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.

This NIST report offers a comprehensive exploration of adversarial machine learning (AML), detailing threats against both predictive AI (PredAI) and generative AI (GenAI) systems. It presents a structured taxonomy and terminology of various attacks, categorising them by the AI system properties they target, such as availability, integrity, and privacy, with an additional category for GenAI focusing on misuse enablement. The document outlines the stages of learning vulnerable to attacks and the varying capabilities and knowledge an attacker might possess. Furthermore, it describes existing and potential mitigation strategies to defend against these evolving threats, highlighting the inherent trade-offs and challenges in securing AI systems.

In this episode, we delve into a newly published white paper that outlines a cutting-edge pipeline for enhancing language models through knowledge distillation and post-hoc mechanistic interpretability analysis. We explore how the approach integrates data enrichment, teacher pair generation, parameter-efficient fine-tuning, and a self-study loop to specialize a base language model—particularly for cybersecurity tasks—while preserving its broader language capabilities. We also discuss the newly introduced Mechanistic Interpretability Framework, which sheds light on the internal workings of the distilled model, offering insights into layer activations and causal pathways. Whether you're building domain-specific AI or curious about making large language models more transparent, this conversation reveals how domain expertise and interpretability can come together to create more trustworthy and efficient AI systems.