Session: Fortifying the Future: Tackling Security Challenges in AI/ML Applications
As Artificial Intelligence (AI) and Machine Learning (ML) applications continue to surge, it is crucial to be aware of and address the security risks associated with these technologies. In this talk, Christine will explore AI/ML failure modes, threats, and mitigation strategies. She will guide you through the fundamentals of ML models then introduce you to key security challenges such as adversarial attacks, data poisoning, model inversion, model stealing, and membership inference attacks, using real-world examples to demonstrate their potential impact.
Christine will also discuss privacy and ethical considerations in ML, touching upon techniques like federated learning and shedding light on the current regulatory landscape surrounding security risks. If you are developing AI/ML applications or incorporating AI/ML components into your technology stack, check out this talk. You will walk away with a deeper understanding of the current AI/ML security landscape and a toolkit to help you address these risks, enabling you to build safer, more secure, and privacy-aware applications.
