In this episode, we unpack a cutting-edge approach to building lean, high-performance AI models tailored for cybersecurity. Based on our latest white paper, we explore a multi-stage knowledge distillation pipeline that transfers expertise from large teacher models to smaller, more efficient student models like Phi-3 Mini. Topics include structured data enrichment, virtual machine-based learning, test-time reinforcement learning (TTRL), and curiosity-driven exploration powered by Information Theory. Whether you're an AI researcher, cybersecurity professional, or tech strategist, this episode offers a deep yet accessible guide to making specialized AI practical for real-world, resource-constrained environments.

​The paper titled "MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits" by Brandon Radosevich and John Halloran investigates security vulnerabilities introduced by the Model Context Protocol (MCP), an open standard designed to streamline integration between large language models (LLMs), data sources, and agentic tools. While MCP aims to facilitate seamless AI workflows, the authors identify significant security risks associated with its current design.​

In this episode, we explore how modern cybersecurity is transforming with agentless threat emulation. We discuss a cutting-edge platform that simulates advanced persistent threat (APT) tactics without installing agents—leveraging open-source tools like Atomic Red Team and PurpleSharp alongside the MITRE ATT&CK framework. Discover how the platform’s user-friendly, drag-and-drop scenario builder, remote execution via SSH/WinRM, and real-time monitoring empower cyber defenders to train effectively, identify detection gaps, and bolster overall security. Join us as we break down the technical innovations, operational benefits, and strategic value of continuous, automated threat simulations in today’s dynamic cyber landscape.