User and Entity Behavior Analytics (UEBA) is a cybersecurity approach that monitors and analyzes the activities of users and non-human entities across an organization’s IT environment. It uses data analysis, machine learning, and behavioral models to identify unusual activity that may indicate a security risk.
The main purpose of UEBA is to understand normal behavior and detect meaningful changes.
For example, an employee may usually log in from one location during standard business hours. The employee may also access a specific group of applications and files. If the same account suddenly logs in from an unfamiliar location and downloads a large volume of sensitive information, UEBA can identify this activity as unusual.
Importantly, UEBA does not monitor only human users. It also analyzes the behavior of entities such as:
By analyzing these activities together, UEBA helps security teams identify suspicious patterns that may remain hidden when each event is investigated separately.
Cyberattacks have become more difficult to detect. Attackers no longer depend only on malware or obvious network intrusions. Instead, they may use legitimate accounts, approved applications, and trusted devices to access business resources.
For example, a compromised employee account may allow an attacker to enter a corporate network without triggering a traditional malware alert. The attacker can then search for sensitive information, increase privileges, and move between internal systems.
Traditional security tools remain important. However, tools that depend mainly on known signatures or fixed rules may not identify every new or unusual attack pattern.
UEBA adds another layer of security by focusing on behavior.
It helps organizations:
Moreover, UEBA can help security teams connect events from different systems. This broader view makes it easier to investigate suspicious activity and understand whether several events are part of the same attack.
UEBA follows a structured process. It collects activity data, builds behavioral baselines, detects anomalies, evaluates risk, and generates alerts for investigation.
Although the exact process varies by solution, most UEBA systems use the following stages.
First, a UEBA solution collects security and activity data from different sources across the organization.
These sources may include:
This data provides information about how users and entities interact with business systems.
For example, authentication logs can show when a user logs in. Network records can show which systems communicate with each other. Endpoint data can reveal unusual processes or file activity.
When these sources are analyzed together, security teams gain a broader understanding of activity across the IT environment.
Next, UEBA builds a baseline of normal behavior for each user and entity.
A behavioral baseline represents the typical activities of an account, device, application, or system over time.
For a user, the baseline may include:
For a server, the baseline may include normal network connections, running services, application activity, and data exchange patterns.
Machine learning models can help identify these patterns. They can also adjust as normal business activity changes.
For example, an employee may start working with a new application after a role change. A well-tuned UEBA system should account for legitimate changes rather than treating every difference as a confirmed attack.
After creating behavioral baselines, UEBA compares current activity with expected patterns.
It looks for unusual actions, unexpected access, and changes that may indicate a security threat.
Common examples include:
However, an anomaly does not automatically mean an attack has occurred.
For this reason, UEBA should consider context, user roles, asset importance, and other security signals before assigning a risk level.
UEBA helps security teams understand which anomalies deserve attention first.
A solution may evaluate several factors, including:
Based on these factors, the system can assign a risk score or priority level.
For example, a user accessing a new application may represent a low-risk change. However, an account that accesses restricted data, increases its privileges, and transfers large amounts of information may represent a much greater risk.
This context helps analysts focus on potentially serious incidents instead of treating every unusual event equally.
When UEBA identifies suspicious behavior, it can generate an alert for the security team.
A useful alert should provide more than a short description of the event. It should help analysts understand:
With this information, analysts can investigate the event more efficiently and decide whether further action is necessary.
Finally, security teams can take action based on the investigation and the organization’s response policies.
Possible actions include:
When UEBA is integrated with SOAR or other response technologies, approved actions may be automated through predefined playbooks.
However, organizations should apply automation carefully. High-impact actions should follow clear policies and approval requirements.
UEBA supports several important security use cases. It is especially useful when threats involve valid credentials, unusual access, or suspicious behavior across multiple systems.
Insider threats can involve employees, contractors, administrators, or other people who already have authorized access to business resources.
Some insiders may misuse their permissions intentionally. Others may expose information accidentally through unsafe behavior.
UEBA helps identify unusual activity that may indicate insider risk.
For example, an employee who normally accesses a small number of project files may suddenly download thousands of confidential documents. The system can compare this activity with the user’s normal behavior and raise an alert when the change appears suspicious.
Other warning signs may include:
Nevertheless, unusual behavior should be investigated fairly. A behavioral alert is an indicator for review, not proof of malicious intent.
Stolen credentials are a common way for attackers to access business networks.
An attacker may obtain a password through phishing, malware, credential theft, or a data breach. Once inside, the attacker may use the legitimate account to avoid basic security controls.
UEBA can help detect this activity by identifying changes in account behavior.
For instance, a user may usually access business applications from one country. Suddenly, the account logs in from another location and starts accessing systems that the user has never used before.
