Private AI and Secure LLM Deployment

Your company knowledge is the thing that makes you competitive. It is worth asking why it is being uploaded to someone else's servers.

For many businesses the answer is that it was the only way to get started, and the trade was made without much scrutiny. That trade is no longer necessary. Capable models can now run inside your own cloud account, next to the data, with nothing crossing a boundary you do not control.

Private AI is the work of building that: an environment where staff get the assistants and automation they want, and the material those systems read never leaves your governance perimeter.

What private actually means here

The word gets used loosely, so it is worth being precise. There are three meaningfully different arrangements.

A vendor API with a no-training agreement. Your data still leaves your environment. The contract restricts what the provider does with it. This is adequate for a great deal of work and is the cheapest option.

A model running inside your own cloud tenancy. The data does not leave your account. You control the network boundary, the logging and the retention. Costs more, and it is the right answer for regulated material and genuine trade secrets.

Fully on premise. Your own hardware. Rarely justified now unless there is a regulatory requirement that names it, because the operating burden is real.

Most businesses need the middle option for a subset of their material and the first option for everything else. Deciding which material goes where is the actual consulting work.

Governance is the part people skip

Running the model privately solves one problem. It does not solve who can ask what.

An assistant with access to every document in the company will answer a junior employee's question using the salary review that was in the same folder. That is not a model failure, it is a permissions failure, and it is the most common serious incident in internal deployments.

So access control comes first. The assistant inherits the permissions of the person asking, rather than having its own blanket access. Every query and every retrieved document is logged. Material is classified before it is indexed, not after. On Databricks this maps cleanly onto Unity Catalog, which is one reason I build there when the data already lives in a lakehouse.

Training on your own voice without giving it away

The common request is an assistant that writes the way the company writes. Achieving that does not require handing your archive to a model provider.

Most of the effect comes from retrieval plus explicit style instruction: the system reads your existing material at the moment of writing and follows a documented style specification. That runs entirely inside your environment and is reversible, which matters when the style changes.

Fine tuning a model on your corpus is a bigger commitment with a narrower payoff, and it is worth doing only when retrieval and instruction have been tried and genuinely fall short.

What it costs you in convenience

Worth stating honestly. A private deployment is slower to set up, needs someone to operate it, and the model available inside your environment may lag the newest public release by a few months. In exchange, the material stays yours and the audit trail is real.

For most businesses the correct answer is a split: sensitive material handled privately, everything else on a vendor API with a sensible agreement. A blanket policy in either direction usually costs more than it saves.

Starting without a large commitment

A full private deployment is a significant piece of work, and it is rarely the right first step. The sensible opening move is the classification pass on its own: work out which material genuinely cannot leave your environment, and how much of your intended use actually touches it.

A common outcome is that the sensitive material is a smaller share than assumed, and a split arrangement covers it: a private deployment for that subset, a vendor API with a proper agreement for everything else. That costs a fraction of treating all material as maximally sensitive, and it is a decision better made on evidence than on policy.

A sensible first step

Start with the classification pass rather than the deployment. It is a small piece of work and it frequently changes the answer.

Businesses regularly discover that the material they cannot expose is a smaller subset than assumed, and that a split arrangement covers it at a fraction of the cost of treating everything as maximally sensitive. That is a decision worth making on evidence.

What the engagement includes

A data classification pass. Which material genuinely cannot leave your environment and which can, decided deliberately rather than by blanket policy.
Private deployment inside your cloud. Models running in your own tenancy next to the data, with the network boundary, logging and retention under your control.
Permission-aware retrieval. The assistant inherits the permissions of the person asking rather than holding blanket access, mapped onto your existing governance.
Full audit logging. Every query and every retrieved document recorded, so an access question can be answered with evidence.
An honest cost comparison. What private deployment costs against a vendor API for your actual volumes, so the decision is made on numbers.

How the work runs

Classify. Sort material by what can and cannot leave your environment. This decides the architecture.
Design the boundary. Where the model runs, what crosses the network, what is logged and how long it is kept.
Wire permissions first. Access control before capability, so the assistant can never answer beyond the asker's rights.
Deploy and evaluate. Stand it up in your tenancy and measure it against a real evaluation set.
Document for operations. Runbooks for the team who will operate it after handover.

Proof

Where the data already sits in a lakehouse, this is built on Databricks with Unity Catalog carrying the governance.

Talk about your situation

The first conversation is short and mostly questions. Get in touch and tell me what you are trying to fix. Or see how this is priced.

Frequently Asked Questions (FAQs)

What is private AI?

Running AI systems in an environment you control, so your data does not leave your governance perimeter. In practice that usually means models running inside your own cloud tenancy next to the data, with the network boundary, logging and retention under your control rather than a vendor's.

Is a no-training agreement with a vendor enough?

For a lot of material, yes, and it is considerably cheaper. It becomes insufficient when you are contractually or legally required to keep data within a boundary, when the material is a genuine trade secret, or when you need an audit trail the vendor cannot supply. The classification pass decides which of your material falls into that category.

Do private models perform worse?

The gap has narrowed a great deal, but the model available inside your environment may lag the newest public release by a few months. For retrieval-based work, where the model reads your documents and answers from them, that gap matters far less than retrieval quality does.

How does this work with Databricks?

Well, because the governance already exists. Unity Catalog gives table and column level permissions that a retrieval layer can inherit, the data is already governed, and models can run against it without the material leaving the workspace. If your data is already in a lakehouse this is the shortest path to a private deployment.

Can we train a model on our own content?

You can, but retrieval with an explicit style specification achieves most of the same effect, runs entirely inside your environment, and is reversible when the style changes. Fine tuning is worth doing once retrieval and instruction have been genuinely tried and fall short, not as the opening move.

Private AI: Enterprise AI on Your Own Data