Emerging AI Security Risks And Considerations: Key Takeaways From The NIST Adversarial Machine Learning Report

As artificial intelligence (AI) systems become increasingly prevalent and their integration into organizations become more entrenched, businesses face novel and evolving security and privacy risks. These risks stem largely from systems powered by machine learning models that are vulnerable to a class of security risks known as adversarial machine learning (AML), which target AI systems, including machine learning models and data used to train and serve such models. These risks will require innovative approaches as organizations learn to adapt and respond to such threats.

To help organizations familiarize themselves with the types of attacks and risks they might expect, as well as approaches to help mitigate them, earlier this year the U.S. National Institute of Standards and Technology (NIST) published a comprehensive report intended to act as a cybersecurity guide for organizations that develop and oversee the governance of AI systems. The report is entitled "Adver