Secure AI deployment is complicated: 5 ways to get your ducks in a row

The report outlines key considerations for securing AI in enterprises, emphasizing AI-specific threats (e.g., prompt injection, adversarial manipulation, model poisoning/extraction), third‑party and supply chain risks, the need for clear employee AI-use policies, careful SOC integration, and new governance roles and skills. It underscores continuous monitoring for model drift, robust MLOps (reproducible training, testing, automated monitoring, rollback), and staying current with evolving regulations and best practices to safely scale AI.