Cybersecurity is a data problem. One of the most effective ways of synthesizing and contextualizing data is via natural language. With the advancement of large language models, we can expand detection and data generation techniques for cybersecurity applications. Organizations are in the …

Cybersecurity remains a top challenge for CEOs. Organizations that use security AI and automation can achieve significant cost savings, so many are integrating generative AI into products and services and using it to inform strategic and operational decisions. Join leading cybersecurity companies to learn …

As modern technology continues to advance at an unprecedented pace, organizations and societies face a delicate balancing act between harnessing the immense opportunities and mitigating the inherent risks. We'll delve into the realm of AI and Generative AI, exploring the challenges and …

The interplay between GPU computing, cybercrime, and cybersecurity has been an increasingly evolving landscape. GPUs, thanks to their computational efficiency, have been repurposed by threat actors for activities ranging from hash cracking and cryptomining to the deployment of trojan …

Inference with long contexts has become increasingly important for enabling real-world applications of Large Language Models (LLMs), particularly in scenarios requiring the integration of external information such as Retrieval Augmented Generation (RAG) and function calling. This talk explains …

AI is destined to transform cybersecurity in many ways, some of them yet to be discovered. The potential is a double-edged sword, also being available to sophisticated threat actors. But in the context of operational technology (OT) security, it's particularly the immense potential to improve …

Our previous work on graph-based flow detection detects malicious behaviors (malware, active threat, zero-days, etc.) across network topologies. While this is a very powerful approach to detection, it has obvious limitations, as it's constrained by the communication between hosts. We extend this …

AI clusters are collections of interconnected computing nodes that work together to perform complex tasks, such as data analysis, machine learning, and natural language processing. AI clusters are increasingly used in various domains, such as healthcare, education, and finance, to provide faster …