Top 16 UEBA Use Cases for Today's SOCs in 2026
Traditional security measures, such as web gateways, firewalls, IPS tools, and VPNs, are no longer sufficient to defend against modern cyberattacks. Attackers routinely operate using valid credentials that rule-based tools never flag.
UEBA systems address this gap by monitoring non-user entities alongside human users, using machine learning to establish behavioral baselines and detect deviations. This gives SOC teams behavioral security insights that improve zero-trust initiatives and reduce the time between compromise and containment.
1. Detecting malicious insiders
Malicious insiders are internal or former employees who intentionally harm an organization by misusing their legitimate access to systems and data. They are among the hardest threats to detect precisely because their activity blends with normal operations.
UEBA identifies these individuals not by flagging specific events, but by correlating behavioral variances against self-baselines and peer-group baselines detecting patterns that no single log entry would reveal:
- Insider activities deviating from the user’s or their peer group’s historical behavior
- Suspicious or malicious sequences of activity
- Correlated alerts from external tools (DLP, CASB, EDR)
Insider threat costs reached $19.5 million per organization annually in 2026, up 12% from 2025, a 123% increase since 2018. Insider incidents now account for approximately 30% of all data breaches. Mean containment time improved to 67 days in 2026, down from 86 days in 2023, correlating with investment in AI-powered behavioral detection.1
Real-life example: A CASB API + UEBA solution detected an insider who authenticated using numerous IP addresses across geographically inconsistent locations. The solution generated “access attempts from certain IP blocks” and “risky countries” alerts based on behavioral deviation not on any explicit rule violation.2
Limitations: The Cyber Strategy Institute’s 2026 Insider Threat Report documents that high-impact incidents increasingly use “low-noise” techniques legitimate admin console commands, infostealer-based session cookie theft, and MFA helpdesk social engineering that don’t trigger behavioral alarms until data has already left the organization. UEBA remains essential for detection but should be combined with identity governance and just-in-time access controls to prevent exfiltration before it occurs.3
2. Detecting user account compromise
Account compromise, where valid credentials are stolen and used by an unauthorized party, is one of the most common attack patterns organizations face. This includes detecting shared account activity, credential stuffing, and overall account fraud.
UEBA detects that an account is being operated by someone other than its legitimate owner by modeling normal behavior and flagging deviations:
- Anomalous Active Directory activity
- Disabled accounts that become active
- Account recovery from unusual locations
- Activity from terminated users
Real-life example: A DLP & UEBA solution detected a user who downloaded over 2,000 files from a corporate OneDrive instance and uploaded over 400 files to a personal Google Drive. Detections included potential sensitive file movement, corporate data movement, a user-based spike in sensitive data uploaded to personal apps, and an unusual spike in download volume, all within a compressed time window. 4
3. Detecting device compromise
Detecting endpoints infected with malware is distinct from the compromised account use case: malicious behavior may originate from a host without any association with a specific user account. Malware can operate silently using system-level processes.
UEBA detects device compromise through behavior-based modeling regardless of how the initial infection was delivered, by tracking changes in:
- Communication patterns between devices
- Communication with external domains or IP addresses not in the device’s historical baseline
- Domain characteristics (newly registered domains, unusual TLDs, high-entropy domain names)
Real-life example: Law firm Winthrop & Weinstine deployed a UEBA solution to detect and respond to cyber attacks. By centralizing security data and visualizing IP communication patterns, the firm identified host and device compromises that had evaded perimeter defenses.5
4. Detecting lateral movement
Lateral movement involves an attacker who has already gained initial access using a trusted identity and systematically expands their reach across the network, escalating privileges, accessing new resources, and positioning for data exfiltration or ransomware deployment.
UEBA detects lateral movement by monitoring user and entity behavior trends and identifying deviations in:
- Privilege escalation patterns
- Access to sensitive resources outside the user’s normal scope
- Abnormal authentication sequences across systems
Specific lateral movement techniques UEBA can detect include:
- Pass the hash (PtH): credential theft where an attacker uses a captured authentication hash to impersonate a user
- Brute force logins: repeated failed authentication attempts across accounts
- Internal spearphishing: unusual email-based communications between internal accounts
- SSH hijacking: unauthorized use of active SSH sessions
5. Identifying network policy breaches
Organizations rely on policies to govern user account sharing, data movement, and device access, but enforcing these policies at scale is difficult. UEBA automates the detection of policy violations that would otherwise require manual review:
- Simultaneous logins from geographically distant locations: UEBA flags near-simultaneous authentications from locations that cannot be physically reconciled, indicating account sharing or credential compromise.
- Unusual data transfers: UEBA detects sudden, large data movements and transfers to unauthorized networks, violating data governance policies.
- Unauthorized device connections: Unknown or unregistered devices attempting to access the network are flagged as critical in BYOD environments.
- RBAC violations: UEBA analyzes access patterns per role and identifies when users access files or systems beyond their defined permissions.
6. Detecting data exfiltration
Data exfiltration is a risk even when accounts and endpoints appear uncompromised, because authorized users with legitimate access can still steal data. UEBA is essential here because standard DLP tools often miss exfiltration by trusted users operating within their normal permissions.
