Most security breaches don’t start with an apparent attack — they begin with stolen credentials or misuse of access by trusted insiders. These actions look normal on the surface, which is precisely why traditional tools fail to detect them.
Solutions like SIEMs and firewalls rely on static rules and known signatures. But what if a user logs in at 2 a.m., downloads large files, or accesses systems they’ve never touched before? These behaviors may appear legitimate, but they aren’t typical.
That’s where User and Entity Behavior Analytics (UEBA) comes in. UEBA doesn’t rely on fixed rules. Instead, it uses machine learning to understand what’s “normal” for each user or system, and flags anything that deviates from that baseline.
In this guide, you'll learn:
- What UEBA is and how it works
- How it helps detect hidden threats that bypass traditional defenses
- Where UEBA fits in your cybersecurity stack
- And how it compares to SIEM, IAM, and other tools
User and Entity Behavior Analytics (UEBA) is a cybersecurity technique that detects abnormal activity by analyzing how users and systems behave over time. Instead of relying on predefined rules, UEBA uses machine learning to build a baseline of “normal” behavior, then flags anything that deviates from it.
Modern cyberattacks often bypass traditional defenses by exploiting stolen credentials or insider access. These threats don’t appear malicious on the surface, making them harder to detect with tools like firewalls or SIEMs alone. That’s where UEBA adds critical value.
- Users: Individual behavior across systems.
- Entities: Non-human elements like devices or applications.
- Analytics: Machine learning and statistical models that detect anomalies.
UEBA provides clear visibility into security threats. This is particularly applicable when detecting compromised accounts and advanced persistent threats (APTs).
1. Data Aggregation Engine
The first step in any UEBA solution is gathering diverse telemetry. This includes logs from cloud platforms, endpoints, authentication systems, VPNs, firewalls, and user directories. The broader and cleaner the input, the more accurate the behavioral analysis.
2. Baseline Behavior Modeling
Once data is collected, UEBA builds a baseline of what “normal” looks like for each user and entity. This includes:
- Login times and geolocations
- File access frequency
- Device and application usage
- Session durations
Deviations from this behavioral profile are flagged as potential anomalies.
3. Anomaly Detection Algorithms
User and entity behavior analytics relies on machine learning and statistical models to detect unusual activity. Example: If a user accesses files during business hours but suddenly downloads gigabytes of data at 2 a.m., UEBA flags it as suspicious.
4. Risk Scoring System
Each detected anomaly is scored based on factors such as severity and context. There are risk scores that help security teams prioritize which alerts require urgent action and which can be monitored.
5. Threat Correlation and Contextualization
When it comes to user and entity behavior analytics, it does not analyze activities in isolation. It just correlates multiple signals, like failed login, into a narrative that helps teams understand the threat.
Control Access Before Behavior Becomes a Threat
UEBA tools flag risky activity after it happens. Securden reduces insider threats or credential misuse by letting you approve or block privileged access before any action is taken.
Stage 1. Collect and Aggregate Security Data
The process is initiated with data collection. The UEBA system collects logs and telemetry from sources including:
- Security Information and Event Management (SIEM) systems
- Identity and Access Management (IAM) platforms
- Firewalls, VPNs, and endpoint security solutions
- Application logs, file servers, and cloud platforms
Getting such unified data gives a 360-degree view of user and entity activities within the network.
For example, it tracks user logins, file access, device usage, privilege escalation, and network movement. If this diverse data is not collected, user and entity behavior analytics do not have behavioral history to work with.
Stage 2. Establish Normal Behavior Patterns
When enough data is collected, UEBA moves ahead with baseline modeling. The UEBA system uses machine learning technology to define “Normal” behavior for each user or system entity. The process involves:
- Typical login times and geolocations
- Frequency and timing of access to specific resources
- Devices used and network paths taken
- Duration of sessions and transaction types
For instance, if a user normally logs in from a company laptop at any time between 9 AM and 6 PM, that becomes part of the baseline, right? But in case of any deviation, like a 3 AM login from a personal device, it is something that becomes suspicious.
