[Reboot] ML foundations for cybersecurity in 2026
This session provides cybersecurity professionals with practical machine learning skills, from ML basics up to deep learning with TensorFlow. Participants will set up a complete development environment and learn foundational ML concepts through hands-on implementation rather than mathematical theory. The curriculum covers core ML principles through deep learning, with emphasis on security-relevant applications. No advanced mathematics or prior AI experience required.
We break the myth. You don't need a PhD to do AI here.
Pauline Bourmeau is an independent security researcher specializing in the intersection of artificial intelligence, cognitive psychology, and threat intelligence. She has consulted on multilingual natural language processing, led deep learning and NLP workshops, and created training materials blending STEM with human factors. As founder of DEFCON Paris and contributor to the MISP project, she actively advances collaborative cybersecurity practices.
Previously, Pauline worked as a Threat Intelligence Analyst conducting OSINT, HUMINT, and SOCINT analysis to profile threats and investigate APTs. She holds a Master’s in Criminology with a thesis on cybersecurity intelligence sharing, and a background in sociolinguistics and computer science from Sorbonne and School 42.