Secure LLMs: Kubernetes on OCI with OKE and NVIDIA Guardrails
The increasing integration of large language models (LLMs) into enterprise applications has raised significant security concerns. Prompt injection attacks, which manipulate LLMs' behavior, pose a real threat to data integrity and system access. Traditional security measures often fall short in protecting LLM endpoints. However, a robust solution has been proposed using Kubernetes on Oracle Cloud Infrastructure (OCI) with OKE.
The proposed solution employs containerization to wrap LLMs and their supporting components, enforcing strict security boundaries. This approach offers multiple layers of protection, including network isolation, resource constraints, runtime security policies, and container-level access controls. Additionally, the implementation features multilayered network, resource, and access policies in Kubernetes, combined with the use of NVIDIA NeMo Guardrails.
NVIDIA Guardrails is an open-source framework that integrates safety filters to scan and sanitize prompts before they reach the LLM inference engine. Its key features include text filtering, context enforcement, and adaptive learning. This ensures that only authorized and safe prompts reach the core model logic, preventing unintended behaviors and data breaches.
The design also integrates with Kubeflow for continuous training, validation, and deployment (MLOps), allowing for seamless integration into existing workflows. The solution is set to be showcased at the upcoming KubeCon + CloudNativeCon Europe 2025 in London.
By combining containerization, Kubernetes, OKE, and NVIDIA NeMo Guardrails, enterprises can deploy LLMs securely, mitigating the risks associated with prompt injection attacks. This enterprise-grade solution ensures the protection of sensitive data and maintains the integrity of internal systems. As LLMs continue to play a crucial role in enterprise applications, such robust security measures are paramount.
