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In this episode, we welcome Chef Emmanuel Victorio, a security expert and data scientist at Microsoft, to Mr. Maeda’s Cozy AI Kitchen! Emmanuel walks us through a powerful hybrid approach that blends traditional machine learning with large language models (LLMs) to enhance security incident response and prevention.
From clustering security findings to generating actionable insights using OpenAI embeddings and K-means, this episode is packed with practical demos, real-world architecture, and advice for anyone navigating the evolving AI landscape.
Chapters:
00:00 – Welcome to the Cozy AI Kitchen
00:06 – Meet Chef Emmanuel Victorio
00:43 – Emmanuel’s background in security and AI
01:31 – Microsoft’s approach to security incidents
02:47 – Combining ML and LLMs for smarter security
03:56 – Architecture overview: From findings to embeddings
05:00 – Clustering with K-means and OpenAI
06:42 – Visualizing and interpreting clusters
08:01 – Using LLMs to explain and act on clusters
10:00 – From insights to engineer-ready actions
12:00 – Scaling with classification and continuous learning
13:45 – Advice for blending ML and LLMs effectively
14:30 – Final thoughts and sign-off
What You’ll Learn:
– How Microsoft Security uses hybrid AI to respond to incidents
– The role of clustering and embeddings in understanding security data
– How LLMs can generate actionable insights from complex datasets
📂 Links & Resources:
🚀 GitHub Repo with Demo Code: https://aka.ms/CAIK-AICyberSecurity
👥 Speakers:
Emmanuel Victorio – Security Expert & Data Scientist, Microsoft
LinkedIn: https://www.linkedin.com/in/emmanuelvictorio/
John Maeda on LinkedIn: https://www.linkedin.com/in/johnmaeda/
John Maeda on X: https://x.com/johnmaeda
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📣 Follow the Series
All Cozy AI Kitchen episodes: https://aka.ms/CAIK-YTPlaylist
🏷️ Tags & Keywords
#SipAndSync #MicrosoftDeveloper #AzureAI #SecurityAI #OpenAI #MachineLearning #LLM #GenerativeAI #AIFoundry #DataScience #KMeans #Embeddings #IncidentResponse #HybridAI #Developers #SoftwareDevelopment Read More Microsoft Developer
