https://blog.pytoshka.me/en/tags/dashboard/Recent content in Dashboard on pyToshka's DevSecOps BlogHugoen-USping@pytoshka.me (pyToshka)ping@pytoshka.me (pyToshka)Mon, 23 Mar 2026 21:09:47 +0400https://blog.pytoshka.me/en/post/local-ollama-in-the-wazuh-dashboard-for-llm-powered-insights/Wed, 21 Jan 2026 00:00:00 +0000ping@pytoshka.me (pyToshka)https://blog.pytoshka.me/en/post/local-ollama-in-the-wazuh-dashboard-for-llm-powered-insights/<h2 id="introduction"> Introduction <a class="header-anchor" href="#introduction" aria-label="Permalink to this section"> <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.65 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" fill="currentColor"/> </svg> </a> </h2><p>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&rsquo;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.</p>