Additional warning signs include:
When these signals appear together, security teams can investigate the account and take appropriate action.
Privileged accounts have access to important systems, sensitive data, and administrative functions. Therefore, misuse of these accounts can create serious security risks.
UEBA can monitor the behavior of administrators, database managers, developers, and other users with elevated permissions.
It can identify activities such as:
This monitoring helps organizations identify possible privilege misuse and investigate unusual administrative activity.
After entering a network, attackers may attempt to move from one system to another. This technique is known as lateral movement.
Attackers may use stolen credentials, remote access tools, or legitimate administrative services to reach additional systems.
UEBA can help identify unusual communication and access patterns between devices and servers.
For example, a workstation that normally communicates with a few internal systems may suddenly connect to several servers. If the activity also involves unusual authentication attempts or privilege changes, it may indicate a potential attack.
By connecting user and entity activity, UEBA gives security teams more context for investigating lateral movement.
Data exfiltration occurs when sensitive information is transferred outside an organization’s approved environment.
Attackers may use cloud storage, email, remote connections, or other channels to move stolen data.
UEBA can identify unusual data access and transfer patterns.
For example, an account may suddenly download a large number of customer records or transfer information to an unfamiliar external destination.
Other warning signs include:
When combined with data loss prevention and network monitoring, UEBA can provide additional context for investigating possible data theft.
Organizations increasingly use cloud applications, storage services, and infrastructure. As a result, user and entity activity extends beyond traditional office networks.
UEBA can analyze cloud sign-ins, application activity, resource access, and changes to cloud permissions.
It may help identify:
This visibility is useful for organizations that operate hybrid or multi-cloud environments.
Connected devices and operational systems can create additional security challenges.
IoT devices, industrial controllers, servers, and other operational assets often follow predictable communication patterns. A sudden change in those patterns may indicate a compromised device or unauthorized activity.
UEBA can help monitor these entities and identify unusual behavior.
For example, an industrial device may normally communicate with a limited set of systems. If it suddenly connects to an unfamiliar destination or begins sending unusual amounts of data, security teams can investigate.
However, organizations should deploy monitoring carefully in operational environments. Security controls must account for availability, safety, and operational requirements.
UEBA provides several benefits when implemented as part of a well-planned cybersecurity program.
Traditional detection methods often depend on known signatures, rules, or indicators. However, attackers can change their methods or use legitimate accounts to avoid detection.
UEBA adds behavioral analysis to the security process. It looks for unusual activity even when the activity does not match a known attack signature.
As a result, organizations can gain another way to identify suspicious behavior.
Security teams often work with data from many different tools. When these tools operate separately, it can be difficult to understand how events are connected.
UEBA helps bring user, device, application, and system activity into a broader behavioral view.
This visibility can support investigations across endpoints, networks, cloud services, and business applications.
High alert volumes can make it difficult for security analysts to identify important incidents.
UEBA can help prioritize alerts by considering behavioral context and risk. It can also correlate related events to provide a clearer picture of suspicious activity.
This approach can help analysts focus on events that require investigation.
However, results depend on data quality, model accuracy, integrations, and proper tuning. UEBA does not eliminate false positives entirely.
Investigating an incident often requires analysts to review login records, network events, endpoint activity, and application logs.
UEBA can connect relevant behavioral information and highlight unusual patterns.
This can reduce the time required to understand an event and identify the systems or accounts involved.
UEBA helps organizations monitor user activity and identify possible misuse of authorized access.
It can highlight unusual access to sensitive information, unexpected privilege changes, and suspicious data transfers.
This gives security teams an opportunity to investigate potential insider threats earlier.
Not every unusual event presents the same level of risk.
For example, an employee using a new device may be normal. In contrast, a privileged account accessing sensitive information from an unfamiliar location may require immediate attention.
UEBA helps evaluate activity in context and prioritize events based on their potential impact.
Many organizations must monitor access to sensitive information and maintain records of important security activities.
UEBA can support these efforts by helping organizations identify unusual access, review user activity, and investigate possible policy violations.
It may also provide useful information for internal audits and security reporting.
However, UEBA alone does not guarantee compliance. Organizations must also implement the required policies, controls, documentation, and audit processes for each applicable framework.
User Behavior Analytics (UBA) focuses on monitoring and analyzing human user activity. It looks for unusual actions that may indicate compromised accounts, insider threats, or policy violations.
User and Entity Behavior Analytics (UEBA) expands this approach. In addition to people, it monitors non-human entities such as servers, applications, endpoints, databases, and network devices.
| Feature | UBA | UEBA |
|---|---|---|
| Main focus | Human user activity | Users and non-human entities |
| Monitoring | Logins, access, and user actions | User, device, application, and system activity |
| Detection | Unusual user behavior | Unusual behavior across users and entities |
| Visibility | Primarily user-focused | Broader environment visibility |
| Common use cases | Insider threats and account misuse | Insider threats, compromised devices, and suspicious system activity |
In short, UBA focuses on people, while UEBA provides a wider view of both people and the systems they use.
