Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Discusses the ongoing challenge of embedded security in healthcare, emphasizing the need for solutions to protect against malicious behavior and secure critical assets and services.
