As AI adoption continues to grow across enterprise use cases such as chatbots, fraud detection, and recommendation systems, organizations often focus on improving performance and automation. However, behind these improvements, there is a deeper layer of risk that is often overlooked.
AI systems are not just models. They are built from data, pipelines, deployment environments, and human interaction. Many of the risks do not come from external attacks, but from how the system is designed, trained, and optimized.
In this webinar, we will explore hidden risks in AI systems from a research perspective. This includes first-order risks that come from data and model behavior, as well as second-order impacts in real-world settings such as bias, incorrect decisions, and business loss.
Using simple analogies and practical frameworks, this session will help you understand how these risks form, why they are often missed, and how organizations can start addressing them in a more structured way.