Stage 3. Identify Anomalies Through Machine Learning
At this stage, the system knows what normal behavior is, so it watches for anything unusual. And, as UEBA does not rely on fixed rules, it finds data access patterns that do not fit the baseline.
Let’s say a user logs in from two different countries within short periods. Or let’s say the user downloads files that have never been accessed before. These actions might not be malicious, but at some point, they are unusual. User behavior analytics marks these actions as anomalies.
The UEBA system tracks both users and entities, including devices and service accounts. Even subtle changes trigger alerts if they seem out of place. A change like a new access time or access from a different device signals anomalous behavior worth investigating.
Stage 4. Trigger Alerts for High-Risk Activities
User and entity behavior analytics do not treat every anomaly as a security threat. The system assigns a risk score based on context. This risk score helps separate normal irregularities from actual suspicious activity.
If a privileged user downloads sensitive files outside business hours, the UEBA system flags it. Alerts are ranked by severity, which helps analysts focus more on the most urgent ones. The goal is to reduce alert fatigue and prioritize real threats.
Stage 5. Support Security Analysts with Contextual Insights
User and entity behavior analytics do not stop at raising alerts. Each flagged activity includes context to help explain what is unusual. This context includes details like:
- Timeline of actions leading to the anomaly
- Associated users, IPs, and devices
- Related alerts from other systems (like SIEM or EDR)
These insights provide analysts with better visibility into activity and reduce investigation time through faster incident response. This reflects the entire UEBA process, from collecting data to detecting threats and supporting faster response.
1. Detects Cyber Threats Early Before They Escalate
Most breaches start small — a strange login, an unusual file transfer, or a subtle misuse of access. UEBA establishes behavioral baselines and detects deviations early, giving security teams time to respond before incidents turn into breaches.
2. Reduces Alert Fatigue for Security Teams
Security analysts waste time on false alarms and repetitive alerts. User behavior analytics reduces this burden by learning which behaviors are normal and flagging only true anomalies. This allows teams to focus their attention on real threats that demand faster response.
3. Uncovers Insider Threats with Precision
Insiders use valid credentials, so their actions rarely trigger traditional alarms. Here, user and entity behavior analytics strengthen insider threat detection by tracking user behavior patterns and identifying deviations like unusual file access or login times. This allows businesses to spot malicious activity from trusted users.
4. Automates Risk Scoring for Faster Decisions
Alerts are hard to prioritize and easy to miss when context is not involved. The UEBA system assigns risk scores based on how far the activity deviates from the baseline and what the involved assets are. This makes it easier for the security teams to identify security threats quickly and respond based on severity.
5. Minimizes Operational Costs Through Efficiency
Manual investigations consume time and resources. User and entity behavior analytics eliminates the investigation time by offering rich context for each incident, like timelines and correlated alerts. This improves efficiency and lowers the total cost of incident response.
6. Strengthen Compliance with Audit-Ready Logs
When you meet regulatory requirements, it means keeping clear records of access and actions. In this case, a UEBA solution constantly monitors user activity and stores it in structured logs. This approach supports fast reporting and strengthens the company’s compliance posture as well.
Whether you're scaling security or modernizing compliance, UEBA strengthens your defenses from the inside out — with real-time insights, context-rich alerts, and smarter risk prioritization.
Know Who, When, and Why Before UEBA Flags It
Securden logs every privileged session. When UEBA raises an alert, you already have the answers.
Here are the challenges and associated solutions for user and entity behavior analytics.
1. Lacks Clean and Consistent Data
User and entity behavioral analytics rely on security data from various sources. These sources are firewalls, endpoints, identity systems, and cloud tools. In most setups, this data is either delayed or incomplete. Logs come with missing fields or inconsistent tags, and duplicate records also create confusion during behavioral mapping. Such gaps breach the baseline logic and affect detection accuracy.
