Segura User Behavior Analytics

What is Segura User Behavior Analytics

Segura User Behavior Analytics (UEBA) is the central analytical engine of Segura® Platform for detecting and responding to risks based on the behavior of users, applications, and services that access privileged resources. UEBA continuously monitors, learns, and correlates the activity patterns of users throughout the privileged access journey, from consulted credentials, open sessions (RDP, SSH, Web, APIs), executed commands, to browsing patterns and secret queries.

Main objectives

• Adaptive and dynamic security: identify risks in real time from behavioral deviations, automate responses, and reinforce the principle of Zero Trust.
• Continuous compliance: generate detailed audit trails, detect violations of internal policies or regulations (SOX, LGPD, GDPR, among others).
• Reduction of internal risks: quickly detect internal threats, improper use of credentials, and signs of compromised accounts, imposters, or bots.
• Intelligent automation: enable automated orchestration of responses (MFA, blocks, revalidations, revocations, SIEM/SOAR alerts) without manual intervention.
• Operational optimization: facilitate efficient management of privileged access, minimize false positives, and reduce the overload for security teams.

Main differences of Segura® Behavior Engine

• Dynamic behavioral profile (baseline): uses machine learning to build unique profiles per user, resource, and credential, considering time, origin, sequence of actions, typing patterns (keystroke dynamics), commands, and navigation flows.
• Multivector and adaptive detection: correlates behavioral and technical signals (device, network, location, context, SIEM/SOAR integration) to identify subtle deviations or sophisticated attacks.
• Automatic and orchestrated responses: triggers automatic responses based on the detected risk, from adaptive MFA and session blocking to integration with external containment systems (SOAR, SIEM, ITSM).
• Real-time and post-event analysis: monitors privileged sessions in real time (RDP, SSH, Web, database, APIs) and also performs retrospective analyses of recordings and logs.
• Native integration with Segura® modules: Behavior Analytics operates transversally, integrating data and decisions with the PAM, Discovery, Executions, A2A, Secrets, and Segura Intelligence IA modules, strengthening the entire adaptive security strategy.
• Enrichment with Threat Intelligence: capable of consuming external intelligence signals (SIEM, SOAR, threat feeds), increasing the accuracy of triggers and allowing contextual and proactive responses.
• Orchestration of adaptive policies: supports execution of dynamic policies (Just-in-Time, Zero Standing Privilege, step-up auth) based on risk and real-time context.
• High auditability and transparency: all events, decisions, and responses are logged and exportable for audit trails, compliance reports, and forensic investigations.

Connection with other Segura® modules

The Behavior Engine acts as an analytical and decision-making layer throughout the platform, interacting natively with:

• PAM Core: enhances the detection of anomalous use of credentials, sessions, and commands.
• Discovery: correlates newly discovered assets and emerging access patterns.
• Executions: reinforces controls and triggers in sensitive automations (e.g., rotation of risky credentials).
• A2A and Secrets Management: monitors the use of secrets by applications, APIs, and automations, blocking or revoking non-standard access.
• Segura Intelligence (AI): takes advantage of generative AI to summarize sessions, suggest responses, and enrich adaptive detection.

Central value

With User Behavior Analytics, the Segura® Platform transforms raw privileged access data into actionable intelligence, allowing organizations to migrate from a reactive to a proactive and adaptive posture in facing threats, internal risks, and regulatory demands.

Architecture and operation

Data flow and event capture

The Segura® User Behavior engine is designed to continuously capture, analyze, and correlate privileged access events across multiple vectors of the infrastructure. The data flow consists of:

• Monitored sessions: Real-time activity capture in RDP, SSH, Web (HTML5), database, API, local application, and remote endpoint sessions.
• Integration with logs and auditing: Consumes Segura®'s internal records (audit logs, session recordings, credential checkout logs) and can integrate external logs from SIEM, SOAR, EDR, Threat Intelligence, and ITSM for expanded context.
• Contextual metadata: Includes information about the device, location, network, time, access method, credential type, applied policy, geographic origin, and user history.
• Triggered events: Every user action, credential checkout, session start/end, command execution, secret retrieval. It’s enriched and registered with complete context, forming a detailed and correlatable timeline.

