User and Entity Behavior Analytics (UEBA) For Enterprise Security

Not all cyber threats come from outside your organization. Sometimes the biggest risk is already inside your network — a trusted employee going rogue, a compromised account, or a third-party contractor with too many access privileges.

As attack surfaces expand, those once-rare scenarios are becoming more common – and more expensive. According to the 2025 Ponemon Cost of Insider Risks Global Report, the average cost of insider threat incidents increased by more than 100% between 2018 and 2024.

High-profile incidents have shown how damaging this can be in practice, from employees leaking sensitive internal data to attackers using stolen credentials to access millions of customer records. Verizon’s 2025 Data Breach Investigations Report estimates that compromised credentials were involved in 22% of all data breaches.

Even with intrusion prevention systems and antivirus software in place, these threats often go undetected until real damage has already been done. To address this growing risk, organizations are increasingly turning to User and Entity Behavior Analytics (UEBA) to identify suspicious activity that traditional security tools miss.

The limitations of traditional security tools

Traditional security tools aren't designed to catch trusted users behaving maliciously. They're built to block known, external attackers, while allowing authorized users to go about their business.

However, this risk is more prevalent than many realize. According to Cybersecurity Insiders’ 2024 Insider Threat Report, 83% of organizations experienced at least one insider attack in the past year, up from 66% in 2019.

Most organizations still rely on signature-based detection. These systems maintain a database of known attack patterns and block any traffic that matches them. If a hacker uses an exploit known to the system, it’s caught.

The problem is that insider threats rarely match known signatures. A legitimate employee accessing files they’re authorized to see doesn’t trigger alarms. An admin account downloading unusually large volumes of data may also go unnoticed — even though that behavior could indicate data exfiltration.

In several widely reported breaches, attackers gained access using valid credentials and operated inside the network for extended periods without triggering traditional security controls.

In a breach disclosed in 2025, attackers used stolen credentials to access PowerSchool’s customer support systems, exposing personal data on more than 60 million students and teachers before the activity was detected.

To address this gap, some organizations turn to behavioral detection systems such as IPS (Intrusion Prevention Systems) and IDS (Intrusion Detection Systems). These tools establish a baseline of “normal” activity and flag deviations from it.

While more effective than signature-based tools, they often generate high volumes of false positives. A salesperson working late to close a deal might trigger alerts, or an engineer may need one-time access to production systems during an outage. Security teams end up spending valuable time investigating harmless anomalies instead of real threats.

User and Entity Behavior Analytics (UEBA) addresses these limitations more directly. It builds on the behavioral approach of IPS and IDS but applies advanced analytics to reduce false positives and add context. Rather than flagging every deviation, UEBA builds risk profiles and prioritizes the alerts that matter.

What is UEBA?

UEBA uses machine learning to establish behavioral baselines for users and systems across your network. When activity deviates from those patterns — for example, an employee suddenly accessing files they’ve never touched before — the system calculates a risk score and alerts security teams to investigate.

While UEBA is often associated with malicious insiders, it is just as effective at detecting compromised accounts. In many cases, unusual behavior is not intentional misuse, but the result of stolen credentials being used by someone else. Because UEBA focuses on behavior rather than intent, it can identify both scenarios when activity no longer matches established patterns.

Unlike User Behavior Analytics (UBA), which focuses only on people, UEBA also tracks entities such as devices, systems, and applications. This broader view allows security teams to spot abnormal behavior in infrastructure itself – like a compromised database acting out of character – not just suspicious user activity.

For example, if an employee typically accesses 20 files per day but suddenly downloads 2,000 files at 3 a.m., UEBA would flag the behavior as suspicious and alert administrators before the data leaves the network.

How UEBA works

UEBA detects and responds to insider threats through four phases:

1) Training phase: learning normal behavior

UEBA begins by collecting activity data from across your network — including firewall logs, VPN connections, file access records, email activity, database queries, and application usage. It analyzes this data to build behavioral profiles for every user and entity.

What files does an HR manager typically access? When does the marketing database usually see peak activity? Over time, these patterns form a baseline of what “normal” looks like.

2) Monitoring phase: detecting deviations

Once those baselines are established, UEBA continuously compares current activity against them. Unusual behavior — for example, an accountant accessing HR files or a server making outbound connections to unfamiliar IP addresses — is flagged for manual review.

