SOC Insider Threat Security Analytics
User and Entity Behavior Analytics (UEBA) is a security solution that monitors user activity and machine behavior within a corporate network. It uses statistical modeling and machine learning to create a baseline of normal behavior for users and entities across the corporate network. When any activity deviates from this baseline behavior, it is flagged as potentially malicious. This approach helps detect threats like identity-based attacks, malware, data theft, and insider attacks, enabling organizations to respond proactively to security incidents. According to Verified Market Research, the User And Entity Behavior Analytics Software Market size was valued at USD 1.27 Billion in 2024 and is projected to reach USD 19.40 Billion by 2031.
Traditional rule-based threat detection systems create too much noise to discern true risk, only find what you are looking for and often act as a reactionary tool dependent on incident response. UEBA systems provide the necessary context to predict malicious or damaging behavior before harm can be inflicted. Security analysts need to establish a proactive posture to Threat Detection, Investigation and Response (TDIR) and UEBA systems provide that where traditional tools are faltering.
Detecting identity-based attacks and insider threats in real time is a key benefit of UEBA security. When a bad actor uses legitimate credentials to access sensitive data, their behavior may go unnoticed without continuous monitoring allowing it to detect when a user is accessing systems or data that fall outside their normal pattern. It can predict and prevent data exfiltration from user accounts and identify suspicious IP addresses.
Data Collection and Integration: UEBA security provides a comprehensive view of user and entity activities by ingesting data from multiple data sources including, but not limited to: system logs, network traffic, application, identity and traditional security.
Establishing Behavioral Baselines: Statistical modeling and machine learning turn all relevant data into behavioral baselines, which establish a state of normalcy. Typically training on historical data and dynamically updating as peer-group baselines change.
Threat Detection and Risk Scoring: UEBA security comes loaded with machine learning models designed to identify deviations from the behavioral baselines. However, not all anomalies are threats, so the more sophisticated solutions leverage all correlated telemetry to contextualize the anomaly and assign a risk score for prioritized investigation and response.
Continuous Monitoring and Refinement: The systems are dynamic, constantly learning from changes in data and interaction with analysts. The best systems allow SecOps teams to easily configure ML detection models for high-fidelity detections and false positive reduction using data analytics to prevent data breaches.
Security Information and Event Management (SIEM) systems focus on collecting and correlating logs and events within a corporate network. While SIEM tools are great for identifying log-based events, many lack the ability to detect behavioral anomalies. This is where UEBA adds value, as it can detect unusual user activity that may not generate traditional security alerts. Read more about UEBA vs. SIEM in our blog post, “UEBA vs SIEM: The Key Differences of Each Solution.”
While traditional SIEM players have bolted-on UEBA systems, Next-Gen SIEM solutions were actually born out of UEBA. The advanced machine learning analytics are the backbone of these big data solutions, easing the reliance of static rules and improving detections. The ability to ingest vastly more disparate data combined with the power of behavioral analytics delivers greater visibility and significantly more context that reduces MTTD and MTTR.
The reality many organizations face is a SIEM replacement is not in the short-term cards for them. Augmenting your SIEM with UEBA is a viable first-step which can result in significant data ingestion cost savings and improving threat detection, investigation and response capabilities. Whatsmore, partnering with a Next-Gen SIEM player who can deliver stand alone UEBA accelerates your transition to a modern SIEM platform while minimizing disruption.
Network Traffic Analysis (NTA) and User Entity Behavior Analytics both play critical roles in cybersecurity, but they focus on different aspects of threat detection. NTA monitors network traffic patterns to detect anomalies, such as unusual data transfers or suspicious connections, often identifying threats like malware or unauthorized access. In contrast, UEBA focuses on analyzing the behaviors of users and entities within the network, using machine learning to detect deviations from normal activities that may indicate insider threats or compromised accounts. While NTA is great for spotting network-based threats, UEBA excels in identifying behavior-based risks, making them complementary in a comprehensive security strategy.
User Entity Behavior Analytics extends the concept of User Behavior Analytics (UBA) by not only monitoring user behavior but also including entities like devices, applications, and systems in its analysis. While UBA focuses solely on detecting anomalies in user activities to identify potential threats, UEBA provides a broader scope by analyzing both users and non-human entities, offering more comprehensive threat detection. This expansion makes UEBA systems more effective in identifying a wider range of risks, such as those involving compromised devices or insider threats, compared to UBA’s user-centric approach.
Gurucul is a unified security analytics platform that ingests any data, in any format, from any location complete with federated search. This reduces false positives and identifies true threats by leveraging our extensive library of machine learning (ML) models, which can be chained together for high-fidelity detections, confirmation, and cross-validation, ensuring accuracy and compliance.
The Gurucul security platform helps analysts handle the full threat cycle, providing the insight and context needed to stop data theft and protect assets. By combining data from various sources and using advanced behavioral analytics with identity, network, security, endpoint and business application data, Gurucul breaks down traditional obstacles. This empowers teams to work more effectively, giving them the tools to prevent threats and safeguard sensitive information.
It adjusts to changes in baseline behavior by continuously monitoring and updating its model as new behavioral data is collected.
It handles insider threats by identifying unusual actions and access patterns that deviate from typical user behavior.
Machine learning in UEBA detects anomalies, refines baselines, and adapts to evolving behavioral patterns for accurate threat detection.
