What Is UEBA in Cybersecurity?

What Is User and Entity Behavior Analytics (UEBA)?

User and Entity Behavior Analytics (UEBA) is a class of security software that analyzes behavior patterns of users and systems to detect anomalies that may indicate risky or malicious activity.

The “entity” component in UEBA distinguishes it from earlier User Behavior Analytics (UBA), which profiled end-user behavior. UEBA expands this scope to include entities such as routers, servers, IoT devices, and service accounts to establish baselines and identify deviations that indicate risk.

Traditional controls primarily detect known threats by matching events to rules, signatures, or indicators. UEBA learns baseline behavior for users and entities and highlights statistically unusual patterns, even when traffic appears legitimate, often signaling insider misuse or account takeover.

How UEBA Works

UEBA detects threats by ingesting telemetry, learning normal behavior, and flagging risky deviations, then prioritizing and adapting as environments change. Most modern solutions run as an analytics layer over SIEM/XDR and analyze logs/alerts from connected data sources (often agentless), with optional endpoint signals where available.

Here’s a step-by-step overview of a typical UEBA workflow.

Step 1: Telemetry ingestion from multiple sources

UEBA starts with broad, reliable telemetry. It ingests identity events, endpoint and server logs, network signals, and cloud/SaaS activity into a single pipeline. Every event is linked to the correct user, service account, device, and cloud identity. This enables analysts to quickly switch between the user, device, and application during investigations.

Common sources include:

• IdP/AD sign-ins
• EDR telemetry
• Windows/Linux authentication logs
• CloudTrail/Azure sign-in logs
• SaaS audit logs
• VPN/proxy connections
• DLP/file access events.

Step 2: Behavioral baseline creation

Machine learning (ML) algorithms then process the collected data to identify normal behavioral patterns for each entity. For example, a sales executive might access CRM systems during business hours from specific locations.

In another instance, a file server typically receives requests from defined IP ranges at predictable volumes. These patterns serve as the baseline for anomaly detection across the organization.

Step 3: Real-time anomaly detection

When behavior deviates significantly from established baselines, UEBA generates alerts. The system flags deviations such as finance employees suddenly downloading terabytes of customer data, dormant service accounts authenticating from unfamiliar countries, or applications making unexpected external connections.

Step 4: Risk scoring and prioritization

UEBA converts raw anomalies into prioritized action by maintaining a dynamic risk score for every user and entity. Each event raises or lowers that per-entity score based on who the user is (identity, role, privileges, or peer group) and what the asset is worth (data sensitivity or business priority). The platform then routes only the highest-risk entities to analyst queues.

Step 5: Continuous learning and adaptation

UEBA learns from new data to continuously refine its models. When your organization experiences different user or entity behaviors due to seasonal patterns, role changes, or legitimate workflow adjustments, UEBA will automatically update its baselines. This adaptive learning capability ensures accuracy while reducing noise from false positives.

Core Components of UEBA Solutions

UEBA solutions consist of four core components: large-scale data collection, identity and asset context enrichment, behavioral modeling with anomaly detection, and response orchestration.

Together, they reveal the highest-risk users and entities behind signals such as account compromise, lateral movement, data exfiltration, and insider threats.

Data collection and normalization

A UEBA platform needs a resilient data plane that can collect high-volume, high-variety telemetry in near real time. Connectors pull events from identity providers, endpoints, servers, network devices, cloud platforms, and SaaS applications into a single pipeline.

The collected data will then undergo data normalization, which converts diverse log formats into unified behavioral metrics. A Windows login event, Linux SSH session, and cloud application access represent authentication but log differently. UEBA standardizes these formats to support cross-platform correlation and analysis.

Identity and asset context

UEBA is only as strong as its understanding of “who did what, from where, against which asset.” This requires identity and entity resolution to form a single, trustworthy profile. Instead of treating every log line as an island, the platform builds a unified entity graph so analysts can see behavior in context and make faster, better decisions.

For example, an organization monitoring only login events would miss data exfiltration through legitimate sessions. At the same time, one with comprehensive monitoring coverage can access the full scope of user and entity activities.

Behavioral modeling services

UEBA learns normal activity and flags meaningful deviations using complementary methods:

• Supervised learning to recognize known attack behaviors quickly.
• Unsupervised learning (clustering or outlier detection) to surface novel threats.
• Peer group analysis to compare users with similar roles and privileges.
• Sequential patterning to detect risky chains.
• Adaptive baselines across time, location, access, volume, relationships, and authentication methods.

Since UEBA adapts its baselines to new data, it reduces the number of false positives that often occur with signature-based detection.

