717: Overcoming Adversaries with A.I. for Cybersecurity, with Dr. Dan Shiebler

Dr. Dan Shiebler, Head of ML at Abnormal Security, joins Jon Krohn this week and unveils the intricacies of cybercrime detection and email protection, and the role of AI in future challenges. This episode is brought to you by Grafbase (https://grafbase.com), the unified data layer, by ODSC (https://odsc.com/), the Open Data Science Conference, and by Modelbit (https://modelbit.com), for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • The heuristic and “intermediate” ML models that they develop at Abnormal Security [07:08] • How Dan uses LLMs at Abnormal Security [15:46] • How false negatives are individually the biggest classification error to avoid in cybersecurity [20:49] • How head-to-head competitor analysis helps refine models [34:34] • Resilient ML in cybersecurity [38:36] • Abnormal Security’s routine for updating their models [52:37] • AI's impact on the urban world [1:09:57] • How to stay updated in data science and AI [1:13:46] Additional materials: www.superdatascience.com/717

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.