This article argues that AI security should be treated as an engineering problem requiring defined security requirements, enforceable controls, named owners, and evidence that protections work, and proposes that security depends on the full agent stack (models, harnesses, runtime environments) working together, introducing NVIDIA OpenShell as an open source secure runtime with integrations across Open Secure AI Alliance partners, along with defensive tool examples such as CrowdStrike SafeMind, Palo Alto Networks Prisma AIRS, Capital One VulnHunter, and ReversingLabs Spectra Assure, while emphasizing open sharing of failure evidence and verification methods to shift advantage toward defenders.
This article argues that AI security should be treated as an engineering problem requiring defined security requirements, enforceable controls, named owners, and evidence that protections work, and proposes that security depends on the full agent stack (models, harnesses, runtime environments) working together, introducing NVIDIA OpenShell as an open source secure runtime with integrations across Open Secure AI Alliance partners, along with defensive tool examples such as CrowdStrike SafeMind, Palo Alto Networks Prisma AIRS, Capital One VulnHunter, and ReversingLabs Spectra Assure, while emphasizing open sharing of failure evidence and verification methods to shift advantage toward defenders.