What it is
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.