Objectives

Build an intelligent security system that:

• Analyzes network traffic in real time,
• Uses ML to predict and detect attacks,
• Provides UEBA for behavioral insights,
• Surfaces actionable recommendations for admins,
• Supports compliance with GDPR, NIS2, ISO 27001.

Deliverables

• Anomaly detection module across network & system events,
• UEBA engine for user/entity behavior analytics,
• API integrations with SIEM (Splunk, Microsoft Sentinel),
• Incident dashboard with auditable logs,
• Deployment methodology and governance model.

Tech & Capabilities

• Machine Learning for threat classification & prediction,
• UEBA for user/entity behavior analytics,
• Cloud-native architecture (Kubernetes + Sentinel integration),
• Zero-trust security (RBAC/ABAC, encryption, audit logs),
• Observability via Elastic Stack, Grafana, alerting.

The platform blends AI, cyber defense, and digital trust to meet modern regulatory and resilience demands.

LEXANTE Advisory Role

• Risk assessment and needs analysis,
• Development of ML detection models,
• Implementation of the UEBA module,
• Integrations with existing SIEM/SOC,
• UX/UI for security dashboards,
• Pilot deployment, testing & training.

Project Partners

• Internal IT/Sec team – requirements & validation,
• External security experts – pen-testing & red-teaming,
• University partner – cyber methodology consulting.

Outcomes

• −55% MTTR (faster incident response),
• +65% detection accuracy vs. rule-only SIEM,
• Stronger compliance with NIS2, GDPR, ISO 27001,
• Higher trust from clients and regulators.

Publications

• Internal case study on AI in cybersecurity,
• White paper: “AI-first Threat Detection & UEBA”,
• Peer-review article for a cybersecurity journal (in review).