Analytics engine and processing layers

The core of Segura®'s Analytics Behavior platform follows a multi-layered processing model:

• Collection layer: aggregates data from live sessions, event logs, external integrations, and security signals. Supports massive ingestion without performance degradation.
• Analysis and correlation: normalizes, enriches, and correlates events across multiple domains. Goes beyond isolated actions, analyzing sequences, frequency, deviations, and interactions.
• Baseline modeling: creates dynamic behavioral profiles for each user, credential, and asset, using machine learning to define what is common considering times, commands, devices, typing patterns, accessed resources, session durations, etc.
• Real-time evaluation and dynamic limits: new events are compared with the baseline using adaptive limits (adjusted by AI/ML). This allows for the identification of anomalies even when patterns change gradually.
• Response orchestration and triggers: upon detecting deviation or suspicious behavior (e.g., out-of-hours access, excessive viewing, use of a new device, abnormal command), the system triggers adaptive responses such as alerts, MFA, session suspension, or automatic integration with SOAR for containment.

Integration with logs, sessions, and external data

• Sessions: all session events (RDP, SSH, web, API, database) are correlated with the user's historical behavior and active context.
• Logs and auditing: every action, evaluation, and response is immutably registered, forming a detailed trail for compliance, forensic investigation, and regulatory reporting.
• External signals: the Behavior Engine can consume risk indicators from external systems (SIEM, SOAR, EDR, Threat Intelligence), increasing threat detection accuracy and enabling automated responses based on external events (such as instant blocking in case of confirmed IOC).

Decision and orchestration flow

Typical decision and orchestration flow:

1. Action: user performs an activity (e.g., starts a session, queries a password, executes a command).
2. Capture and enrichment: event is registered with full context (who, when, where, how, and why).
3. Baseline evaluation: system compares current behavior with individual baseline and risk model.
4. Thresholds and dynamic evaluation: if the behavior is within expectations, access proceeds normally. If it exceeds dynamic thresholds, triggers are activated.
5. Automated response: depending on the level of risk and policy, the system can alert the team, require MFA, suspend the session, revoke access, or trigger automated playbooks via SOAR.

Security, compliance and value

• Adaptive security: the engine adjusts in real-time to changes in behavior, reducing the response time to internal threats or compromised accounts.
• Automated execution: suspicious or anomalous actions result in immediate execution of policies, without the need for manual intervention.
• Reporting and compliance: reports and dashboards offer complete visibility for risk management, auditing, and compliance with regulations.

Baseline behavioral profiling

What is a behavioral baseline

A behavioral baseline is the reference profile built for each user, credential, or asset monitored by Segura®'s Behavior Engine. It represents the typical set of actions, patterns, times, access methods, and interaction behaviors that characterize the common and legitimate use of that user within the organizational context.

This profile serves as a dynamic parameter for continuous comparison, being updated as behavior evolves or new legitimate activities are learned.

Creating the user's profile

Segura® employs multiple sources and signals to build and update each user's baseline:

• Typing patterns: analyzes the speed, rhythm, pressure, and sequence of keystrokes during interactive sessions (RDP, SSH, web), identifying unique patterns for each user.
• Executed commands: monitors the sequence, frequency, and nature of commands and scripts used, both in terminals and specific applications.
• Access times: maps the most common times and days of the week for each user to access sensitive resources.
• Devices and locations: identifies endpoints, IP addresses, mobile devices, and usual access locations.
• Accessed applications: records which systems, web applications, databases, and assets the user usually utilizes, as well as the frequency and context.
• Session patterns: analyzes typical session duration, intervals between accesses, navigation workflow, and seasonal variations.
• Credential queries and views: monitors the frequency, type, and time of queries to secrets and passwords.

Baseline phases

The baseline process is divided into three main phases:

1. Initial learning: in the first accesses, the behavior engine collects broad data, learning the fundamental patterns of each user. In this phase, thresholds are more flexible to avoid false positives.
2. Continuous adaptation: with regular use, the baseline automatically adjusts to legitimate changes (e.g., new work hours, job promotion, change of role). The model learns new routines and dynamically adjusts limits.
3. Dynamic limits: the limits (thresholds) for anomaly detection are not static: they are adapted by machine learning algorithms and constantly reevaluated, taking into account organizational context, seasonality, and policy changes.

Real-time session analysis and continuous monitoring

The Segura® Platform Behavior Engine monitors every privileged session, RDP, SSH, Web, Database, API, local commands, among others, in real time. Capturing detailed signals and events from the beginning to the end of the session. This continuous analysis allows for the identification of normal patterns, behavioral deviations, and emerging risks without delays.

