for cyber attacks, especially since the use of connected devices to check patients in and out and verify important patient information, such as insurance and financial details, is increasing.
UEBA can help monitor connected medical devices and software such as iPads, computers, sensors, and other connected machines used in the field to detect potential threats, prioritize incidents, and take action against potential attacks.
UBA and UEBA
UEBA is different from user behavior analytics. In fact, the “E” gives away their key difference: entities. UBA monitors human processes for irregularities, while UEBA monitors both human and non-human behaviors for anomalous activities. That includes unmanaged endpoints, applications, and networks.
SIEM vs. UEBA
SIEM often comes up in conversation when discussing UEBA because they both track and manage threats and cyber events. SIEM, or security information and event management, uses several tools and technologies to give IT administrators a complete view of the security posture across an organization, which is very similar to what UEBA does for cybersecurity.
The main difference in SIEM vs. UEBA is that SIEM is rules-based, while UEBA is based on algorithms and advanced risk scoring to detect irregular activities. While hackers can work around or evade SIEM rules, it is much more difficult to trick a machine learning AI.
Another key difference between the two cybersecurity systems is that SIEM is designed to detect threats already occurring, while UEBA can detect the early signs of a cyber attack before it is launched.
SIEM and UEBA should be considered complementary tools that are part of a larger security system. Networks that use both systems will be much safer since attacks can be detected at nearly every stage of the cycle.
UEBA Best Practices
Here are some best practices to make the most of a UEBA implementation:
1. Decide How You Will Use UEBA
Organizations must define the use cases that need to be addressed and what risks are most threatening to the company.
2. Choose Which Data Sources UEBA Will Support
Next, companies should ensure their solution has access to the appropriate logs, business data, HR information, and any other data sets required to inform the analytics.
3. Define Which Behaviors UEBA Will Monitor and Detect
Some examples of detectable user behaviors for security purposes include work habits, location history, application activities, type of data accessed, non-IT information, contextual factors, and biometrics.
4. Take the Time to Establish a Reliable Baseline
UEBA vendors can make recommendations for how long it takes to build a baseline, but organizations should also consider factors like the scale of the business, the time of year, and other factors that can skew your baseline results. It can also be helpful to rebuild the baseline periodically.
5. Update Company-Wide Security Policies and Training
Once UEBA is implemented, workers will need to be retrained to understand how UEBA protects them and the organization from cyber risks in conjunction with regular cybersecurity training.