Real-time threat detection at Rust-grade speed.
The Forensic CyberTech SIEM is a production-grade Security Information and Event Management platform that collects logs from across your environment, normalizes them into a unified schema, evaluates them against a powerful detection rule engine, and raises prioritized alerts in real time.
Built in Rust, the platform delivers enterprise throughput with a fraction of the infrastructure footprint—capable of sustaining 1M+ events/sec while providing native multi-tenancy and zero-downtime hot reloads.
Logs flow in over reliable transports, are decoded into a common schema, evaluated against your detection rules and correlation logic, and turned into prioritized, enriched alerts.
Multi-stage parsing for regex, key-value, JSON, CSV, and syslog.
Detection logic written as readable code with 70K+ use cases out of the box.
Detect patterns over sliding time windows and multi-stage attacks.
Maintain dynamic collections of IP addresses and domains for real-time lookups.
Durable, Kafka-backed buffering sustaining 1M+ events per second.
Complete per-client data isolation across storage, alerts, and APIs.
Detects impact, command and control (C and C), and data exfiltration in DNS traffic.
Flags unusual patterns and behavioral deviations from the norm
Identifies coordinated cyberattacks at any stage of the kill chain in real-time.
Managed Security Service Providers run dozens of clients on one platform with complete tenant isolation — each client's logs, alerts, and dashboards are fully separated, while a parent-child organization model and per-tenant detection thresholds let analysts manage everything centrally.
In-house SOC teams get real-time detection of brute force, lateral movement, intrusion, and malware activity, with enriched alerts routed straight to their ticketing or SOAR workflow for fast triage.
Pre-built rule packs and MITRE/compliance tagging support PCI DSS, HIPAA, GDPR, and SOC 2 monitoring, with retained, portable JSONL evidence for audits and investigations.
Correlate firewall, IDS/IPS, proxy, and endpoint logs across the estate to catch multi-stage attacks that any single tool would miss, with stateful sequence correlation linking related events into a single high-fidelity alert.
Ingest and normalize AWS, Azure, and GCP logs alongside on-prem sources for unified detection across hybrid environments — without standing up a separate analytics cluster.
The core engine is built in Rust with zero-copy parsing and no garbage collector, delivering up to 10× the per-core throughput of legacy agent-based SIEMs at roughly one-tenth the memory footprint. That means enterprise event volumes on commodity hardware — up to 50K EPS per node, scaling past 1,000,000 EPS across a cluster — with microsecond-scale rule evaluation and predictable performance that doesn't degrade as data grows.
Per-client isolation across storage, alerts, and APIs is a first-class part of the architecture — not an enterprise add-on or a shared index with filters. Ideal for MSSPs and large organizations.
Over 70,000 detection use cases ship ready to run — spanning authentication, network, firewall, web, endpoint, and compliance — with MITRE ATT&CK mapping, so you're catching threats from day one and tuning, not building from scratch.
Add or change rules, decoders, and lists with validated, atomic hot reload — no restarts, no maintenance windows, no dropped events. Detection keeps pace with the threat.
Run as a single binary, via Docker Compose, or on a cloud VM — no Elasticsearch cluster, no appliances, no specialist administration. Up and detecting in minutes, scaled horizontally when you need it.
Compared end-to-end against the leading open-source and enterprise SIEMs, the platform wins where it matters for modern security operations: raw performance at scale, native multi-tenancy, breadth of out-of-the-box detection, live extensibility, and operational simplicity.
| Capability | Wazuh | Splunk | IBM QRadar | Forensic CyberTech SIEM |
|---|---|---|---|---|
| Core engine & language | C / Python | C++ / Python | Java | Rust — memory-safe, zero-GC |
| Throughput per node | ~2K–10K EPS | High | Capacity-bound | Up to 50K/node · 1M+ scaled |
| Ingestion & normalization | Supported | Supported | DSM-dependent | Universal decoders + unified schema |
| Native multi-tenancy | Limited | Via indexes (Ent.) | Via domains (Ent.) | Full isolation, standard |
| Correlation & cross-correlation | Limited | Supported (SPL) | Supported | Aggregation + sequence + cross-correlation |
| Out-of-the-box detection content | Community rulesets | Apps / TA add-ons | Content packs | 70,000+ use cases bundled |
| Extensibility & live updates | XML, restart | SPL | DSM / custom | Rules-as-code + zero-downtime hot reload |
| Automation & alert routing | Basic | SOAR (premium) | SOAR (add-on) | Webhook / Slack / ticketing built in |
| Deployment flexibility | Agent-based | Heavy / distributed | Appliance / heavy | Single binary · Docker · cloud VM |
| Operational complexity | Medium | High | High | Low — no cluster to run |
| Resource footprint | Medium | Very high | High | Low |
| ★ denotes a capability unique to, or significantly stronger in, the Forensic CyberTech SIEM. Comparison based on publicly available information as of 2026 and reflects typical default configurations; capabilities vary by vendor edition and configuration. Verify current details directly with each vendor. | ||||
No cluster to provision and no appliances to rack. Deploy, point your logs at it, and start detecting.
Launch via Docker Compose, a single binary with systemd, or on a cloud VM such as AWS EC2. The full stack — ingestion, engine, API, and dashboard — comes up in minutes.
Step 01Forward logs over reliable syslog (RELP) or plain TCP from rsyslog, firewalls, and endpoints. Durable Kafka-backed ingestion guarantees no events are lost in transit.
Step 02The Universal Decoder Framework automatically parses and maps each vendor's fields into one unified schema, so your rules work the same across every source.
Step 03Start with 70,000+ bundled detection use cases covering authentication, network, firewall, web, endpoint, and compliance, then add your own in the DSL — applied live with zero-downtime hot reload.
Step 04Watch alerts and analytics in the React dashboard, query the REST API, and route high-severity alerts to webhooks, Slack, or your ticketing/SOAR platform. You're live.
Step 05Deployment prerequisites, sizing guidance, scalability characteristics, and benchmark figures for planning a production rollout.
Book a live demo and we'll walk you through detection, correlation, and multi-tenant monitoring on real data.