SIEM and UEBA are related technologies, but they serve different purposes.
A SIEM platform collects and analyzes security logs and events from different sources. It helps organizations monitor activity, correlate events, investigate incidents, and generate security alerts.
UEBA focuses on behavioral patterns. It learns what normal activity looks like and identifies unusual behavior across users and entities.
The two technologies work well together.
| Feature | SIEM | UEBA |
|---|---|---|
| Primary purpose | Security event collection and analysis | Behavioral analysis and anomaly detection |
| Data focus | Logs, alerts, and security events | User and entity activity patterns |
| Detection approach | Rules, correlation, and analytics | Behavioral models and anomaly analysis |
| Main value | Centralized security visibility | Detection of unusual behavior |
| Typical use | Log monitoring and incident investigation | Insider threat and compromised account detection |
When integrated, SIEM can provide broad event visibility while UEBA adds behavioral context. This combination helps security teams investigate suspicious activities more effectively.
Extended Detection and Response (XDR) brings together security information from multiple layers, such as endpoints, networks, identities, and cloud environments.
UEBA adds another layer by analyzing how users and entities behave across those environments.
For example, an XDR platform may identify suspicious activity on an endpoint. UEBA can provide additional context by showing whether the associated user has also displayed unusual login behavior or accessed unexpected resources.
Together, these capabilities can help organizations:
Therefore, UEBA and XDR are complementary capabilities rather than direct replacements for each other.
Choosing the right UEBA solution requires more than checking whether a product supports machine learning. Organizations should evaluate how well the solution fits their security environment and operational needs.
Consider the following features.
The solution should support behavioral baselines and anomaly detection. It should also adapt to legitimate changes in user and entity activity.
A useful UEBA solution should collect information from identity systems, endpoints, networks, servers, cloud services, and applications.
Better data coverage can provide stronger behavioral context.
The solution should help analysts understand why an activity is suspicious and which events need attention first.
Real-time monitoring helps identify suspicious activity as it occurs. Historical analysis helps investigators understand earlier events and changes in behavior.
Analysts need understandable alerts, activity timelines, behavioral comparisons, and information about affected users and assets.
UEBA should work with the organization’s wider security tools. Integration with SIEM and XDR can improve event correlation and investigation.
Where appropriate, UEBA should support response workflows through SOAR or other security tools. Organizations should be able to define which actions require approval and which can run automatically.
The solution should support the organization’s current and expected data volumes. It should also provide appropriate access controls, data protection, and retention options.
UEBA can improve threat detection, but implementation requires planning. Organizations should understand common challenges before deployment.
UEBA depends on accurate and useful data. Missing logs, inconsistent timestamps, incomplete user records, and disconnected systems can reduce detection quality.
Organizations should review data sources before deployment and establish a process for maintaining data quality.
During the early stages, a UEBA system may identify many unusual activities. Some alerts may reflect normal business changes rather than actual threats.
Security teams should tune models, review alerts, and improve context over time.
Large organizations may use many applications, cloud platforms, devices, and identity systems.
Connecting these sources can require careful integration work. Therefore, organizations should define their priorities and begin with the most important data sources.
Behavioral analytics involves analyzing user activity. Organizations should establish clear policies that explain what data is collected, why it is collected, who can access it, and how long it is retained.
They should also follow applicable privacy, employment, and data protection requirements.
UEBA alerts require investigation and interpretation. Security analysts need to understand behavioral patterns, identity risks, and the organization’s business environment.
Training and clear investigation procedures can help teams use UEBA effectively.
A structured implementation approach can improve the value of UEBA.
Seceon provides AI/ML-powered cybersecurity capabilities through its Open Threat Management (OTM) Platform. Its platform brings together security functions such as SIEM, XDR, UEBA, threat intelligence, and SOAR to help organizations improve security visibility and response.
Seceon’s UEBA capabilities are part of its broader aiSIEM and aiXDR approach. The platform analyzes activity across networks, endpoints, servers, devices, applications, IoT, and OT environments.
Seceon uses machine learning to establish behavioral baselines for users and entities. These models can help identify unusual activity across connected systems.
For example, behavioral analysis can help highlight unusual access patterns, suspicious data movement, and unexpected system communication.
A single event may not provide enough information to confirm a security threat. Seceon’s approach combines behavioral information with other security signals, including network and endpoint activity.
This broader context can help security teams investigate suspicious activity and understand potential attack patterns.
Seceon’s UEBA capabilities support the identification of suspicious user behavior and potential credential misuse.
