Implementing Secure by Design Principles for AI

The report argues that traditional, reactive cybersecurity approaches are insufficient for AI systems due to their nondeterministic nature and unique attack vectors (e.g., data poisoning, prompt injection, model deserialization). It recommends adopting a Secure by Design / MLSecOps strategy that integrates security across the AI lifecycle—threat modeling, data preparation, secure training and testing, deployment and monitoring—backed by executive commitment and specialized tools like model scanners, AI red teaming, policy enforcement, and automated incident response.