AIGuardSIEM: A Production-Grade SIEM/XDR Platform

Comprehensive Technical Whitepaper

Version 1.0 — June 2025

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Table of Contents

1. Executive Summary
2. Problem Statement & Industry Context
3. System Architecture Overview
4. Data Ingestion Layer
5. ML-Based Intrusion Detection System
6. Core Processing Engine
7. XDR Capabilities
8. Incident Response & SOAR
9. Storage Layer
10. Visualization & Dashboards
11. API & Integration Layer
12. Security & Compliance
13. Deployment Architecture
14. Performance Benchmarks
15. Threat Model & Attack Surface
16. Future Roadmap
17. Appendix A: Technology Stack
18. Appendix B: Glossary

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1. Executive Summary

AIGuardSIEM is a modular, microservices-based Security Information and Event Management (SIEM) platform with Extended Detection and Response (XDR) capabilities. It is engineered to ingest, process, correlate, and analyze security telemetry at scales exceeding 1 million events per second (EPS) with end-to-end detection latency under 15 milliseconds (P99.9).

The platform employs a polyglot architecture that leverages the strengths of three programming paradigms:

• C++ (20) for performance-critical paths: data collectors, packet capture, stream processing, storage engine, and cryptography. These components achieve near-zero-copy data movement, SIMD-accelerated correlation, and hardware-accelerated encryption.
• Go (1.22+) for concurrent networked services: API gateway, service orchestration, cloud connectors, visualization backend, and the Kubernetes operator. Go's goroutine model and channel-based concurrency map naturally to fan-out/fan-in service topologies.
• Python (3.11+) for ML/AI pipelines: supervised and unsupervised learning, deep learning sequence analysis, reinforcement learning threshold tuning, UEBA risk scoring, and ONNX model infe