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Air-gapped AI is an AI system deployed without network access to cloud inference or remote model repositories. It can perform tasks supported by the locally installed model and software when the required model files, applications, and data are already inside the isolated environment. The air gap describes how the system is connected—not a special kind of AI, or a guarantee that it is secure, accurate, safe, or up to date.
What “air-gapped AI” means
“Air-gapped AI” describes a deployment arrangement. It does not identify a distinct model category or determine what a model is capable of. NIST defines AI broadly as a machine-based system that, for human-defined objectives, can make predictions, recommendations, or decisions that influence real or virtual environments. An air gap tells you about the system’s network environment, not which of those functions it can perform.
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In NVIDIA’s NIM LLM 2.0.2 deployment guide, an air-gapped machine has no connection to remote model registries such as NGC or Hugging Face Hub. Model assets are prepared on a connected machine, transferred to the isolated environment, and loaded from local storage.
What an air-gapped AI system can do
It can run the functions supported by its installed model and software, provided everything those functions need is available locally. For example, a local assistant may answer questions about materials supplied to it, or a model may process inputs using its installed capabilities—if the chosen software supports the task and the necessary model assets and data are present.
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NVIDIA documents offline inference for a configured NIM deployment using locally staged assets. That example does not establish that every model, vendor, or workload supports the same arrangement. While disconnected, do not expect features that depend on cloud services, live web searches, current external information, or automatic downloads to work unless they have a separate local implementation.
How assets get into an isolated environment
The NVIDIA NIM workflow illustrates a common two-environment pattern: prepare the deployment while connected, then carry the required assets across an approved boundary for local use.
- Prepare on a connected system. Obtain and prepare the model assets needed for the deployment.
- Transfer the assets. Move them to the isolated system through a channel permitted by the organization. NVIDIA lists archive copy,
scp,rsync, and physical media as possible methods; these examples are not blanket approval for any organization. - Load and run locally. Mount or load the assets from local storage and run the model without outbound access or remote service credentials.
The organization’s security process determines which transfer method is allowed and what scanning, custody, and approval controls apply. A disconnected system cannot silently retrieve a newer model or current data. Bringing in updates or replacement assets requires a controlled preparation and transfer process; the exact validation and approval steps depend on the product and site.
Does an air gap make AI secure?
No. Network separation can reduce direct network connectivity, but it does not by itself secure the software, hardware, data, or people involved. NIST describes AI security in terms of protecting confidentiality, integrity, and availability, and notes that AI systems can have complex attack surfaces and evolving security challenges. Risks can remain in local components, transferred files, removable media, physical access, and operations.
Offline deployment also does not establish that a system is accurate or safe for its intended use. NIST’s AI Risk Management Framework calls for documenting a system’s intended scope and knowledge limits, testing validity and reliability, evaluating security and resilience, and recording limits on generalization beyond development conditions. Its trustworthiness guidance says accuracy measurements should use defined, realistic test sets representative of expected use. These controls matter whether a system is connected or isolated.
For operational technology, where AI may be integrated into environments supporting critical functions, security and safety concerns warrant particular care. On December 3, 2025, the NSA announced guidance issued with CISA, ASD’s ACSC, and other partners on secure AI integration in OT. Network isolation should not be treated as a substitute for that deployment-specific risk management.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess an air-gapped deployment
Before choosing or approving a setup, establish what actually works offline and what protections and operating procedures remain necessary.
- Offline capability: Identify which model-backed tasks run entirely locally and which features still require a remote service.
- Model and data handling: Determine what must be staged, how it crosses the boundary, and how integrity and custody are controlled.
- Compute and storage: Check the local resources required for the selected model and workload. Requirements are product-specific; the sources cited here do not quantify them.
- Updates: Find out how software, models, and security fixes are prepared, validated, approved, and brought into the isolated environment.
- Validation and safety: Confirm that intended uses, test conditions, performance limits, human oversight, and failure behavior are documented.
- Threat model: Consider risks in local software and hardware, removable media, people, and physical access—not only network connections.
NIST’s AI Risk Management Framework and trustworthiness guidance provide a basis for documenting scope, testing performance, and assessing security and resilience. NIST’s AI security material discusses the broader and evolving attack surface.
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