Yamato-Security/suzaku

Description and use

**Brief summary**
Suzaku (朱雀) is an open‑source, Rust‑based tool that converts cloud logs into Sigma queries and automatically builds forensic timelines for threat‑hunting and incident response. It bundles a lightweight AI/ML layer that lets teams prototype retrieval‑augmented generation (RAG) or autonomous agent workflows without having to assemble a model stack from scratch.

**Value proposition**

• **Accelerated AI integration** – Suzaku ships with pre‑wired prompts and model adapters, turning raw log data into actionable insights with minimal ML engineering effort.
• **Fast forensics** – By generating Sigma rules and correlated timelines in seconds, security analysts can triage incidents faster and focus on remediation.
• **Extensible prototyping platform** – The codebase is modular, making it easy to plug in custom LLM providers, enrichments, or downstream SOAR actions.

**Practical adoption path**

1. **Proof‑of‑concept** – Clone the repo, run the provided Dockerfile or Cargo build, and feed a small sample of your cloud logs (e.g., AWS CloudTrail, GCP Audit). Verify that Sigma rules and timeline outputs are produced as expected.
2. **Integration checklist** – Review the README for required environment variables (LLM API keys, log storage credentials), add your own log ingestion adapters, and test the AI prompt templates against your internal data.
3. **Pilot rollout** – Deploy Suzaku as a sidecar service in a staging environment, hook it to a SIEM or ticketing system, and evaluate false‑positive rates and latency.
4. **Scale & harden** – Containerize the service, add monitoring, configure RBAC for model API keys, and implement automated tests for rule generation.

**Production readiness**
Suzaku sits at a **medium** readiness level. It is mature enough for internal prototypes and limited production use (181 ★, recent updates, Rust performance), but teams should perform due‑diligence on:

• **Dependency management** – audit Cargo crates for security vulnerabilities.
• **Model cost & latency** – benchmark your chosen LLM provider under realistic log volumes.
• **Observability** – add logging, metrics, and health checks before exposing it to critical workflows.

With these safeguards, Suzaku can become a reliable component of a cloud‑native security stack, especially for organizations looking to experiment with AI‑enhanced threat hunting without building a model pipeline from the ground up.

Use in a workflow

Yamato-Security/suzaku helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.