What is captured

• Typing patterns
  • Analyzes speed, rhythm, and typing style during the session.
  • Detects inconsistencies, such as operator changes, use of automatic scripts, or non-standard commands.
• Command flow and navigation
  • Monitors the sequence of commands, accessed files, and navigation paths in applications or terminals.
  • Detects atypical executions, unexpected jumps between resources, or automation patterns.
• Session duration and context
  • Evaluates if the session is compatible with the baseline usage time.
  • Identifies excessively long, short, or unusual time sessions.
• Device, location, and network context
  • Registers the endpoint, geolocation, IP address, VPN, and user environment.
  • Cross-references this data with the user's history to detect access from new locations or suspicious devices.

Adaptive analysis and profile update

• Dynamic profile: the user profile is constantly updated, incorporating legitimate changes and identifying rapid adaptations (e.g., travel, shift changes).
• Proactive risk identification: any sign of relevant deviation, such as a new command, use of atypical credentials, access outside of hours, or device change, is immediately evaluated by the analytical engine and can trigger automatic responses.
• Real-time update: there is no delay in the response. The system reacts in real time to suspicious activity, reducing the window of exposure to threats.

Use cases

• Takeover/imposter detection: changes in typing patterns, command usage, or timing may indicate that the account has been compromised.
• Unauthorized automation identification: repetitive sequences, without human variation, are quickly detected, preventing misuse of scripts.
• Unauthorized or improvised access: Sessions initiated from new devices, unexpected remote locations, or times incompatible with the user's profile generate automatic alerts and may require immediate MFA.
• Forensic tracking: every anomalous event is recorded with detailed context, supporting auditing, compliance, and post-incident investigations.

Integration with policies and responses

• Adaptive policies: rules and workflows can be configured to react automatically to anomalies, escalating the response according to the detected risk.
• Response orchestration: integration with SIEM, SOAR, and other Segura® Platform modules allows for triggering automatic playbooks, additional MFA, session pause/blocking, forwarding for analysis, notification of security teams, and much more.
• Zero standing privilege: continuous analysis ensures that any privilege granted can be dynamically revoked in case of risk, aligning with zero-trust best practices.

Triggers and adaptive response

Triggers: anomaly detection and risk events

The Segura® Platform's Behavior Engine utilizes a powerful combination of behavioral and technical triggers to identify deviations, risks, and signs of compromise in real-time. These triggers serve as intelligent sensors that, upon detecting unexpected patterns, immediately activate automatic responses or alerts for security administrators.

Main triggers monitored

Behavioral

• Typing anomalies: sudden changes in rhythm, speed, or keyboard pattern compared to the user's baseline.
• Execution of non-standard commands/systems: commands not usually used, attempts to access sensitive systems or data without history.
• Atypical times and locations: sessions initiated outside of business hours or from new time zones/locations.
• Device or endpoint switching: new devices, IPs, browsers, or access environments not previously authorized.

Technical

• Geo-velocity: detection of access from multiple incompatible geographical regions within a short period.
• Policy drift/privilege alteration: unplanned changes in permissions or access groups during a session.
• Suspicious navigation or interaction behavior: abnormal jumps between applications, excessive queries, repeated denied access attempts.
• External threat signals: events received from SIEM, SOAR, or Threat Intelligence indicating IOCs, vulnerabilities, or related activities.
• Real-time context change: detection of changes in ITSM ticket status, compromised device, or session revocation by third parties.

Adaptive response: automatic orchestration of responses

When triggers are activated, the Behavior Engine can execute one or multiple automatic responses, customizable according to the organization's policy and the context of the detected event. This approach ensures rapid risk mitigation and reduces incident response time.

Possible adaptive responses

• Step-up authentication: immediate request for additional MFA (OTP, push, certificate, biometrics) for enhanced user validation.
• Session blocking or pause: temporary suspension or forced termination of the session until human analysis or revalidation.
• Identity revalidation: requirement of new factors (smartcard, re-login, biometrics) if risky behavior is detected.
• Smart notifications: automatic alerts via email, dashboards, SIEM, or messaging to security teams.
• Forwarding to SOAR/forensic analysis: automatic triggering of playbooks on orchestrated response platforms, integrating logs and context for detailed investigation.
• Command/permission restrictions: immediate blocking of sensitive commands, downloads, transfers, or access to critical data.
• Increase of risk score: elevation of user or session risk, activating stricter mitigation policies for the entire duration of the session or subsequent sessions.

