User Entity and Behavioral Analytics (UEBA) is a type of security technology that detects threats based on user and asset activity. This approach is distinct from other security tools that look for malware signatures and indicators of compromise — with UEBA, it’s the asset’s behavior that counts.
The term was first introduced in 2015, as an evolution of User Behavior Analytics (UBA) commonly used in high-risk enterprise environments. These tools were limited in scope because they only analyzed the behavior of human users, not the assets and infrastructure they interact with.
UEBA adds “Entities” to the analytics model, allowing security teams to leverage artificial intelligence and machine learning to the entire network at once. Every endpoint device, server, and application is continuously monitored for behavioral evidence of malicious intent.
How Does UEBA work?
At the implementation phase, cybersecurity specialists create a baseline model of regular network behavior. This baseline indicates what every user, asset, and application does as part of its routine.
After implementation is complete, the UEBA platform triggers alerts when users, assets, or applications deviate from that established routine. The more unusual the behavior is, the higher-severity alert is generated.
Here’s a short list of actions that might trigger UEBA alerts:
- Downloading an unusually large file.
- Downloading a file from a new source.
- Logging into web applications from new devices or locations.
- Multiple login failures over a short period of time.
- Sending an unusual volume of requests to a server or application.
- Receiving an unusual volume of requests from an internal server or application.
- Attempting to access a network or asset that has never been accessed before.
None of these data points offer “smoking gun” evidence of wrongdoing. However, if you add enough of them up, they paint a compelling picture of insider risk that clearly merits investigation.
Is UEBA a Standalone Technology?
Standalone UEBA platforms do exist, and some organizations use them to great effect. However, UEBA technology fits well within a comprehensive multi-layered security tech stack, so many security leaders prefer to integrate UEBA into existing technologies and platforms.
UEBA integrations add significant value to two types of security technologies, in particular:
- Security Information and Event Management (SIEM) platforms use log data to drive security insight and run investigations across the organization. They combine so well with behavioral analytics data that many next-generation SIEM vendors now incorporate UEBA as a standard feature.
- Extended Detection and Response (XDR) deployments already leverage data from a broad source of third-party sources. Behavioral data provides valuable context for improving detection and response workflows, enabling security teams to respond to potential threats with much higher confidence.
What Kinds of Threats can UEBA Detect?
Behavioral analytics tools can detect a wide range of security threats, but they are particularly well-suited to managing insider risk. Some of the threats that UEBA-enhanced security tools address well include:
- Insider threats. Rogue employees and supply chain vendors can launch attacks from within the network, bypassing or disabling security tools in the process. However, they can’t hide their activity from UEBA monitoring.
- Credential-based attacks. Threat actors who gain access to privileged account credentials can operate safely inside the network. Well-configured UEBA technology can reliably detect account takeovers and other credential-based attacks.
- Data exfiltration. Threat actors cannot easily steal large volumes of data without triggering behavioral alerts. UEBA forces attackers to attempt stealing data at a much slower pace than they would otherwise.
- Brute-force attacks. Hackers who target cloud-based applications or third-party authentication tools are likely to trigger UEBA alerts. This provides the incident response team with early warning, leading to dramatically lowered risk.
- Distributed Denial-of-Service (DDoS) attacks. To conduct a DDoS attack, hackers must leverage a botnet towards targeting network assets with an overwhelming number of requests. UEBA technology can detect assets participating in those attacks, and the ones targeted by them.
- Advanced persistent threats. Technically proficient attackers can cover their tracks when conducting long-term reconnaissance, but they can’t keep their activities a secret from UEBA-based monitoring.
4 Ways UEBA Technology Helps Organizations Optimize Their Security Posture
Implementing behavioral analytics helps security teams improve detection and response performance significantly. Some of the most important benefits that UEBA implementation provides to organizations include:
- Deep visibility into security incidents. Providing analysts with highly contextualized data on a wide range of security events makes effective detection and response much easier for the whole team.
- Greater analyst efficiency and performance. UEBA technology provides automated insights directly to analysts without requiring manual investigation, allowing them to reach meaningful insights faster.
- Improved Zero Trust implementation. Verifying authenticated users for signs of malicious activity is an important part of Zero Trust. Obtaining unlimited visibility into every device, user, and asset helps organizations achieve identity-based monitoring and security.
- Optimized risk management. Security teams use UEBA technology to reduce risks associated with sophisticated cyberattacks, insider threats, and non-compliance. Behavioral analytics empowers insider risk teams to proactively catch malicious insiders before they launch attacks.
3 UEBA Implementation Challenges Security Leaders Face
Leveraging behavioral analytics to catch insider threats is a major security milestone, but it’s not always an easy one to reach. Security leaders often face steep challenges implementing UEBA technology, such as:
- Complex and costly implementation. Enterprise UEBA solutions come with high licensing fees. Implementing the technology requires specialist expertise, and may include additional costs, such as custom configuration and dedicated storage space for large volumes of log data.
- Lengthy deployment time. UEBA solutions require accurate behavioral baselines to produce meaningful results. Obtaining accurate baseline models and reducing false positives can take time and effort.
- Custom detection rule requirements. Making the most of UEBA technology requires customizing the solution to understand your organization’s unique security risk profile. High-performance implementations are configured to filter out false positives consistently and accurately.
Optimize Your Security Posture with UEBA-Enhanced Insights
Novawatch drives behavioral insight by leveraging sophisticated technologies like Rapid7 IDR in its state-of-the-art Security Operations Center (SOC). Discover how our UEBA-enhanced next-generation SIEM capabilities reduce risk, eliminate complexity, and lower costs for organizations that need comprehensive protection.
Consult a specialist and discover how Novawatch can help your organization achieve its Zero Trust goals today. Leverage behavioral analytics to catch malicious insiders before they have a chance to strike.
