Secure deployment
Air-gapped & on-premises AI deployment
For organisations that cannot send their data to a third-party AI service, Integrated AIS designs and deploys AI that runs entirely within infrastructure you control — on-premises, in a private cloud, or fully air-gapped with no external network dependency at all. The model comes to your data, so your data never has to leave your environment.
What air-gapped AI deployment means
An air-gapped AI deployment runs models, data and inference inside a network boundary with no connection to the public internet, so data cannot leave the environment even in principle. It is not a stripped-down cloud product with the network switched off — it is an architecture built from the ground up around data sovereignty and zero egress, where every dependency the system needs at runtime already lives inside the enclave and nothing calls out silently.
On-premises deployment is the same idea a step short of full isolation: the system runs on infrastructure you control, under your security architecture, without depending on an external provider — but may retain controlled connectivity where the constraint allows it. Both keep sensitive data inside your walls; which one you need depends on how hard your constraint actually is.
Air-gapped AI infrastructure: what sits inside the boundary
Because nothing can be fetched at runtime, an air-gapped deployment has to carry its whole supply chain with it. The infrastructure inside the enclave typically includes:
- Compute — GPU servers sized for the models and the number of people using them at once.
- Model weights — open-weight models held locally, verified before they cross the boundary.
- An inference layer — the serving software that runs the models and exposes them to internal applications.
- A retrieval store — the organisation's own documents, indexed inside the enclave so answers draw on internal knowledge.
- Package and container mirrors — every software dependency, staged internally, so no component calls out for an update.
- Identity, logging and monitoring — access control and a full audit trail that live inside the boundary too.
- A controlled transfer route — the governed, inspected path by which updates and new models are brought in.
The last item is where most designs are weakest. A deployment is only as isolated as the process for moving things across the gap, which is why operating an air-gapped system in production matters as much as building it.
Who needs it
This is for organisations where the public cloud AI stack is simply not an option — because the data is regulated, classified, or held under contractual confidentiality that predates the AI conversation entirely. In practice that means financial services, public sector and defence, healthcare, critical infrastructure and energy, and the professional firms that hold sensitive client data.
The common thread is a constraint that no amount of contractual assurance from a cloud provider can satisfy: the data must stay under your control. Where that is a hard requirement, on-premises or air-gapped deployment is not a preference but a precondition — and it is exactly the work Integrated AIS is built for.
How we deliver it
Secure deployment is a core part of how we design systems from the outset, not an option applied afterwards to something built on public cloud. We assess the data classification, network boundaries and accreditation regime the deployment must satisfy; architect the topology around zero egress and least-privilege access; install and harden it inside your infrastructure with every dependency accounted for; and provide the ongoing patching, monitoring and re-accreditation support that keeps it compliant as it evolves.
This sits inside our broadersecure deployment capabilityand oursecurity posture, and it is deliberately vendor- and model-neutral — we deploy the right model for your constraint rather than steering you toward one we happen to sell.
Common questions
Is air-gapped AI slower or less capable than cloud AI?
No. The same class of models runs inside an air-gapped environment as in the cloud — the isolation is a network boundary, not a downgrade. Local inference removes network round-trips entirely, which for many workloads is faster. What changes is the operational discipline around updates and maintenance, not the capability of the model itself.
Can you update a model that has no internet connection?
Yes. Updates cross the boundary on signed, reviewed media on a controlled cadence, rather than over the network. This is slower and more deliberate by design — that is the point of the air gap — and we plan the update and re-accreditation process into the deployment from the start rather than discovering it later.
Do we actually need a full air gap, or is on-premises enough?
Often on-premises or a private deployment is enough. A full air gap is warranted when the requirement is that the system must have no network path to the outside world at all — typically for classified, defence or critical-infrastructure data. Where the constraint is residency or contractual confidentiality rather than isolation, a less extreme architecture usually satisfies it at lower cost. Establishing which you actually need is the first thing we do.
Will an air-gapped deployment fit our existing security accreditation?
That is how we design it. We work within your existing security architecture, hardware and approval processes rather than asking you to adopt new infrastructure to accommodate us, so the result is something your security and compliance functions can sign off on rather than work around.
Read further
In-depth writing on secure and air-gapped AI deployment:
Talk to us about a secure deployment
If you need AI that keeps data inside your control, we can help you work out which architecture your constraint actually requires — and build it.
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