For example, when executives travel internationally, UEBA can recognize the pattern and adjust alerts. Additionally, when seasonal workers access systems during peak periods, UEBA automatically incorporates these variations into updated baselines.

Investigation and response orchestration

Integrations with IT and security systems enable standardized actions such as enrichment requests, step-up authentication, session termination, access revocation, host isolation, ticketing, and notifications. Playbook tooling enforces consistent handling while preserving analyst control, and audit trails capture who did what during triage and containment.

Key Benefits of UEBA

UEBA improves the capabilities of signature and rule-based tools by surfacing risky behavior that blends in as legitimate. Older security systems may fail to detect insider threats or compromised credentials because these attacks exploit legitimate access.

For CISOs, UEBA delivers risk-based detection and better threat visibility. For SOC managers, it reduces false positives and enables quicker, context-aware triage.

We explain more on UEBA’s key benefits below:

Reduce false positives

Risk-based scoring and peer comparison suppress low-value anomalies and elevate what truly matters, shrinking queues and focusing analyst time. A study published in the International Journal of Mechatronics, Robotics and Artificial Intelligence found that AI-powered anomaly detection reduced false positives by 40% compared to binary alert systems, highlighting the benefits of UEBA’s risk-aware detection.

Detect beyond signatures

UEBA identifies misuse hiding behind valid credentials (such as insider activity, shared accounts, and living-off-the-land) by modeling normal behavior rather than relying solely on signatures.

Faster investigation and response

Enriched alerts arrive with behavioral context, asset criticality, and historical patterns already attached. This way, your analysts skip manual log correlation and receive pre-contextualized intelligence showing what changed and which assets are affected, reducing mean time to investigate.

Strengthen compliance readiness

Continuous behavioral baselines and anomaly logs map directly to GDPR, PCI-DSS, HIPAA, and SOC 2 requirements. You have timestamped evidence of access patterns and policy violations ready at any time, demonstrating proactive monitoring and reducing remediation timelines.

Scale security visibility

Behavioral analysis adapts to distributed architectures by monitoring on-premise systems, cloud workloads, and SaaS applications from a unified framework. Anomalies are detected regardless of where users authenticate, ensuring consistent threat detection as infrastructure expands.

Common UEBA Use Cases

UEBA applies behavior and context-aware analytics to expose threats that hide behind valid credentials and routine activity. Typical use cases include insider misuse, compromised accounts, lateral movement or APT behavior, data exfiltration, third-party and contractor risk, and cloud abuse.

The examples below combine entity context, baseline deviation, and asset sensitivity to produce high-signal detections and clear investigative pivots.

Insider threat detection

The average cost of insider risks has increased from $15.4 million to $17.4 million annually, compared to $15.4M in 2022. UEBA serves as a key component for detecting and containing these insider incidents.

Compromised account detection

Credential abuse and social engineering remain leading attack vectors, with attackers increasingly leveraging AI to bypass traditional defenses. In 2025, 86% of organizations reported at least one AI-related incident involving AI-powered phishing or social engineering.

UEBA flags compromised accounts through deviations in login times, accessed resources, geographic locations, and activity patterns—adding a behavioral verification layer beyond password authentication.

Advanced Persistent Threat (APT) detection

APTs move laterally across networks over weeks or months using compromised accounts and legitimate tools. UEBA correlates small anomalies across time to reveal coordinated intrusions.

While a single unusual login may not trigger alerts, a pattern of escalating privileges, lateral movement, and gradual data staging exposes multi-phase attack campaigns.

While behavioral alerts can flag credential compromise, forensic analysis is required to uncover its full scope. Our guide to digital forensics tools looks at leading software and hardware solutions for conducting forensic examination and incident investigation.

How UEBA Detects Insider Threats

Insider threats are harder for legacy security tools to detect because insiders use legitimate credentials and authorized access. UEBA detects these threats by analyzing behavioral context. It recognizes that how users access systems matters as much as whether they’re permitted to do so.

Why behavioral analysis catches insiders

Your authentication logs may show authorized activity when a database administrator downloads customer records or your developer accesses financial systems. Signature-based tools see valid credentials and approved permissions in these activities.

However, UEBA flags the anomaly as these actions fall outside normal behavioral patterns for those roles, indicating potential misuse despite the user having legitimate access.

Detection patterns that reveal insider threats

UEBA identifies specific behavioral indicators:

• Data accumulation. Systematic collection of sensitive files outside job functions, such as HR managers gradually copying thousands of employee records
• Privilege escalation. Users are exploiting existing privileges to access systems beyond their role scope without a business justification
• Evasion tactics. Accessing sensitive data during off-hours or using unusual export methods that avoid DLP controls
• Pre-departure signals. Employees under performance review who suddenly increase data downloads or transfer intellectual property to personal accounts

Distinguishing intent from negligence

According to a Data Breach Investigations Report, 60% of data breaches involved a human element, which includes insider threats. UEBA differentiates malicious insiders from negligent users through pattern analysis.

