Segura® Intelligence collects data from multiple sources, analyzes behavior in real time using AI/ML, and enforces automated responses to risk. It’s modular, secure, fully auditable, and easy to integrate.
Data ingestion and integration
- Multi-source data collection: Segura® Intelligence ingests data from a wide range of sources, including privileged session recordings (video/logs), system and application logs, cloud entitlement events, configuration baselines, user interaction telemetry, audit trails, and support tickets.
- Real-time streaming: all supported session types, RDP, SSH, web/HTML5, database, API, and local console, are monitored in real time, with events tagged for contextual awareness (user, device, asset, geolocation, risk level and others).
- External feeds and APIs: the engine integrates natively with SIEM, SOAR, cloud identity platforms, EDR, ITSM, and vulnerability/threat intelligence feeds, enriching context for risk scoring and adaptive response.
AI/ML processing engine
- Layered analytical stack: the core intelligence module combines deterministic rules, advanced machine learning (ML), and GenAI models. The stack processes:
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Session data: parsing, event correlation, command interpretation, anomaly detection.
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Behavioral patterns: user, device, and entitlement baselines are created and continuously updated for each identity.
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Text and video analytics: transcription, semantic analysis, and summarization are performed on recordings and logs.
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- Dynamic baselines: AI builds unique profiles for users, service accounts, and assets, tracking login times, session duration, typing and command patterns, privilege use, and environmental context.
Real-time event analysis and triggering
- Continuous monitoring: events are evaluated against dynamic thresholds and baselines. Deviations from the established norm trigger risk scoring and, where relevant, automated or recommended response actions.
- Adaptive triggers: technical and behavioral triggers (e.g., suspicious privilege escalation, off-hours access, risky commands, device posture changes, unusual entitlement use) feed the AI’s risk model and policy engine.
- Just-in-time policy enforcement: when risk increases (detected by AI/ML or external signals), just-in-time policies enforce step-up authentication, access revocation, or session suspension.
Automated response and orchestration
- Integrated automation: Segura® Intelligence orchestrates responses, alerting security teams, triggering incident response playbooks (via SOAR), generating detailed audit logs, and enforcing custom workflows (e.g., privilege revocation, forced re-authentication).
- Closed-loop feedback: user and auditor feedback on AI-generated insights (e.g., false positives, incident outcomes) are ingested to refine future detection accuracy and adapt the model to evolving environments.
Privacy, security and compliance by design
- Data residency and isolation: all data processing can be scoped to comply with customer/regulatory residency and privacy requirements.
- Encryption and access control: all captured data is encrypted in transit and at rest; granular RBAC and ABAC models govern access to AI features and sensitive outputs.
- Transparency and auditability: every insight, recommendation, and automated action is logged and exportable for compliance (SOX, GDPR, LGPD, ISO, etc.).
Adaptability and extensibility
- Modular and extensible: new AI modules, connectors, and models can be added without disrupting core workflows. Open APIs and plugin frameworks enable integration with enterprise-specific or evolving security tools.
- Continuous improvement: the engine is designed for continuous learning, leveraging both supervised and unsupervised techniques as well as human-in-the-loop (HITL) validation, to enhance performance and minimize operational risk.