Automation

Introduction

In the previous article we embedded a local Ollama model directly into the Wazuh Dashboard chat via ML Commons. That approach provides full control over data with no cloud dependencies. In this series we take a parallel path: using AWS Bedrock - specifically Claude Sonnet 4.5 - as the inference backend, while all security data stays strictly within the local Docker network.

Wazuh decoder XML files define how raw log lines are parsed into structured security events. A misconfigured decoder – a missing <order> element, an orphaned parent reference, or a regex group mismatch – can silently drop critical fields from alerts, leaving blind spots in your SIEM pipeline. Manual code review catches some of these issues, but it does not scale across hundreds of decoder files shipped with Wazuh or maintained by your organization.

Introduction

Integrating local language models directly into the Wazuh interface opens fundamentally new capabilities for information security teams. Unlike cloud-based AI solutions, Ollama enables security event analysis entirely within an organization’s isolated infrastructure, eliminating the transmission of confidential data beyond the network perimeter. Embedding an AI assistant into the Wazuh dashboard provides SOC analysts with instant access to intelligent alert interpretation, automatic incident correlation, and response recommendation generation directly within the workflow context. This approach significantly reduces the time required for initial threat analysis and decreases the cognitive load on specialists, allowing them to focus on strategic decision-making instead of routine event processing. Meanwhile, full control over the model and data remains within the organization, which is critically important for regulatory compliance and internal security policies.

When Your SOC Analyst Can’t Keep Up (Or Just Needs a Break)

Let’s be honest: analyzing thousands of security events every day isn’t the most exciting job.

Introducing Wazuh LLM: Why Specialized Security Analysis Matters

In the cybersecurity world, SOC specialists deal with massive streams of security events daily. Analyzing each alert requires deep knowledge, experience, and time. That’s why I created a specialized language model to assist security analysts in their day-to-day operations.

Continuing the Series: Integrating a Wazuh Cluster with Ollama — Part 4. Configuration and Implementation

Related: Check out our Wazuh LLM fine-tuned model for specialized security event analysis.

Wazuh and Ollama: Part 3. Creating Integration Between Your Wazuh Cluster and Ollama

Wazuh offers vast and nearly limitless possibilities for integration with various systems. Even if a specific feature is missing, you can always create your own custom integration.

Wazuh and Ollama: Part 2. Deploying the Wazuh Cluster

Now it’s time to set up Wazuh, which we will integrate with Ollama.

Introduction

Welcome to the first part of our guide on enhancing Wazuh with Ollama!

Introduction

Integrating Wazuh SIEM with MARK (Mitigation Anomaly Revelation Keeper) enables automated threat detection and enriches security alerts with intelligence data. This guide walks you through setting up a custom integration for enhanced SOC operations.