UEBA identifies data loss or theft across multiple vectors:
- Network infrastructure (firewalls and proxies)
- Cloud storage services (personal accounts, shadow IT)
- Removable storage (USB devices)
- Email (unusual attachment volumes, external recipients)
UEBA establishes what “normal” data transfer behavior looks like for each user and role, flagging anomalies in volume, destination, timing, and file-type patterns that a rule-based DLP would not catch if the user has access rights to the data.
7. Privileged access misuse prevention
Privileged accounts used by system administrators, DBAs, and executives have broad access to sensitive systems. Their compromise or misuse carries outsized consequences: data breaches, system disruption, or complete domain compromise.
UEBA continuously monitors privileged user behavior and flags:
- Access to sensitive data or systems outside the user’s normal operational scope
- Activity at unusual times (off-hours, weekends, holidays)
- Unusual command sequences or administrative actions that deviate from the user’s historical baseline
- Privilege escalation attempts that go beyond the account’s established usage patterns
8. Security alert automation and investigation
SOC teams face alert fatigue high volumes of alerts from anti-malware, DLP, and network access control tools that lack sufficient context to triage efficiently. Alerts missing the host, file hash, user identity, or prior activity chain require hours of manual investigation per incident.
UEBA solves this by enriching third-party alerts with behavioral context, enabling analysts to access a complete picture of who, what, and when by entering a single alert ID.
As of 2026, UEBA-generated risk scores are increasingly feeding into automated, agentic SOC workflows, with AI agents performing initial investigation steps, validating anomalies, and escalating only confirmed high-risk cases to human analysts. The WEF Global Cybersecurity Outlook 2026 reports 77% of organizations have adopted AI for cybersecurity, and UEBA risk scores are a primary input to these automated systems.6
Real-life example: Union Bank deployed a UEBA solution to aggregate all DLP events and establish behavioral baselines. The solution enabled the bank to filter out false positives and focus analysts’ time on genuinely high-risk situations, significantly reducing the investigation burden.7
9. Account lockout investigation
Account lockouts drain administrative resources in large organizations. Some companies dedicate a full-time position annually to research on account lockouts alone. Without UEBA, each locked account requires manual review to determine whether it is a user error, a cached credential conflict, or an active attack.
UEBA automates this investigation by checking:
- Domain controller event logs to identify the lockout source
- Cached credentials on the user’s device that may be triggering repeated authentication failures
- Active sessions that conflict with the lockout
This reduces investigation time from hours to minutes per incident and provides analysts with a behavioral history that distinguishes routine lockouts from potential account hijacking.
10. Account creation monitoring
Attackers who have gained an initial foothold often create new accounts as a persistence mechanism; even if the originally compromised machine is remediated, the new credentials keep them in the network.
UEBA monitors account creation activity and detects:
- Unauthorized credential creation outside normal provisioning workflows
- Fraudulent digital accounts using stolen or synthetic identities
- New accounts are immediately used for spamming, lateral movement, or policy violations
11. Third-party and supply chain risk monitoring
Third-party suppliers, contractors, and partners routinely access enterprise systems as part of normal operations. This access is necessary but creates an expanded attack surface that is difficult to monitor with standard perimeter tools.
UEBA monitors third-party activity and detects:
- Unauthorized access attempts beyond the partner’s defined scope
- Data exfiltration patterns from third-party accounts
- Behavioral anomalies that indicate a contractor account has been compromised
Real-life example: Lineas, Europe’s largest private rail freight company, deployed a UEBA solution to shift analyst focus from raw log review to behavioral supply chain analytics. The solution provided visibility into hosts, accounts, network traffic, and data repositories that had previously been blind spots.8
12. Insider risk monitoring
Insider risk encompasses both malicious and negligent behavior. UEBA captures and analyzes how users regularly interact with IT systems, establishing what “normal” looks like and reports deviations for further investigation.
Current 2026 data: 55% of insider incidents stem from negligence rather than malicious intent, and organizations experienced an average of 14.5 insider-related incidents per year.9 UEBA flags behavioral changes that may indicate rising risk, unusual working hours, new file access patterns, changes in communication behavior before those patterns escalate into incidents.
This gives SOC teams an overview of user activity analytics across the organization and supports proactive rather than purely reactive insider risk management.
13. Forecasting software and hardware breakdowns
UEBA’s behavioral baselining extends to infrastructure health, giving operations teams early warning of impending failures.
- Software: UEBA collects and analyzes application logs and response times. If it detects increasing error rates or transaction response times that historically precede crashes, it sends a software issue alert before the outage occurs.
- Hardware: UEBA monitors CPU utilization, memory consumption, and network traffic on servers, storage systems, and network equipment. Spikes or deviations from established operating profiles trigger hardware issue alerts, enabling proactive maintenance rather than reactive incident response.
14. Adhering to GDPR compliance
GDPR: The EU’s General Data Protection Regulation requires enterprises to account for who accesses personal data, how it is used, and when it is deleted. UEBA supports GDPR compliance by continuously monitoring user activity and access to personal data, maintaining audit trails, and detecting unauthorized access.
