gensecaihq/Wazuh-MCP-Server — AI-powered security operations for Wazuh SIEM—use any MCP-compatible client to ask security question…

Description and use

**Brief Summary (2‑3 sentences)**
The gensecaihq/Wazuh‑MCP‑Server is a production‑ready Model Context Protocol (MCP) server that plugs AI assistants into the Wazuh SIEM, letting users ask security‑related questions in plain English. By exposing Wazuh’s APIs, SDKs and CLI through a standard MCP interface, it enables conversational SOC workflows for faster threat detection, incident triage, and compliance monitoring.

**Value**

• **Unified AI‑to‑tool bridge** – The server translates natural‑language queries into concrete Wazuh actions, allowing any MCP‑compatible client (ChatGPT, Claude, custom agents) to interact with real security data without custom code.
• **Accelerated operations** – Security analysts can retrieve alerts, run hunts, and generate compliance reports instantly, cutting mean‑time‑to‑detect and mean‑time‑to‑respond.
• **Standardised integration** – By adhering to the open Model Context Protocol, the project reduces vendor lock‑in and makes it easy to swap or add AI models while keeping the underlying security tooling unchanged.

**Practical Adoption Path**

1. **Deploy the server** – Pull the Docker image or run the Python package, configure it with your existing Wazuh API credentials, and expose the MCP endpoint (HTTP/WS).
2. **Connect an MCP client** – Use any MCP‑compatible chatbot (e.g., OpenAI’s function‑calling, LangChain MCP adapters) and point it to the server’s endpoint.
3. **Define intents** – Optionally extend the built‑in intent catalog (alert lookup, host‑inventory, compliance checks) via the provided SDK or YAML config.
4. **Pilot in a sandbox** – Run a limited set of security queries, validate the responses, and tune rate‑limits and authentication (OAuth/JWT).
5. **Roll out to SOC** – Integrate the chatbot into existing ticketing or chat platforms (Slack, Teams) for analysts to use in daily triage.

**Production Readiness**

• **Activity & community** – 188 ⭐, 55 forks, recent commits (last update 2026‑06‑26), and a Python codebase with 18 topical tags indicate an active project.
• **Maturity** – The server is described as “production‑ready” and already implements the full MCP spec, exposing Wazuh’s API, SDK, and CLI signals.
• **Scalability** – Designed as a stateless service; can be containerised and horizontally scaled behind a load balancer.
• **Risks** – Licensing and long‑term maintainer commitment still need a final check, and a security audit of the exposed MCP endpoint is recommended before production use.

Overall, Wazuh‑MCP‑Server offers a low‑friction way to bring conversational AI into a Wazuh‑based SOC, with a clear deployment path and sufficient maturity for pilot projects and, after the final security/license review, full production adoption.

Problem

gensecaihq/Wazuh-MCP-Server helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

AI-powered security operations for Wazuh SIEM—use any MCP-compatible client to ask security questions in plain English. Faster threat detection, incident triage, and compliance checks with real-time monitoring and anomaly spotting. Production-ready MCP server for conversational SOC workflows.

Source