nFOX Launches Secure AI Sandbox for Enterprises

The New York startup introduces tools to deploy and monitor open models within a kernel-enforced security environment.

Updated on Oct. 27, 2026 in Artificial Intelligence

Bold flat-color editorial illustration of a central hexagon within nested architectural frames, representing secure AI infrastructure. — New York-based nFOX has introduced its Blockhouse AI sandbox, providing enterprises with kernel-enforced security tools for deploying and monitoring open models. AI Illustration. Upload story photo >

Live Poll

Do you trust current security measures to protect your organization's data when adopting new AI tools?

nFOX has launched its AI platform and the Blockhouse sandbox, a system designed to limit access to model runtimes by default. The company also announced plans to eventually release the Blockhouse software as open source.

Why it matters

Enterprises currently face challenges when deploying open AI models, as security teams require verification of model behavior and access. This platform provides the necessary infrastructure to maintain control over AI operations in corporate environments.

The Blockhouse sandbox supports runtimes including vLLM, SGLang, and llama.cpp. System logs and audit records are generated in JSON and Parquet formats for integration with security information workflows.

The players

nFOX

A New York-based startup focused on developing secure infrastructure and monitoring tools for artificial intelligence model deployment.

The details

The platform enforces security boundaries below the application layer, using Porthole to route communications through a kernel-policed channel. Models are sealed using SHA-256 hashes to ensure file integrity during deployment.

October 27, 2026: nFOX launched the platform and announced open-source plans.

The Tech Race

This development marks a shift toward hardening AI runtimes, which previously operated with fewer restrictions. By integrating security directly into the model runtime, the platform positions itself against legacy approaches that rely solely on external application-layer firewalls.

Enterprise developers can now implement kernel-enforced security boundaries to control model access and improve log transparency. This provides businesses with a standardized way to audit AI behavior and ensure data integrity across their internal deployments.

The takeaway

Businesses looking to adopt open models should prioritize infrastructure that offers transparent auditing and runtime isolation. Utilizing kernel-level security tools helps satisfy corporate compliance requirements while maintaining performance flexibility.