What is user and entity behavior analytics (UEBA)?

At its core, UEBA consists of two key components: user behavior analytics (UBA) and entity behavior analytics (EBA).

UBA helps organizations see and stop potential security risks by understanding user behavior. This is accomplished by monitoring and analyzing patterns across user activity to form a baseline model for typical behavior. The model determines the probability of a specific user performing a specific activity based on this behavioral learning pattern.

Like UBA, EBA can also help organizations identify potential cyberthreats—on the network side. EBA monitors and analyzes activity between non-human entities such as servers, applications, databases, and the Internet of Things (IoT). This helps identify suspicious behaviors that could indicate a breach, such as unauthorized data access or abnormal data transfer patterns.

Together, UBA and EBA form a solution that compares a variety of different artifacts, including geographical locations, devices, environments, time, frequency, and peer or organization-wide behavior.

UEBA collects user and entity data from all connected data sources across the organization's network. User data may include sign-in activity, location, and data access patterns, while entity data may include logs from network devices, servers, endpoints, applications, and other additional services.

UEBA analyzes the collected data and uses it to define baselines, or typical behavior profiles, for every user and entity. The baselines are then used to create dynamic behavioral models that continuously learn and adapt over time based on the incoming data.

Using baselines as a guide for typical behavior, UEBA continues to monitor user and entity activity in real time to help an organization determine if an asset has been compromised. The system detects anomalous activities that deviate from typical baseline behavior, such as the initiation of an abnormally high-volume data transfer, which triggers an alert. While anomalies on their own do not necessarily indicate malicious or even suspicious behavior, they can be used to improve detections, investigations, and threat hunting.

Alerts featuring insight into user behavior, the type of anomaly, and the potential risk level are sent to a security operations center (SOC) team. The SOC team receives the information and determines whether they should further the investigation based on behavior, context, and risk priority.

By using UEBA alongside a broader set of cyberthreat solutions, organizations form a unified security platform and enjoy a stronger security posture overall. UEBA also works with managed detection and response (MDR) tools and privileged access management (PAM) solutions for monitoring; security information and event management (SIEM); and incident response tools for action and response.

Threat hunters use threat intelligence to help determine whether their queries have uncovered suspicious behavior. When the behavior is suspicious, the anomalies point toward potential paths for further investigation. By analyzing patterns among both users and entities, UEBA can detect a much wider range of cyberattacks sooner, including early cyberthreats, insider cyberthreats, DDoS attacks, and brute-force attacks, before they escalate into a potential incident or breach.

UEBA models are driven by machine learning algorithms that continuously learn from evolving user and entity behavior patterns using data analysis. By adapting to security needs in real time, security solutions can stay effective in the face of a changing security landscape featuring sophisticated cyberthreats.

Security analysts use anomalies to help confirm a breach, assess its impact, and provide timely and actionable insights into potential security incidents, which SOC teams can use to further investigate cases. This, in turn, results in faster, more efficient incident resolution, which minimizes the overall impact of cyberthreats on an entire organization.

In the era of hybrid or remote work, today's organizations face cyberthreats that are always evolving—which is why their methods must evolve too. To detect new and existing cyberthreats more effectively, security analysts look for anomalies. While a single anomaly doesn’t necessarily indicate malicious behavior, the presence of multiple anomalies across the kill chain can indicate greater risk. Security analysts can enhance detections even further by adding alerts for identified unusual behavior. By adopting UEBA and expanding the scope of their security to encompass devices outside of the traditional office setting, organizations can proactively improve login security, mitigate cyberthreats, and ensure a more resilient and secure environment overall.

In regulated industries such as financial services and healthcare, data protection and privacy regulations come with standards that every company must comply with. UEBA’s continuous monitoring and reporting capabilities help organizations keep track of these regulatory compliance requirements.

While UEBA provides organizations with invaluable insights, it also comes with its own unique set of challenges to consider. Here are some common issues to address when implementing UEBA:

• False positives and negatives
  On occasion, UEBA systems can erroneously categorize normal behaviors as suspicious and generate a false positive. UEBA might also miss out on actual security cyberthreats, which can generate a false negative. For more accurate cyberthreat detection, organizations need to investigate alerts with care.
  
