Sophia Rowland is a Senior Product Manager focusing on MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. As an active speaker and writer, Sophia has spoken at events like All Things Open, SAS Explore, and SAS Innovate as well as written dozens of blogs and articles. As a staunch North Carolinian, Sophia holds degrees from both UNC-Chapel Hill and Duke including bachelor’s degrees in computer science and psychology and a Master of Science in Quantitative Management: Business Analytics from the Fuqua School of Business. Outside of work, Sophia enjoys reading an eclectic assortment of books, hiking throughout North Carolina, and staying upright while ice skating.
As AI agents optimize how they communicate, the shift away from human-readable language underscores why transparency and interpretability are essential for building trust in autonomous systems.
As AI agents gain autonomy and access to sensitive systems, emerging threats like prompt injection worms highlight how human-like security training and governance must evolve to prevent large-scale, opaque cybersecurity breaches driven by agent behavior.
This article was co-written with Sundaresh Sankaran. The Artificial Intelligence (AI) era is here. To prevent harm, ensure proper governance and secure data, we need to trust our AI output. We must demonstrate that it operates in a fair and responsible manner with a high level of efficiency. As builders of
