A Disconnected Container Runtime is a container orchestration environment, typically a Kubernetes distribution, that has been deliberately severed from all external networks. It relies entirely on a local, air-gapped container registry to serve all application images, eliminating any dependency on public registries like Docker Hub. This architecture ensures that no container image pull, update, or dependency resolution ever traverses a network boundary.
Glossary
Disconnected Container Runtime

What is Disconnected Container Runtime?
A container orchestration engine configured to operate without pulling images from the internet, relying solely on a local, air-gapped container registry for all application deployments.
In this configuration, the runtime's default image pull policy is strictly enforced to source artifacts only from the internal registry. All necessary base images, application binaries, and sidecar proxies are pre-staged via a sneakernet protocol or one-way data diode. This guarantees deterministic, verifiable deployments and prevents supply chain attacks that exploit upstream image poisoning, making it a foundational component of a Zero Trust Architecture for critical infrastructure.
Core Characteristics of Disconnected Runtimes
A disconnected container runtime is not merely a standard orchestrator without internet access. It is a fundamentally re-architected platform where every dependency, security check, and lifecycle operation must be satisfied from a strictly internal, pre-seeded supply chain.
Local Registry Dependency
The runtime is configured to deny all external image pulls by default. All container images must be sourced from a private, air-gapped registry hosted within the secure perimeter. This requires a complete mirror of all base images, application dependencies, and sidecar proxies. The runtime's imagePullPolicy is strictly enforced, and any attempt to resolve an external endpoint results in an immediate failure, ensuring no data leaks through DNS or pull requests.
Immutable Base Images
To maintain integrity, runtimes in disconnected environments rely on immutable, hardened golden images. These images are built, scanned, and signed in a secure build pipeline before being manually transferred via sneakernet or a data diode into the air-gapped registry. Once deployed, containers are never patched in place; a new signed image is pushed and the old container is destroyed, adhering to strict immutable infrastructure principles.
Offline Admission Control
Without connectivity to cloud-based policy engines, the runtime must enforce security policies locally. Admission controllers and Policy as Code (PaC) engines operate entirely offline, using internally stored Rego policies or custom webhooks. These controllers validate every resource creation request against cryptographic signatures, vulnerability scan results from an offline vulnerability scanner, and internal compliance rules before a pod is scheduled.
Static Network Identity
Service discovery is handled through local DNS and static service mesh configurations. Since there is no access to public certificate authorities, all inter-service communication is secured via mutual TLS (mTLS) using certificates issued by an offline certificate authority (CA). The CA root is physically secured and only activated in a controlled environment to sign subordinate certificates, eliminating trust-on-first-use vulnerabilities.
Manual Artifact Injection
The deployment pipeline relies on physical media validation. Updates, including new container images, model weights, and vulnerability definition files, are written to removable media in a low-side environment. This media is then rigorously scanned for malware before being mounted in the high-side environment. The runtime watches these local paths for new artifacts, triggering automated canary deployments only after the artifact's digital signature is verified against a hardware-backed keystore.
Measured Boot Chain
The runtime's host operating system and container engine integrity are verified at startup through a measured boot process. Each component in the boot chain cryptographically measures the next before loading it, storing the hashes in a Trusted Platform Module (TPM). The runtime will only initialize if the remote attestation quote matches the expected golden measurement, preventing execution on a compromised kernel or container engine.
Frequently Asked Questions
Clear, technically precise answers to the most common questions about operating container orchestration engines in fully air-gapped environments without internet access.
A disconnected container runtime is a container orchestration engine configured to operate without pulling images from the internet, relying solely on a local, air-gapped container registry for all application deployments. In this architecture, the runtime—such as containerd, CRI-O, or a Kubernetes kubelet—is explicitly pointed to an internal registry mirror that hosts all required images. The runtime never initiates outbound connections to external registries like Docker Hub or Quay.io. Instead, all images are pre-staged by manually importing them via sneakernet protocols or one-way data diodes. This ensures that no runtime dependency, base image, or sidecar container can introduce a supply chain compromise through a network vector. The runtime's trust store is also locally managed, with internal certificate authorities signing all image references, making it impossible for a tampered image to be pulled without detection.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.

Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.

Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Related Terms
Core infrastructure components that enable container orchestration in physically isolated environments without internet dependency.
Admission Controller
A Kubernetes-native security plugin that intercepts API requests before resources are persisted. In disconnected environments, it enforces policies ensuring only images from the local private registry are deployed.
- Image Policy Webhook: Validates that every pod spec references only the internal registry URL
- Mutating Webhooks: Automatically rewrites image pull specifications to point to the local registry
- Policy as Code: Uses tools like Open Policy Agent (OPA) to define rules declaratively
- Drift Prevention: Blocks manual overrides that could attempt to pull from external sources
Immutable Infrastructure
A deployment paradigm where containers and nodes are never patched in place. Instead, a new golden image is built, tested, and deployed, with the old instance destroyed. This aligns perfectly with air-gapped security models.
- Golden Image Pipeline: Builds hardened images in a controlled environment before transfer
- Immutable Snapshots: Creates tamper-proof point-in-time copies for forensic baselines
- Rollback Strategy: Enables instant reversion to a known-good state by redeploying a previous image
- Configuration Drift Elimination: Prevents undocumented changes that accumulate over time in long-running systems
Offline Vulnerability Scan
The process of running security scans against container images and running workloads using a locally hosted vulnerability database. Definition files are manually imported via removable media.
- CVE Database Import: Transfers vulnerability feeds via data diode or USB from connected networks
- Trivy Offline Mode: Tools like Trivy support air-gapped scanning with pre-downloaded databases
- Runtime Scanning: Continuously monitors running containers for newly discovered vulnerabilities
- Compliance Reporting: Generates audit-ready reports without external data exfiltration
Policy as Code (PaC)
The practice of defining security and operational rules in machine-readable files stored in version control. In disconnected runtimes, PaC ensures consistent enforcement without relying on external policy engines.
- Rego Policies: Uses OPA's Rego language to define complex admission rules
- GitOps Integration: Stores policies alongside application manifests in a local Git repository
- Automated Remediation: Automatically rejects non-compliant resources with clear violation messages
- Audit Trail: Every policy decision is logged for forensic analysis in the air-gapped environment
Disconnected Kubernetes for AI
A specialized Kubernetes distribution configured to operate GPU workloads without cloud dependencies. It bundles all required components—container runtime, registry, and monitoring—into a self-contained deployment.
- GPU Operator Offline: NVIDIA GPU Operator deployed from local Helm charts without internet
- Local Helm Repository: Hosts all charts internally for deploying AI serving frameworks
- Node Feature Discovery: Operates in offline mode to label nodes with hardware capabilities
- Resource Isolation: Uses cgroups and NUMA pinning to guarantee GPU access for inference workloads

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us