AI Security Is an Engineering Problem — How to Solve It at Every Layer of the Agent Stack
NVIDIA says AI agent security needs enforceable boundaries and highlights OpenShell plus partner controls.
NVIDIA argues AI agent security requires enforceable controls across models, harnesses, and runtimes, not only instructions inside the model. Its open-source OpenShell runtime sandboxes execution and applies file, network, and process policy outside the agent's reach. Cisco DefenseClaw, JFrog, CrowdStrike SafeMind, Palo Alto Networks Prisma AIRS, Capital One VulnHunter, and ReversingLabs Spectra Assure are cited for governance, skill verification, continuous red teaming, and package analysis. The post says each deployment needs a scoped identity, human approval for consequential actions, and repeatable evidence that controls block unauthorized data export and privilege changes.
- Agent controls must hold even if the model follows malicious instructions.
- OpenShell sandboxes agents and enforces policy outside the agent.
- Partners add governance, skill scanning, red teaming, and package checks.
- Deployments need scoped identity, human approval, and test evidence.
- Organizations
- Open Secure AI Alliance
From the original article
4 min read at blogs.nvidia.com
AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work. As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what works faster.
By Saša Zdjelar · blogs.nvidia.com
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