#AIForCybersecurity #Cybersecurity #ResilientMachineLearning Dr. Dan Shiebler, Head of ML at Abnormal Security, joins @JonKrohnLearns 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 https://jonkrohn.com/podcast for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:05:39] The heuristic and “intermediate” ML models that they develop at Abnormal Security • [00:14:17] How Dan uses LLMs at Abnormal Security • [00:19:19] How false negatives are individually the biggest classification error to avoid in cybersecurity • [00:33:04] How head-to-head competitor analysis helps refine models • [00:36:56] Resilient ML in cybersecurity • [00:51:09] Abnormal Security’s routine for updating their models • [01:08:35] AI's impact on the urban world • [01:12:24] How to stay updated in data science and AI Additional materials: https://www.superdatascience.com/717