For instance, malicious actors often exhibit systematic exploration, careful timing with their actions, and methodical data staging. Negligent users, on the other hand, exhibit random anomalies and correct them immediately when notified.

Our phishing investigation guide provides methodologies for tracing phishing attacks that compromise insider credentials, complementing UEBA’s behavioral detection with forensic analysis.

UEBA vs. SIEM

UEBA and Security Information and Event Management (SIEM) systems are complementary in cybersecurity operations. However, there are some differences which we’ll elaborate on in the table below.
 

Figure 1: Comparison of SIEM and UEBA capabilities

SIEM is excellent for log aggregation and rule-based correlation of known threats and compliance requirements. UEBA then adds behavioral intelligence to catch insider threats that don’t match signatures.

Today, many organizations integrate both to reduce insider risks. SIEM provides data, while UEBA detects anomalous behavior, with alerts fed back into SIEM for incident management.

UEBA vs. NTA

Network Traffic Analysis (NTA) and UEBA both analyze behavioral patterns, but focus on different security aspects. Refer to the table below to see how their differences help determine which technology addresses your detection gaps.
 

 
Figure 2: Comparison of NTA and UEBA capabilities

NTA examines network communications, including protocols, packets, and traffic flows, to identify malicious channels and data exfiltration. However, it is based purely on network behavior. On the other hand, UEBA focuses on identity-driven context, tracking who accessed what and whether that behavior fits their role and history.

Both NTA and UEBA feed XDR for multi-vector detection. NTA identifies suspicious communications, while UEBA determines which user or entity initiated them and assigns a risk score based on behavioral norms.

UEBA vs. UBA

UBA is UEBA’s predecessor. Gartner introduced the “E” in UEBA in 2015 to emphasize the need to profile non-human entities alongside users for comprehensive threat detection.
 

 
Figure 3: Comparison of UBA and UEBA capabilities

Cyberattacks today increasingly exploit non-human entities, such as compromised service accounts, infected IoT devices, and manipulated applications, that can execute attacks without human involvement.

UBA’s focus on users only limits its ability to detect these threats. UEBA has a broader scope that allows it to identify entity-based attacks with minimal additional overhead, as systems already log non-human activity. In short, deploying UEBA provides you with comprehensive visibility compared to legacy UBA solutions.

Best Practices for UEBA

A successful UEBA deployment requires clean data, threat context, continuous monitoring, and attack surface management to increase detection accuracy while minimizing operational disruption.

Here are some best practices for deploying UEBA:

Start with clean, complete signals

You’ll need to have detailed log collection from endpoints, identity systems, network devices, and cloud applications. Then, normalize data formats and enrich events with user, device, and asset context.

Add adversary context to eliminate false positives

You can enrich behavioral alerts with tailored threat intelligence. When UEBA detects anomalous access from an unfamiliar IP address, threat intelligence reveals whether that address is associated with known attack infrastructure. Threat intelligence provides customized hunting rules with indicators, attacker infrastructure, and TTPs to prioritize risky behaviors tied to active campaigns.

Monitor and respond continuously

UEBA generates a detection value only when paired with effective response capabilities. SOC teams should establish clear escalation paths, investigation playbooks, and containment procedures. You should also test workflows regularly through tabletop exercises. XDR provides around-the-clock triage and containment, correlating user and entity behaviors while feeding improvements back into models.

Minimize attack surface

Exposed services, shadow IT, and vulnerable systems can create legitimate traffic patterns that trigger UEBA alerts. Tools like Attack Surface Management help you find external vulnerabilities, while Digital Risk Protection supports takedowns of fake login pages and leaked credentials.

How Group-IB Delivers UEBA Outcomes

Group-IB operationalizes UEBA inside our Managed XDR platform, backed by industry-leading threat intelligence and continuous surface hardening. This combination ensures that your SOC experiences fewer false positives, faster triage, and clearer containment paths.

The platform ingests endpoint, identity, network, and cloud logs, filtering out noise so UEBA models learn from reliable data. It delivers accurate risk scoring so your queue highlights who is at risk, not just which rule was triggered.

Integrations with your security tools allow the platform to execute pre-approved actions and feed outcomes back into models for quieter, smarter alerting. Threat Intelligence adds indicators, adversary infrastructure data, and TTP context to each behavioral alert. Every decision is backed by our analysts, who understand your business risk.

Talk to our experts or request a demo to evaluate UEBA in your environment. We provide expert support to align with your workflows, helping you assess value based on genuine alerts.