Practical examples

• A legitimate user accesses a system as usual, but with an anomalous typing pattern: MFA trigger.
• Session initiated from an unknown IP at 3 am: automatic blocking and alert.
• Dangerous command executed in a production environment without prior authorization: automatic response for suspension and audit.
• IOC signal received from SIEM correlated to an active session: response orchestration to isolate access and immediate investigation.

Advanced Behavioral AI Engine

The Advanced Behavioral AI Engine of the Segura® Platform is the analytical core that applies artificial intelligence and machine learning to the monitoring and modeling of user behavior during the use of privileged access. Unlike solutions based solely on fixed rules or exception lists, our behavioral engine continuously learns patterns, adapts to new routines, and identifies threats with increasing accuracy.

Technical and operational details

Multidimensional data collection

• Captured inputs: typing pattern, typed commands, navigation flows, access times, devices, geographic location, sequences of actions, credential access patterns, among others.
• Data sources: RDP, SSH, web, API sessions, local applications, event logs, external integrations (SIEM, SOAR, Threat Intel).

Behavioral modeling by ML/AI

• Dynamic baseline: the system builds a unique behavioral profile for each user, resource, group, or credential, based on learning periods and continuous adaptation.
• Supervised and unsupervised learning: ML algorithms monitor both subtle deviations and extreme outliers, allowing the detection of both small anomalies and abrupt pattern changes.
• Incremental update: the baseline is dynamically adjusted, following the natural evolution of functions, team changes, new devices, or applications.

Correlation and contextualization

• Correlated events: the analytical engine correlates events from different sources and sessions, detecting suspicious chains (for example, an unusual sequence of accesses and commands in different systems).
• Cross-user/asset analysis: identification of similar behaviors between users, detecting lateral attacks, unauthorized automation, or attempted privilege escalation.

Use cases

• Takeover detection: if a user starts typing differently or accessing systems in an out-of-pattern sequence, the engine signals a takeover risk and may require multiple revalidations.
• Prevention of unauthorized automation: Automated scripts tend to exhibit typing or navigation patterns different from humans; these patterns are identified and blocked.
• Continuous learning: routine changes (e.g., promotion, change of function) are incorporated into the baseline after validation, maintaining high accuracy over time.

Security, governance, and compliance

• Total auditability: all decisions of the Behavioral AI Engine are logged, including evaluated inputs, assigned scores, triggered triggers, and orchestrated responses.
• Compliance-ready: detailed logs and reports ensure adherence to regulatory requirements such as LGPD, GDPR, SOX, PCI-DSS, among others.
• Total integration: the Behavioral Engine acts as a central orchestrator, integrating adaptive access policies, automated responses, and threat intelligence data, promoting a predictive and contextual security posture.

Policy enforcement and orchestration

Behavior-based policy application

The Segura® Platform Behavior Engine is directly integrated with the central policy and authorization system, making behavioral enforcement a native and continuous part of the user journey. The behavioral engine's decisions affect permissions, approval flows, authentication, and real-time responses:

• Continuous authorization (CAEP): access permissions, privilege elevation, and session continuity are continuously evaluated as behavioral events and risk signals are captured, both in locally initiated and remote (RDP, SSH, Web, APIs, etc.) sessions.
• Dynamic policy enforcement: changes in baseline, suspicious behavior, or external signals (SIEM/SOAR) can dynamically modify policies, require step-up authentication, identity revalidation, or block sessions.
• Zero standing privilege and Just-in-Time: users only receive the minimum necessary privileges for the strictly necessary time and may be forced to revalidate their identity as risk changes.

Response and workflow orchestration

The Behavior Engine not only evaluates risks but also orchestrates automated and coordinated responses with other modules and external integrations:

• Step-up authentication (MFA/Certificates): upon identifying anomalies, the system may require additional MFA, the use of smartcards, biometrics, or digital certificates.
• Session suspension or blocking: sessions can be paused or terminated automatically if there is evidence of takeover, bot behavior, unauthorized automation, or threat intelligence signals.
• Custom workflows: behavioral triggers can initiate custom workflows. Sending an alert to the SOC, notifying the manager, generating a ticket in ITSM, or forwarding for forensic analysis.
• Automated remediation: the system can block credentials, revoke sessions, trigger containment scripts, or rotate secrets in response to detected risks.

