Why it is worth attention

It combines a polyglot architecture (C++, Go, Python) with lock-free, SIMD-accelerated processing and a hybrid ML ensemble (supervised, unsupervised, reinforcement learning) for real-time threat detection, setting a new performance benchmark over traditional JVM-based SIEMs.

Who it is for

Security Operations Center (SOC) teams requiring scalable SIEM
Platform engineers deploying cloud-native security solutions
ML engineers building real-time intrusion detection pipelines
Security architects evaluating high-performance XDR platforms

Use cases

Real-time threat detection and correlation across network, endpoint, and cloud logs
Automated incident response and SOAR playbook execution
Anomaly detection using UEBA and unsupervised ML on user/entity behavior
High-frequency packet capture and flow analysis with DPDK and 84 CICFlowMeter features

Strengths

Claims 1M+ events per second ingestion with P99 detection latency under 15ms
Lock-free data movement and SIMD (AVX-512) correlation for high throughput
Hybrid ML ensemble (RF, XGBoost, LSTM, Isolation Forest) with real-time ONNX inference under 5ms
Adaptive Q-learning threshold tuning to reduce false positives by ~30%

Considerations

Performance benchmarks (1M+ EPS, <15ms) are stated but require validation in diverse production environments
Reliance on specialized hardware features (AVX-512, DPDK) may limit deployment on older or commodity infrastructure
As a whitepaper, real-world adoption metrics and community support are not yet evident

README quick start

AIGuardSIEM: A Production-Grade SIEM/XDR Platform

Comprehensive Technical Whitepaper

Version 1.0 — June 2025

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

Description

A production-grade SIEM/XDR platform for 1M+ EPS ingestion with sub-15ms detection latency. Built with C++, Go, and Python. Features DPDK capture, ONNX ML inference, Sigma rules, eBPF monitoring, and SOAR.

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