3) Scoring phase: prioritizing risk

Anomalies on their own don’t tell the full story. UEBA assigns risk scores by weighing multiple factors, including how unusual the behavior is, the user’s access privileges, the sensitivity of the data involved, and historical activity.

A low-level employee downloading gigabytes of customer data at 2 a.m. receives a higher risk score than a system administrator performing routine maintenance.

4) Response phase: time for action

When risk scores cross a defined threshold, alerts are escalated to security teams with all the details — what happened, who was involved, why it’s suspicious, and which systems or data may be at risk.

Analysts can then investigate the most concerning threats immediately. This allows them to act quickly if a threat is genuine — disabling compromised accounts, blocking suspicious connections, or isolating affected systems before damage spreads.

Where UEBA fits in the security stack

UEBA is designed to complement existing security tools, not replace them. Its role is to add behavioral context, helping teams spot risky activity.

In practice, UEBA is often used alongside tools such as SIEM platforms and endpoint or network security controls. Those systems generate large volumes of alerts and logs. UEBA helps narrow them down by highlighting activity that looks genuinely unusual.

UEBA is especially effective at detecting activity linked to compromised accounts, where valid credentials are used by someone other than the account owner. In these cases, behavior often changes suddenly — different access patterns, unusual data movement, or activity at odd times — making it easier to spot as a cause of concern.

Like any behavioral approach, UEBA depends on having enough consistent activity data to learn from. It is less effective in environments with very short-lived users or limited visibility. UEBA identifies risk rather than taking action itself. Investigation and response still rely on existing security controls and human judgment.

Why should you use UEBA for enterprise security?

UEBA addresses several persistent challenges that traditional security tools struggle to solve.

Catch more threats

UEBA helps surface insider threats and compromised accounts that blend in with everyday activity — the kind of behavior traditional security tools are likely to miss because nothing seems overtly malicious.

Detect problems sooner

By continuously monitoring and ignoring lower-risk threats, UEBA reduces dwell time, giving security teams a better chance to contain incidents before damage spreads.

According to IBM’s 2025 Cost of a Data Breach Report, breaches involving stolen credentials take an average of 246 days to identify and contain, among the longest of any attack type, as attackers using valid credentials blend in with legitimate users.

Prioritize real threats

Security teams are often buried in alerts. UEBA’s contextual risk scoring highlights the activity most likely to represent real threats, so analysts spend less time chasing false alarms and more time investigating genuine incidents.

Scale security

As environments grow more complex, UEBA can continuously analyze activity across large volumes of users, systems, and applications — something that simply isn’t possible to do manually at scale. This makes it easier to expand coverage without growing security teams at the same rate.

UEBA Implementation Challenges

While UEBA offers significant advantages, organizations inevitably face challenges.

Complex configuration and ongoing tuning

UEBA systems require significant upfront configuration to establish accurate behavioral baselines — typically over a 30–90-day training period. During this time, organizations may see an excessively high volume of alerts as the system is fine-tuned towards what “normal” looks like.

Detection thresholds and risk scoring need tweaking regularly, though. Overly aggressive settings generate noise but being too lenient risks threats slipping through the cracks.

Human oversight is still required

UEBA is not a set-and-forget solution. While it automates detection and alert prioritization, security analysts must still review alerts, determine whether activity is genuinely malicious, and take action to contain incidents. This requires ongoing human judgment and incident response expertise.

Integration and data consistency

Organizations have limited control over how third-party platforms generate and report data. Different tools use different APIs, schemas, and formats, which can make it harder for UEBA to analyze activity consistently across the environment. Gaps in data coverage can create blind spots where threats go undetected.

Before deployment, organizations should verify that their UEBA solution supports all critical platforms and systems.

Employee privacy and trust

Behavioral monitoring can raise concerns among employees, who may see it as a lack of trust. Organizations need to clearly communicate why UEBA is being used and how it protects both the business and its employees, rather than monitoring individuals as a punishment.

Bolster your security with UEBA

You may trust your employees — but insider threats often emerge from unexpected places. A long-standing staff member misusing access, a compromised account, or someone who unknowingly falls for a phishing attack can all introduce serious risk. By the time unusual behavior becomes obvious, the damage is often already done.

UEBA is designed to catch those signals earlier by flagging potential threats that other tools miss. For organizations ready to take insider risk more seriously, UEBA provides a more practical, scalable way to detect and respond before small issues turn into major incidents.

FAQs about User and Entity Behavior Analytics (UEBA)