EU AI Act: The EU AI Act enters full enforcement on August 2, 2026, for most provisions. Organizations deploying high-risk AI systems must implement continuous monitoring, complete audit logging of AI system interactions, transparency obligations, and post-market monitoring plans. UEBA’s behavioral monitoring and audit trail capabilities directly support these requirements, particularly the obligation to log and monitor AI agent activity.10
15. Maintaining zero-trust security
Zero-trust architecture operates on the principle of “never trust, always verify,” requiring complete visibility into all users, devices, assets, and entities across the network at all times.
UEBA is a core enabler of zero-trust because it provides the behavioral intelligence that static access controls cannot: real-time insight into what users and entities are actually doing, not just what they are permitted to do. UEBA flags devices seeking access outside their established patterns, users attempting to exceed their rights, and behavioral changes that indicate a previously trusted identity may be compromised.
16. Monitoring AI Agent Behavior (New in 2026)
The most significant new UEBA use case of 2026 is the extension of behavioral analytics to AI agents, copilots, RPA bots, and other automated systems operating with enterprise credentials.
AI agents accessing data repositories, making API calls, executing workflows, and interacting with business systems behave in ways that closely parallel human users. A compromised or out-of-scope AI agent can exfiltrate data at machine speed, far faster than any human insider. Yet according to a 2026 insider risk report, only 19% of organizations currently treat AI agents with credentials as insiders, making this an actively undermonitored threat surface.11
Exabeam launched Agent Behavior Analytics (ABA) in January 2026 the first commercially available capability applying UEBA’s behavioral baselining principles directly to AI agent activity. When an agent accesses systems outside its functional scope, reads unusual volumes of sensitive data, or makes API calls inconsistent with its established pattern, ABA flags it and automatically generates a forensic timeline.12
Organizations implementing AI agents in 2026 should extend their UEBA scope to include:
- Behavioral baselining for agents: define which APIs an agent calls, which data it accesses, and at what volumes
- Deviation detection: flag when an agent accesses systems outside its expected function
- Forensic timelines: automatically reconstruct the sequence of actions when an agent anomaly is detected
- Credential governance: treat AI agent credentials with the same oversight applied to privileged human accounts
Open source UEBA tools
Tool | Functionality |
|---|---|
OpenUBA | Ingests and analyzes logs for abnormal behaviors using machine learning and behavioral profiling models |
Graylog | Collects logs from servers and applies machine learning-based anomaly detection through its interface |
Wazuh | Monitors telemetry data for threat detection and anomaly analysis |
Apache-Metron | Provides real-time insights into security telemetry through big data platforms |
HELK | Provides threat hunting capabilities using the ELK stack and Apache Spark for real-time data analysis |
Apache-Spot | Detects network traffic anomalies indicating suspicious user or entity activities |
Read more: Open source UEBA tools.
UEBA vs SIEM
- SIEM focuses on security event data rather than user or entity behavior. This means that SIEM collects and analyzes data from security logs, firewall logs, intrusion prevention logs, and network traffic, whereas UEBA uses user- and entity-related sources and various logs.
The core use case for SIEM is real-time security monitoring, event correlation, incident detection, and response.
- UEBA can detect insider threats, account compromises, privilege abuse, and other abnormal behavior or data transfer activities. UEBA uses machine learning algorithms and statistical modeling to establish “normal” behavior baselines, whereas SIEM employs rule-based correlation and pattern recognition.
UEBA can also be integrated into SIEM systems to improve user and entity behavior analytics, and SIEM solutions frequently offer UEBA capabilities as modules. Some vendors, such as ManageEngine Log360 or Microsoft Sentinel, offer unified SIEM products that provide SIEM and UEBA capabilities in a single solution.
FAQ
A UEBA system identifies and responds to cybersecurity threats by monitoring user and network activity. It aids in detecting anomalous behaviors, misconfigurations, and potential vulnerabilities, enabling security teams to take the necessary steps to secure their systems.
Gartner defines the three pillars of UEBA (user and entity behavior analytics) as follows:
1. Use cases: UEBA systems should monitor, detect, and alert to deviations in user and entity activity across multiple use cases.
2. Data sources: UEBA systems should be able to retrieve data from generic data repositories or via a SIEM without deploying agents directly in the IT environment.
3. Analytics: To discover anomalies, UEBA uses several analytical tools, such as statistical models and machine learning.
UEBA tools collect logs and alerts from all connected data sources and analyze them to create baseline behavioral profiles of your organization’s entities (e.g., users, hosts, IP addresses, and apps) over time and across peer groups.
These tools can leverage anomaly-based threat detection to provide comprehensive user and entity insights into unusual activity and help you determine whether an asset has been hacked. This helps SOCs to prioritize investigation and incident response. For more: Incident response tools.
Note that, unlike user behavior analytics (UBA), UEBA has an extended scope. While UBA focuses only on evaluating user activity, UEBA encompasses the behavior of both users and network entities, including:
-network devices
-routers
-databases
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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