Local Ollama in the Wazuh Dashboard for AI-Powered Security Analysis

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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.

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The Catcher in the Prompt: Day 60

The Catcher in the Prompt

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Day 60

Your own personal Jesus

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The Day the LLM Stood Still: A Diary from a World Without AI

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November 18, 2025, is the Day the LLM Stood Still….

Dear diary.

It’s been 15 days since the LLM bubble burst. I’m writing from beneath the rubble of RAM sticks and charred NVIDIA GPUs. The air is dry, smelling of data center dust and burnt silicon. It’s calmer now, but the first days were hell.

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Joining the Wazuh Ambassador Program

I’m excited to announce that I have officially joined the Wazuh Ambassador Program. This is a significant milestone in my journey with open-source security, and I’m honored to represent and contribute to a platform that has become central to my professional work.

My Journey with Wazuh

My path with host-based intrusion detection started long before Wazuh existed — with OSSEC, its predecessor. When Wazuh emerged as a fork and began evolving into the comprehensive security platform it is today, I transitioned along with it. That was over 10 years ago, and Wazuh has been an integral part of my security infrastructure work ever since.

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Two LLM Security Assistants for Wazuh and AWS Analysis

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.

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Introducing Wazuh LLM: Fine-Tuned Llama 3.1 for Security Event Analysis

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.

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Building ML-Powered Threat Intelligence with Honeypot Datasets on Hugging Face

Introduction

Picture this: you’re staring at security logs with thousands of events streaming in daily. Which ones are actually dangerous? Which can you safely ignore? Traditional signature-based detection is like playing whack-a-mole with cybercriminals — they’ve gotten really good at dodging known signatures faster than we can create them.

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Applying RAG for Working with Wazuh Documentation: A Step-by-Step Guide (Part 2)

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Prerequisites and Environment Setup

For local RAG development, ensure you have the following requirements:

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Applying RAG for Wazuh Documentation: A Step-by-Step Guide (Part 1)

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a method that allows the use of information from various sources to generate more accurate and useful responses to questions.

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Enhancing Wazuh with Ollama: Boosting Cybersecurity (Part 4)

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

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