As AI systems are increasingly deployed in production, securing them requires more than just protecting the models themselves. This session focuses on practical external strategies for safeguarding AI and its surrounding infrastructure. Topics include using resource controls to prevent denial-of-service attacks, applying rate limiting and input validation to secure APIs, detecting anomalies through audit logging and output filtering, and protecting data integrity with version control, automated backups, tokenization, and redaction.

By focusing on the systems and workflows around AI models, including containers, endpoints, and data pipelines, this session offers a broader and more realistic view of where threats emerge and how to stop them. Attendees will leave with actionable insights to improve the security of AI deployments and defend against risks like prompt injection, data leakage, and insecure configurations.