Crack The Code: NIST's Adversarial Machine Learning (AML) Explained

This report is a game-changer for anyone working with machine learning systems. It develops a comprehensive taxonomy and defines vital terminology in adversarial machine learning (AML).

Many people struggle to wrap their heads around the various attack vectors and security risks AI systems face. Well, this report will break it all down for you in a way that's easy to understand.

We'll cover the different types of ML methods, the lifecycle stages of attacks, attacker goals and objectives, and the capabilities and knowledge attackers require. Plus, we'll explore strategies for mitigating and managing the consequences of these attacks.

Whether you're an AI developer, security professional, or just someone interested in the cutting edge of AI, you will find a ton of value in this video. So grab your notepad, get comfy, and dive into the NIST Trustworthy and Responsible AI report!