  
• Inconsistent naming across entities
  A resource provider may create an alert that insufficiently identifies an entity, such as a username without the domain name context. When this happens, the user entity can't be merged with other instances of the same account and is then identified as a separate entity. To minimize this risk, it’s crucial to identify entities using a standardized form, and to synchronize entities with their identity provider to create a single directory.
  
  
• Privacy concerns
  Fortifying security operations should not come at the expense of individual privacy rights. Continuous monitoring of user and entity behavior raises questions related to ethics and privacy, which is why it’s essential to use security tools—especially AI-enhanced security tools—responsibly.
  
  
• Rapidly evolving cyberthreats 
  While UEBA systems are designed to adapt to changing cyberthreat landscapes, they may still face challenges in keeping pace with rapidly evolving cyberthreats. As cyberattack techniques and patterns change, it’s crucial to continue to tune UEBA technology to address the organization’s needs.

With advancements in machine learning, AI integration, and data analytics slated to enhance its capabilities, the future of UEBA is looking bright. Here are some of the most compelling trends and developments that are likely to shape the evolution of UEBA:

• Advancements in AI for cybersecurity
  As AI and machine learning continue to grow more powerful and sophisticated, UEBA’s predictive capabilities are expected to develop even further. Machine learning algorithms, which continuously learn based on incoming data, are expected to better identify complex patterns and anomalies, improve the accuracy of cyberthreat detection, and reduce false positives.
  
  
• Integration with extended detection and response (XDR) and endpoint detection and response (EDR) 
  UEBA is expected to integrate even more closely with EDR and XDR solutions. EDR solutions extend the scope of security to provide cross-platform visibility across endpoints. XDR solutions broaden this scope even further by offering security across a wider range of products, including networks, servers, cloud-based applications, as well as endpoints. Incorporating UEBA into XDR and EDR, enhances the threat intelligence provided by these solutions, so that organizations can more effectively detect and respond to cyberthreats across a multicloud, multiplatform environment.
  
  
• Incident response automation 
  When combined with UEBA insights, automated incident responses will become faster, more sophisticated, and more efficient, reducing the impact of security incidents overall.
  
  
• More proactive security capabilities 
  UEBA will become further embedded in identity and endpoint detection, as well as protection solutions. In the face of increasingly sophisticated cyberattacks, UEBA will evolve alongside complementary security solutions to provide even more advanced threat detection, as well as rapid incident responses. Managed extended detection and response (MXDR), for instance, brings together the managed monitoring, hunting, and remediation services of managed detection and response (MDR) with the extended security protection of XDR to provide fast, effective, and proactive cyberthreat coverage beyond the endpoint. These new UEBA capabilities will give SOC teams the ability to proactively search for cyberthreats across a wider variety of IT environments, empowering organizations to stay ahead of emerging cyberthreats.

Network traffic analysis (NTA) is a cybersecurity approach that shares many similarities with UEBA in practice but differs in terms of focus, application, and scale. When forming a comprehensive cybersecurity solution, the two approaches work well together:

• Focuses on understanding and monitoring the behaviors of users and entities within a network through machine learning and AI.
• Gathers data from user and entity sources, which may include sign-in activity, access logs, and event data, as well as interactions between entities.
• Uses models or baselines to identify insider threats, compromised accounts, and unusual behaviors that could lead to a potential incident.

• Focuses on understanding and monitoring the flow of data within a network by examining data packets and identifying patterns that could indicate a potential threat.
• Gathers data from network traffic, which may include network logs, protocols, IP addresses, and traffic patterns.
• Uses traffic patterns to identify network-based threats such as DDoS attacks, malware, and data theft and exfiltration.
• Works nicely with other network security tools and technologies, as well as UEBA.

As cybersecurity threats continue to evolve at a rapid pace, UEBA solutions are becoming more crucial to an organization's defense strategy than ever before. The key to better protecting your enterprise from future cyberthreats is to stay educated, proactive, and aware.

If you’re interested in fortifying your organization’s cybersecurity stance with next-generation UEBA capabilities, you’ll want to explore the latest options. A unified security operations solution brings together the capabilities of SIEM and UEBA to help your organization see and stop sophisticated cyberthreats in real time, all from one platform. Move faster with unified security and visibility across your clouds, platforms, and endpoint services. Get a complete overview of your security posture by aggregating security data from your entire tech stack—and use AI to uncover potential cyberthreats.