Wazuh MCP server: Bridging SIEM data with AI assistants

Security operations teams face a constant challenge: processing vast amounts of security data quickly and accurately. While Security Information and Event Management (SIEM) systems like Wazuh excel at collecting and storing security events, translating this data into actionable insights often requires manual analysis that can overwhelm you and your team.

The Wazuh MCP Server addresses this gap by connecting Wazuh SIEM data directly to AI assistants through the Model Context Protocol (MCP), enabling natural language interactions with security data and automated analysis workflows.

What the Wazuh MCP server does

The Wazuh MCP Server acts as a translator between Wazuh's API and MCP-compatible applications like Claude Desktop. Built in Rust for performance and reliability, this server transforms raw Wazuh security data into a format that AI assistants can understand and work with naturally.

Instead of manually querying Wazuh through its web interface or API, you can ask questions like "Show me critical vulnerabilities from the past week" or "Analyze recent authentication failures" directly through your AI assistant. The server handles the technical complexity of API calls, data formatting, and response structuring behind the scenes.

Key capabilities and use cases

The integration unlocks several powerful security operations workflows:

Automated alert triage: AI assistants can categorize and prioritize security alerts based on severity, affected systems, and threat context. This helps security teams focus their attention on the most critical events first, rather than manually reviewing hundreds of alerts daily.

Enhanced threat correlation: By combining Wazuh alert data with external threat intelligence, CVE databases, and OSINT sources, AI assistants can provide deeper context about security events. An authentication failure alert becomes more meaningful when correlated with known attack campaigns targeting similar systems.

Natural language security queries: Security teams can interact with their SIEM data conversationally. Questions like "What suspicious network activity occurred on our web servers last night?" get answered with specific, actionable information rather than requiring complex query syntax.

Dynamic reporting and visualization: Generate custom security reports and visualizations on demand. Instead of relying on pre-built dashboards, teams can request specific analyses tailored to current security concerns or compliance requirements.

Multilingual security operations: Global security teams can query Wazuh data and receive insights in multiple languages, improving accessibility and response times across distributed teams.

Implementation and setup

For detailed technical architecture, installation instructions, configuration options, and usage examples, visit the Wazuh MCP Server GitHub repository. The repository includes pre-built binaries for major operating systems, comprehensive documentation, and example configurations for Claude Desktop integration.

Why this integration matters

Traditional SIEM workflows require specialized knowledge to extract insights from security data by enabling natural language interactions with Wazuh data; the MCP Server democratizes access to security information. Junior analysts can query complex datasets without mastering Elasticsearch syntax. Incident responders can quickly gather context during active investigations. Security leaders can generate custom compliance reports in a short period of time.

The integration represents a shift toward more intuitive security operations, where the focus moves from data extraction to data analysis and decision-making. As AI-assisted security operations mature, tools like the Wazuh MCP Server help security teams stay ahead of evolving threats through more accessible and efficient workflows.