Korea’s AI Ecosystem Advances and Secures Agentic AI With NVIDIA

NVIDIA is pulling Korean partners into its secure agentic AI stack, framed around a stated premise: as AI agents take on more work, they need capable open models and security that advances alongside them. Hyundai Motor Group is exploring NVIDIA NemoClaw blueprints and the NVIDIA OpenShell runtime, while Korean AI security company AIM Intelligence is integrating its Starfort platform with OpenShell to support secure AI agents for employee productivity and enterprise workflows.

OpenShell is a key component of the NVIDIA Open Agent Safety Platform. NVIDIA describes it as a secure runtime combining sandboxed execution with policy-based controls, built to safeguard sensitive data, manage access to enterprise tools and govern the actions of autonomous agents. In effect, the runtime governs what an agent may execute and reach, a layer separate from the models themselves.

For AIM Intelligence, the integration pairs OpenShell's runtime access controls with Starfort's detection of malicious instructions and data leakage. The company is also considering a preliminary proof of concept for the public energy sector, with potential applications it lists across enterprise, industrial and robotics environments. The combination addresses both sides of agent risk: the instructions an agent receives and the actions it is permitted to take.

On the model side, LG AI Research is opening application programming interface access for K-EXAONE-2.5, developed using NVIDIA Nemotron datasets, including Nemotron-Personas-Korea. That dataset is an open collection of 7 million fully synthetic Korean personas, grounded in official Korean statistics, that model builders use to ground training data and evaluation in Korean contexts. NVIDIA positions Nemotron as a family of open models, datasets and techniques for building specialized, efficient AI agents.

Open questions center on execution: whether AIM's public energy sector proof of concept advances beyond a preliminary stage, how Hyundai's exploration of NemoClaw blueprints and OpenShell translates into running agent workloads, and how sandboxing and policy-based controls hold up once agents act on live enterprise tools. Also unresolved is how far synthetic persona grounding takes Korean-focused models compared with models trained on collected Korean-language data.