Building a Local AI SOC Analyst on an M1 MacBook Pro
The article discusses the development of a local AI-based SOC analyst designed to operate on an M1 MacBook Pro. It highlights the challenges faced in SOC operations, particularly in managing alerts and evidence correlation. The solution integrates various tools and models to enhance the analyst's workflow without replacing existing detection systems.
- ▪The project aimed to create an AI SOC analyst to assist with daily security operations.
- ▪Key components included a local AI model runner and integration with existing monitoring tools like Datadog and Sysdig.
- ▪The AI was designed to support alert triage and analysis without replacing existing detection capabilities.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3932577) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Mike Anderson Posted on May 24 Building a Local AI SOC Analyst on an M1 MacBook Pro #ai #soc #harness #aimodel How I solved a real SOC operations problem for Datadog, AWS, Cloudflare, Sysdig, PagerDuty with an AI runner, a local AI harness with a tricky model selection process Executive Summary We started with a practical SOC problem: build an AI-based SOC analyst that runs locally on an M1 MacBook Pro and helps with daily security operations across an existing cloud-native…
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