Private AI
Bespoke AI systems for organisations that need more control over data handling, model deployment, access and logging than a default consumer AI service offers.
Public AI tools are convenient, and staff often start using them before any policy exists. That becomes a problem once prompts contain records the organisation is responsible for, because nobody can say where that information went or who can read it.
A service category, not one deployment model
Ryoss uses Private AI as a practical service name. It does not mean one universally agreed technical arrangement, and it does not mean that self-hosting is automatically private, secure or compliant. The appropriate design may be a suitable enterprise or provider arrangement, a model deployed in a controlled cloud environment, or a self-operated model, depending on the requirement.
The solution should match the sensitivity, performance, cost and operational requirements rather than defaulting to self-hosting. Running a model inside infrastructure the organisation controls can increase control while also creating substantial operational responsibility. Provider training, retention and residency claims should be checked against current contractual terms rather than assumed.
When this comes up
Sensitive records and public AI tools
Staff are pasting client or personnel information into public AI tools, and nobody can say where it goes or who can read it.
Contractual or residency requirements
Contracts or policy limit where data is processed, who can access it and how long prompts and outputs are kept.
An assistant that must follow existing permissions
An internal AI capability needs to respect the access rules already set for documents and systems.
A restricted network or environment
The model has to reach internal systems that cannot be exposed to an external service.
Typical decisions
Assess the requirement
Work out which information can go to which kind of service, and what contractual or residency constraints actually apply.
Compare deployment options
Compare provider-hosted, controlled-cloud and self-operated models on capability, cost, operational load and the controls they actually provide.
Access, network and logging
Design identity, authorisation, network restrictions and retention for prompts, retrieved information and outputs, then implement the chosen arrangement.
What you end up with
A deployment option chosen on evidence
A written comparison of provider-hosted, controlled-cloud and self-operated options against the actual requirements.
Access and logging that match policy
Identity, network restrictions and retention settings for prompts, retrieved information and outputs.
An operating model
A clear account of who runs the model, what it costs to run and what happens when it fails.
How a typical engagement works
01Requirements
Establish the requirements
We work out which information may go to which kind of service, and which contractual or residency constraints actually apply.
02Options
Compare deployment options
Provider-hosted, controlled-cloud and self-operated models are compared on capability, cost, operating load and the controls each provides.
03Controls
Build the controls
Identity, authorisation, network restrictions, logging and retention are implemented for the chosen arrangement.
04Handover
Hand over operating notes
Operating procedures and ownership are documented so the arrangement can be run and reviewed over time.
05Review
Review as options change
Controls, costs and the choice of deployment are reviewed as models, providers and requirements change.
Frequently Asked Questions
Start with a conversation
Book a free 15 minute call. Describe the problem, the systems involved and what you already know, and we will tell you plainly whether we can help and what a sensible next step looks like. There is no obligation.