For instance, an account that begins accessing unfamiliar resources or behaving differently from its established pattern may require further investigation.
UEBA becomes more useful when its findings are connected with other security information.
Seceon’s aiSIEM and aiXDR capabilities bring together information from multiple security sources. This can help analysts connect user activity with endpoint, network, and system events.
Threat intelligence can add useful context to security investigations. When behavioral information is considered alongside threat indicators and other security events, analysts can make more informed decisions.
Seceon supports alerting and response capabilities within its broader security platform. Its solution brief describes manual and automated response options, including playbook-based workflows.
The exact response actions available depend on the connected tools, integrations, and deployment configuration.
Organizations can explore Seceon’s platform capabilities here:
UEBA will continue to develop as organizations adopt new technologies and face more complex cyber threats.
Several trends are likely to shape its future.
AI and machine learning can help analyze large volumes of security data and identify patterns that may be difficult to detect manually.
Future systems will continue to improve behavioral analysis, anomaly detection, and risk prioritization. However, organizations will still need to validate results and maintain appropriate human oversight.
Identity has become a major part of modern cybersecurity. As organizations use cloud services and remote access, protecting accounts and permissions is increasingly important.
UEBA can support identity security by monitoring login patterns, access behavior, and privilege usage.
UEBA will continue to work alongside XDR and SOAR technologies.
XDR can provide visibility across security layers, while SOAR can coordinate approved response actions. UEBA adds behavioral context to help identify suspicious activity.
Organizations will continue to use cloud services, IoT devices, and connected operational systems.
UEBA solutions will need to analyze behavior across these environments and account for the different activity patterns of each type of entity.
Security teams need to focus on the threats that could cause the greatest business impact.
Future UEBA capabilities will continue to emphasize context, asset importance, identity risk, and event correlation. This can help teams prioritize investigations more effectively.
User and Entity Behavior Analytics (UEBA) is a cybersecurity approach that analyzes the behavior of users and non-human entities. It uses behavioral baselines, machine learning, and anomaly detection to identify unusual activity that may indicate a security threat.
UEBA can help detect suspicious login activity, compromised accounts, insider threats, privilege misuse, unusual data transfers, lateral movement, and unexpected activity involving devices or applications.
UEBA collects activity data from connected systems, builds behavioral baselines, and compares current activity with expected patterns. It then evaluates unusual behavior, assigns risk, and generates alerts for security teams to investigate.
UBA focuses on human user activity. UEBA expands this approach by also analyzing non-human entities such as servers, endpoints, applications, and network devices.
SIEM collects and analyzes security logs and events from different sources. UEBA focuses on behavioral patterns and unusual activity. When integrated, they provide broader security visibility and stronger investigation context.
Yes. UEBA can help identify behavior that may indicate insider risk, such as unusual access to sensitive files, unexpected privilege changes, or large data downloads. However, alerts require investigation before an organization can determine whether misconduct has occurred.
Many UEBA solutions use machine learning and other analytical methods to build behavioral baselines and detect anomalies. Some platforms also use AI to support risk analysis, event correlation, and security investigations.
UEBA compares account activity with normal behavior. It can identify unusual login locations, unfamiliar devices, unexpected resource access, and suspicious activity after authentication.
Yes. UEBA can complement XDR by adding behavioral context to activity across endpoints, networks, identities, and cloud systems. It can also work with SOAR to support approved response workflows and automation.
Seceon provides UEBA capabilities as part of its AI/ML-powered aiSIEM and aiXDR approach. Its platform analyzes activity across users, endpoints, networks, servers, applications, IoT, and OT environments to support behavioral analysis, threat detection, and response.
UEBA can support organizations of different sizes. However, the right solution depends on the organization’s data sources, security requirements, IT environment, and available resources.
No. UEBA complements technologies such as SIEM, XDR, endpoint security, identity management, and data loss prevention. It adds behavioral analysis rather than replacing every existing security control.
User and Entity Behavior Analytics (UEBA) plays an important role in modern cybersecurity. It helps organizations understand normal activity across users, devices, applications, and systems. More importantly, it identifies unusual behavior that may indicate compromised accounts, insider threats, privilege misuse, or suspicious data movement.
By using machine learning, behavioral baselines, anomaly detection, and risk analysis, UEBA adds context to traditional security monitoring.
However, UEBA works best as part of a broader security strategy. When integrated with SIEM, XDR, threat intelligence, and SOAR, it can help security teams connect events, investigate threats, and respond more effectively.
Seceon’s Open Threat Management Platform combines UEBA capabilities with other security technologies to support unified threat detection and response. For organizations looking to improve visibility across users and entities, behavioral analytics can be an important step toward a more informed and proactive cybersecurity program.
