This post provides an overview of machine learning security risks and highlights key threats and challenges. It covers topics such as data security, tools security, ML model security, hardware security, end device security, and the human factor. The post also discusses the top four machine learning security risks: package security vulnerabilities, data poisoning, adversarial attacks, and data privacy. It concludes by providing best practices to improve machine learning security and mentions some open-source AI solutions.
Table of contents
The machine learning attack surfaceThe top four machine learning security risksBest practices to improve your machine learning securitySecurity solutions with open source AI15 Impressions