Integration with Segura® policies and related modules

• PAM (Privileged Access Management): adaptive execution of access, approval, and justification workflows based on behavioral risk.
• A2A (App-to-App): conditional policies for automations and application integrations, allowing granular control over credentials used by bots, scripts, and integrations.
• Secrets, Executions, and Discovery: behavioral triggers can impact discovery, rotation, and secret distribution flows, revoking or requiring additional validation as context changes.
• Integration with ITSM/Workflow: direct integration with tickets, approval chains, and governance, associating behavioral events with business processes.

Continuous policy adaptation

• Risk-Based access: policies are constantly recalculated in real time according to risk score, context, time, location, and other variable factors.
• Policy automation: allows the creation of dynamic policies based on conditional logic, behavioral triggers, and external signals, promoting automation, zero trust, and self-healing.

Integration with threat intelligence and external signals

Connectivity with Threat Intelligence and SIEM/SOAR

The Behavior Engine of the Segura® Platform is designed to operate not only with internal data from the environment but also by integrating external signals and events for a comprehensive view of risk. This integration multiplies the system's detection, response, and adaptation power.

• Threat intelligence feeds: the Behavior Engine consumes threat intelligence feeds in real time, such as lists of Indicators of Compromise (IOCs), data on emerging attacks, phishing campaigns, and global threats. Suspicious events in user sessions can be automatically correlated with external information for faster and more contextualized risk detection.
• SIEM/SOAR integration: bidirectional integration with Security Information and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) systems allows the Behavior Engine to receive external alerts and also send critical behavioral events, triggering automatic response or investigation playbooks.

Orchestrated and automated actions

The connection to external risk sources enables orchestrated actions and adaptive responses:

• Triggering playbooks: Detection of anomalous behavior, when aligned with threat intelligence or SIEM alerts, can initiate automatic playbooks, such as session blocking, credential revocation, or incident creation.
• Dynamic risk-scoring: The risk score of each user or session is adjusted in real time according to signals received from external sources, making enforcement decisions even more precise and contextualized.
• Adaptive policy execution: Risk changes coming from external intelligence immediately impact permissions, MFA requirements, approval workflows, and other policies.

Practical examples

• IOC detected: if an endpoint used in a session is listed in an IOC feed, the session can be automatically blocked, and the incident forwarded for investigation.
• Event correlation: if a suspicious behavior in a session coincides with an active attack campaign identified by threat intelligence, automatic responses are prioritized.
• Integrated response: the Behavior Engine can be configured to work together with SOC teams, ITSM, and risk analysts, ensuring that relevant alerts are handled in multiple defense systems.

Extensibility and customization

• APIs and webhooks: allows easy integration with new threat intelligence sources, proprietary SIEM/SOAR, or third-party solutions.
• Custom triggers: administrators can create custom triggers based on threat intelligence signals, allowing tailored responses to the organization's risk profile.

Auditability, logging and compliance

Centralized and immutable logging

The User Behavior module of the Segura® Platform records all relevant events of the privileged session lifecycle and user activities in a centralized and immutable log. This includes:

• Session actions: start, end, duration, executed commands, viewed credentials, user switches, used devices, and context changes.
• Behavioral events: anomaly detection, activated triggers, executions of adaptive responses (e.g., MFA, session blocking).
• Automated responses: every decision of the Behavior Engine—alerts, policy enforcement, integrations with SIEM/SOAR—is recorded with a timestamp and detailed context.

All logs are protected against unauthorized modifications, with integrity verification mechanisms (hashing, digital signatures) and configurable retention according to compliance policies (e.g., SOX, GDPR, LGPD).

Visibility and searchability

• Detailed query: intuitive interfaces allow for quick searches by any action, user, session, or trigger, including filters by event type, risk, time, origin/device, and response status.
• Interactive dashboards: advanced visualizations show trends, risk hotspots, critical sessions, user behavior, and incident evolution.
• Forensic drill-down: allows for detailed (forensic) investigation of incidents, correlating multiple events and sessions to reconstruct a chain of suspicious actions or violations.

Export and integration

• Secure export: logs can be exported in standard formats (CSV, JSON, syslog, CEF, etc.) for integration with SIEM, auditing platforms, or forensic analysis systems. APIs and webhooks allow for export automation, realtime sending to external tools, and orchestration of personalized compliance and investigation workflows.
• Native integration: Behavior Engine logs can be consumed by external platforms via plug-and-play integrations (Splunk, QRadar, ArcSight, Elastic, etc.).