How can you address this challenge?
You can develop a reliable pipeline before you plan to integrate UEBA. Use preprocessing tools to clean and enrich raw data. You can also normalize the formats and add consistent metadata or, if possible, route everything through an SIEM or event collector. Clean data means more reliable alerts and fewer false triggers.
2. Generates Too Many False Positives
Still, many user and entity behavior analytics tools mark every deviation as a threat. Normal user behavior shifts often appear risky to the system. For example, a user accessing from a new location triggers alerts repeatedly. This creates alert fatigue, right? Here, analysts waste time investigating low-risk actions.
How can you address this challenge?
Use context-aware filtering to reduce the noise. You can include factors like time of access, device type, and risk level of the app accessed. Set up feedback loops where analysts train the model by flagging safe behaviors. Over time, this makes detection more precise and cuts down on unnecessary alerts.
3. Lack of Context Behind Alerts
User and entity behavior analytics raise alerts without context. The system might flag an "anomalous login," but it will not address why it matters. Analysts are left unsure about what has been changed or what action to take in such a situation. Lack of clarity delays the response and weakens trust in the tool.
How can you address this challenge?
Deploy tools that explain what triggered the alert. Use platforms that break down which normal behavior was violated and how risky it is. Visual maps or behavior timelines also help show the flow of events. Clear insight makes decisions faster and more confidently.
4. Compatibility Issues with Existing Security Systems
Legacy systems may lack behavior-level logging. Cloud services might not expose API data. Some tools simply don’t “talk to” UEBA platforms — creating blind spots in your behavioral monitoring.
How can you address this challenge?
Fix this by choosing UEBA platforms with flexible integration options. Look for connectors built for cloud apps, IAM platforms, and VPN gateways. If you deal with legacy systems or scattered privileged accounts, a solution like Securden helps you fill those gaps. It logs privileged activity in detail and integrates across cloud and on-prem environments, which gives UEBA the data it requires to work.
Below are real-world examples across industries where UEBA adds value:
1. Finance & Banking
Financial institutions often face attempts at credential theft and fraud.
- A junior analyst logs in at 2 a.m. from a personal device and initiates a large wire transfer request — behavior completely outside their usual access pattern.
- UEBA flags this as high-risk due to the unusual combination of time, device, and action, allowing the SOC team to intervene before funds are released.
2. Healthcare
Patient data access is heavily regulated and monitored under HIPAA.
- A nurse accesses dozens of unrelated patient records over the weekend, none of which match her shift or department.
- UEBA correlates this behavior and triggers an alert for potential data snooping or identity misuse, protecting sensitive medical records.
3. HR & Internal IT
Insider threats often come from trusted employees before they exit.
- An employee accesses resignation letters, downloads HR policy documents, and emails their payroll data three days before quitting.
- UEBA recognizes this as a sequence of “departure-related behavior” and notifies internal IT for preemptive risk mitigation.
4. Cloud-First Environments
UEBA bridges visibility gaps in multi-cloud and SaaS-heavy setups.
- A service account normally used for daily backups starts accessing dev repositories and performing privileged actions it never did before.
- The system detects the deviation from historical patterns and sends a high-risk alert tied to possible credential misuse or lateral movement.
5. Manufacturing & Operational Technology (OT)
UEBA can monitor critical infrastructure systems where changes may go unlogged.
- A PLC configuration is edited remotely by an admin account that’s been inactive for months.
- UEBA identifies this as anomalous machine behavior and helps OT teams investigate whether credentials were compromised or reused improperly.
These scenarios show how UEBA provides behavioral insight across users, service accounts, and systems, helping security teams respond before minor anomalies become major breaches.
Security teams rely on SIEM tools to collect logs and detect known attack patterns. These systems work well for compliance and external threat detection but miss insider threats or slow attacks that avoid rule-based alerts.
