An On-Premise Alternative to Public Cloud AI That Keeps Data Inside Your Network

An on-premise alternative to public cloud AI keeps data inside your network by running every model on hardware you own, with no route off the premises. To do this genuinely it must provide five things: local inference, no telemetry, no model-improvement upload, a zero-egress inbound perimeter, and a signed record of every action. Data that never leaves the building cannot be logged by a vendor, compelled from one, or trained into a shared model. Mickai is a Sovereign Intelligence Operating System, a SIOS, engineered to this specification and running offline on operator-owned hardware.

The question matters more in 2026 than it did two years ago. Public cloud AI services such as ChatGPT, Claude and Gemini are capable, and for much general work they are the right choice, but they are the category that regulated buyers in defence, finance, healthcare and government cannot use for sensitive data. A prompt sent to a shared endpoint is a copy of your data on someone else's infrastructure, governed by their retention policy and their jurisdiction. The market has moved from asking whether a local alternative exists to asking what one must prove.

What must a genuine on-premise alternative provide?

Running a model on a server in your building is necessary but not sufficient. Many self-hosted setups still phone home. A genuine alternative meets a stricter test.

• Local inference: the model runs on your hardware, and prompts and outputs stay in memory you control.
• No telemetry: no usage metrics, crash reports or prompt samples leave the network.
• No model-improvement upload: your data is never queued for training, fine-tuning or human review elsewhere.
• A zero-egress inbound perimeter: the system serves requests from inside the network and opens no outbound connection.
• A signed record: every action is written to a tamper-evident ledger you can verify offline.

If any one of these is missing, data can leave, and the alternative is on-premise in name only.

How does keeping data inside the network actually work?

Three mechanisms do the work. First, inference is local, so the model weights and the runtime sit on operator-owned hardware and never call an external endpoint. Second, the perimeter is zero-egress: the SIOS accepts inbound requests but opens no outbound path, so there is no channel for a prompt to escape even if a component is misconfigured. Third, identity is hardware-attested and bound to the audit chain, so every request is tied to an attested device and user rather than a shared cloud account. Together these mean the sensitive payload has nowhere to go.

What can an auditor check?

An on-premise claim is only as good as the evidence behind it. Mickai writes every action, prompt, model response, access and configuration change to a post-quantum signed audit ledger. The ledger is signed using ML-DSA, the signature scheme standardised as FIPS 204, so entries can be verified as authentic and unaltered long after the fact and against future quantum attack. Confidentiality of keys inside the network uses ML-KEM, standardised as FIPS 203, which is key encapsulation and does not sign the ledger. Verification is offline: an auditor checks the signatures on the operator's own premises without contacting any external service. Pull the ledger, verify the signatures locally, and inspect the network boundary for any outbound route; if the signatures hold and the perimeter shows no egress, the record is intact.

“Data that cannot leave a network cannot be logged by a vendor, compelled from one, or trained into a shared model, which is why a zero-egress perimeter is a control and not a claim.”

Which rules make this necessary in 2026?

Several regimes push regulated buyers towards local inference, though the architecture supports a duty rather than discharging it. We never claim it satisfies or guarantees compliance.

• GDPR: keeping personal data inside a controlled network reduces the transfer and processor exposure that a shared endpoint creates.
• DORA: in force since January 2025, it holds financial entities responsible for the resilience and oversight of their technology, including third-party AI dependencies.
• NIS2: raises security and accountability duties for essential and important entities.
• US CLOUD Act: data held by a provider subject to US jurisdiction can be compelled regardless of where the servers sit, which a contract cannot fully neutralise.
• ISO/IEC 42001: the AI management-system standard that expects demonstrable governance and records.

On the EU AI Act, the high-risk Annex III obligations once due on 2 August 2026 were deferred by the Digital Omnibus to 2 December 2027, with embedded Annex I high-risk moved to 2 August 2028 and the Article 50 transparency duties largely unchanged. We read this as a build window, not a reprieve.

How is this different from a private cloud or a contractual promise?

A private cloud region and a data-processing agreement both keep data in a named place on paper. Neither is a technical guarantee. A contract is a promise enforced after a breach; a zero-egress perimeter is a control that prevents the breach. A provider subject to a foreign disclosure law can be compelled to hand over data it holds, and a signed clause does not switch off that statute. Using a public cloud service for regulated data does not automatically breach a law or waive privilege, but it creates exposure that is difficult to bound. Keeping the data on hardware you own removes the third party from the equation.

What does cross-model consensus add?

Local does not have to mean a single fragile model. Mickai can run several sovereign models in parallel and compare their outputs, a cross-model consensus that raises reliability on high-stakes questions without any of the data leaving the network. Accuracy and confidentiality are not traded against each other. The design behind this architecture is described in 104 filed UK patent applications, approximately 2,340 claims, owned by Mickai LTD, all patent pending and never granted or patented.

Frequently asked questions

Is a private cloud region the same as on-premise AI?

No. A private cloud region still runs on a provider's infrastructure under the provider's control and, often, a foreign jurisdiction. On-premise AI runs on hardware you own inside your own network. The difference matters when a disclosure law or a retention policy applies to the provider rather than to you.

Can ChatGPT or Claude be run fully offline inside our network?

No. Public cloud AI services such as ChatGPT, Claude and Gemini are delivered from the vendor's own infrastructure and require a connection to it, so prompts leave your network. A genuine on-premise alternative uses sovereign models that run entirely on operator-owned hardware, which is the design Mickai follows.

Does a data-processing agreement keep our data inside the network?

A data-processing agreement is a contractual promise, not a technical control. It can define where data should sit and how it should be handled, but it cannot physically stop an outbound connection or override a statute that compels the provider. A zero-egress perimeter enforces the boundary in the architecture rather than on paper.

What is a zero-egress inbound perimeter?

It is a network boundary that accepts inbound requests from inside your organisation but initiates no outbound connections. The system answers queries locally and has no route to send prompts, outputs or telemetry to an external service. This is what turns "we do not send your data" from a policy into an enforced property.

How do we prove to a regulator that data never left?

Keep a signed, tamper-evident record. Mickai writes every action to an audit ledger signed with ML-DSA, standardised as FIPS 204, that an auditor can verify offline on your premises. Combined with an inspection of the network boundary showing no egress, this gives a checkable record rather than an assurance.