UEBA fills this gap. It learns user and entity behavior over time. This makes it more effective at flagging unusual or risky activities, even those that unfold over weeks or months. To get more information, check out the comparison in detail.
| Feature |
UEBA |
SIEM |
| Detection Approach |
Uses behavioral baselines to flag deviations from normal patterns |
Matches log data against static rules and known threat signatures |
| Scope of Detection |
- Insider threats
- Compromised users
- Gradual misuse
|
- External attack patterns
- Policy breaches
- Unauthorized access
|
| Data Input Types |
Ingests structured and unstructured data from various sources |
Collects structured logs from IT systems, apps, and devices |
| Alert Relevance |
Assign risk scores to help prioritize genuine threats |
Often generates high volumes of alerts, many of which are false positives |
| Use Case Focus |
Ideal for advanced threat detection and behavioral investigation |
Better suited for log auditing, compliance tracking, and known threat monitoring |
| Output Insights |
Delivers contextual insights like user timelines and intent |
Provides event-based summaries for forensic analysis and reporting |
Bottom Line: SIEM provides visibility and compliance monitoring for known threats. UEBA builds on that by identifying subtle internal risks that static rules miss.
UBA focuses on user behavior like login habits and privilege use. However, modern cyber threats include non-human elements like devices and servers.
UEBA expands the scope. It includes both users and entities. This allows security teams to monitor how systems behave and detect cross-layered threats. Check out the detailed comparison to get more clarity.
| Feature |
UEBA |
UBA |
| Monitoring Scope |
Covers both user behaviors and system entities like devices and apps |
Focuses only on human user behavior |
| Detection Capability |
Spot threats from both users and machine-based activities |
Limited to insider threats or credential misuse |
| Use Case Coverage |
Suitable for hybrid environments, IoT systems, and cloud workloads |
Suitable for internal user monitoring and policy enforcement |
| Data Sources |
Ingests data from users, endpoints, cloud services, and network logs |
Primarily collects data from identity systems and access logs |
| Threat Context |
Links user behavior with device or app activity for deeper context |
Provides user-focused threat timelines and anomalies |
Bottom Line: While UBA helps track human misuse, UEBA improves visibility by adding system-level context. This makes it more adaptive for modern infrastructure.
Network Traffic Analysis (NTA) monitors the data moving within a business network. It excels at spotting unusual traffic patterns and IP anomalies, but NTA does not link behavior to identities.
UEBA tracks actions by users and devices. Instead of focusing on traffic alone, it understands who or what initiated the action. Learn how both approaches are different.
| Feature |
UEBA |
NTA |
| Focus Area |
Behavior-based detection tied to identities and activities |
Traffic-based detection tied to network flows and endpoints |
| Identity Context |
Connects behaviors to specific users or entities |
Does not provide user attribution |
| Strengths |
Effective at detecting misuse and gradual changes in behavior |
Good at spotting command-and-control, DDoS, or data exfiltration |
| Blind Spots |
May not catch encrypted or low-level packet anomalies |
Cannot detect internal misuse tied to user activity |
| Deployment Fit |
Works best with identity systems and cloud services |
Suited for network-heavy environments and perimeter monitoring |
Bottom Line: UEBA and NTA approach threat detection from different angles. UEBA gives identity context to activity, while NTA focuses on how traffic behaves.
Identity and Access Management (IAM) ensures that the right individuals access the right resources. It sets access policies and logs access attempts. But IAM doesn't detect what happens after access is granted.
UEBA steps in post-access. It observes how users behave once they have logged in and flags suspicious activity. This is something that adds a behavioral layer to IAM's rule-based control. Let’s discuss each of the approaches in detail.
| Feature |
UEBA |
IAM |
| Role in Security |
Detects security threats based on behavior after access is granted |
Prevents unauthorized access through policies and controls |
| Detection Timing |
Works continuously post-login to monitor behavior |
Works before access by enforcing identity checks |
| Risk Identification |
Flag misuse, anomalous behavior, and insider threats |
Prevents unauthorized access but not misuse of authorized accounts |
| Decision Basis |
Based on behavior baselines and anomaly scoring |
Based on predefined roles, policies, and user identity |
| Response Capability |
Triggers alerts and helps with investigations |
Blocks, grants, or modifies access based on identity attributes |
Bottom Line: IAM controls who gets in. UEBA watches what happens afterward. While IAM reduces external risk, UEBA helps detect insider abuse or unusual behavior from legitimate users
Block High-Risk Access Without Slowing Down Teams
Behavioral analytics flags security threats too late if anyone can access anything. Use Securden to issue least-privilege access dynamically before the risk grows.
Here are the best practices for user and entity behavior analytics.
1. Prioritize High-Risk Scenarios First
Not all user activities carry the same risk. Users with elevated privileges, remote access, or admin rights. Focus UEBA on these high-risk areas first. Monitoring them ensures that the most damaging actions are caught early before they escalate.
2. Continuously Update Behavioral Baselines
User behavior changes over time. A role shift or a new tool can alter patterns. If UEBA sticks to old baselines, it may flag normal behavior as suspicious. Keep baselines dynamic by scheduling periodic recalibration to reduce false positives and keep detection precise.
3. Combine UEBA with Threat Intelligence Feeds
A user might act oddly, but without external context, it’s hard to judge the threat. Integrate UEBA solution with threat feeds to spot tactics linked to known attacks. This pairing improves threat visibility and decision-making speed.
4. Train SOC Teams to Interpret UEBA Insights
UEBA alerts need expert interpretation. Without proper training, even valid alerts can be ignored. You can train analysts to read behavioral patterns and map alerts to security threats. This improves response accuracy and avoids wasted effort.
5. Run Pilot Projects Before Full Deployment
Jumping into full deployment causes disruption. Instead, what you can do is to test UEBA in a small environment, like with finance or admin teams. Use this to observe alert frequency, response readiness, and integration gaps. It gives you time to fine-tune settings and plan a smoother rollout.
6. Review and Adjust Alert Thresholds Monthly
Too many alerts cause fatigue. Too few leave gaps. Alert thresholds must evolve with your environment. You can review patterns every month and adjust thresholds based on false positives and new user behaviors. This keeps the system balanced and effective.
User and Entity Behavior Analytics (UEBA) is no longer optional — it’s a crucial layer in detecting threats that evade traditional tools. From insider misuse to slow-moving credential abuse, UEBA helps security teams spot what static rules miss.
But detection alone isn’t enough.
To build a resilient defense, you need to combine behavioral visibility with access control — stopping threats before they escalate.
A unified privileged access management platform like Securden helps control who gets access to what. This eliminates the chances of insider risk and adds another context to behavioral analytics.
With Securden, you can:
- Control who gets privileged access, and when
- Detect unusual behavior across users and systems
- Correlate access logs with behavioral alerts for faster triage
- Stay compliant with audit-ready logs and session records
Together, UEBA and Securden give you a complete view of risk — from access decisions to activity monitoring.
What Customers Are Saying
“We’ve been using Securden to help users run programs with admin privileges — without making them full system admins. It’s a great way to minimize security risks while maintaining productivity.”
— Jacob V., Systems Engineer, Mid-Market Company
“Securden’s team delivered custom features for us in under a month — something no other password manager offered. Their response time and support are phenomenal.”
— Olivia Grimstead, NRTC Managed Services
Recognized by Industry Leaders
- 4.5/5 G2 Rating (Spring 2024)
- Best ROI & Highest User Adoption
- Used by Harvard Medical School, Trimble, Veeam, and more
Ready to see how Securden can combine access control with behavioral analytics?
Book a Personalized Demo
