Private LLMS: Secure AI on your own terms

Across most organisations today, AI has quietly become part of everyday work, but not always through the tools approved by IT. Microsoft research found that 71% of UK employees had used unapproved AI tools at work, with 51% still doing so every week. The solution is not to ban AI, but to give employees a safe way to use it. That’s where a private LLM comes in. It runs the model and processes prompts and data within an environment your organisation controls.

At OneAdvanced, we help organisations across healthcare, legal, education, government and housing adopt AI, while maintaining control over their data. Our OneAdvanced AI platform is built on a sovereign LLM foundation, giving you the control, security and contextual intelligence enterprise AI demands. This guide explains what a private LLM is, compares the main deployment options and explores how to choose the right approach to embrace a managed, sovereign platform.

What is a private LLM?

A private LLM means deploying an AI model in an environment controlled by your organisation, so your data, prompts, documents and outputs remain within that environment and are not used to train anyone else’s model. It can run on your own infrastructure, in a dedicated cloud environment or through a managed platform.

Public AI tools such as ChatGPT, Gemini and Copilot offer general-purpose AI for a wide range of users and organisations. Enterprise versions provide additional security and data controls, but the level of control depends on the service and its configuration.

On the contrary, a private LLM can be configured around your organisation’s needs, using approved business data, access permissions and connections to the systems employees use every day. For example, an employee could ask a private LLM about a customer and receive an answer based on relevant information from your CRM, contracts or internal policies, provided they have permission to access it. You’ll explore more about the differences later in this guide.

Why are businesses moving to private AI?

As AI moves from experimentation into everyday business operations, data security, regulatory compliance and control are becoming harder to overlook. Three factors are driving the shift towards private AI are:

Regulatory accountability stays with you

Under UK GDPR, your organisation remains responsible for how personal data is collected, used, protected and transferred, regardless of which AI tool employees use. Serious infringements can result in fines of up to £17.5 million or 4% of global annual turnover.

The ICO has also confirmed that data protection law applies to generative AI. Using a public AI tool does not transfer that responsibility to the provider. Your organisation remains accountable for how its data is handled.

Data exposure is a risk

AI-related data loss does not always involve a sophisticated cyberattack. It can be as simple as an employee pasting sensitive information into an unapproved AI tool.

IBM’s 2025 research found that 97% of AI-related breaches involved systems without proper AI access controls. The lesson for business leaders is straightforward: organisations need clear oversight of where data goes, who can access it and how AI systems use it.

Sovereignty is becoming a procurement priority

For regulated organisations, choosing an AI service means looking beyond where data is stored. Buyers need to understand where AI models run, where data and model weights are held, who can access them and which laws govern the service.

The UK is investing heavily in sovereign AI capabilities, including £500 million through its Sovereign AI Programme. However, hosting data in a UK data centre does not automatically make an AI service sovereign or ensure it falls exclusively under UK jurisdiction.

The Shadow AI problem

Shadow AI, AI tools employees use without their employer’s approval, creates risks around data security, compliance and accountability. For businesses, the answer is not simply to restrict AI, but to provide a secure, governed alternative.

Why it happens

Employees rarely use unapproved AI to break the rules. They use it because it is familiar and helps them work faster. 41% of UK employees use consumer AI tools, while 28% say their employer provides no approved alternative. The issue extends to senior leaders too: TrustedTech found that 62% of senior leaders use shadow AI, compared with 31% of other employees.

What it puts at risk

The biggest risk is what employees share with these tools. Customer details, employee records, financial data and confidential business documents can all be entered into systems the organisation does not control. This can leave leaders with limited visibility over how information is stored, processed or used.

The financial impact can also be significant. IBM reports that 20% of organisations experienced a breach involving shadow AI, adding around $670,000 to breach costs on average. Shadow AI is therefore not just an IT issue; it’s a wider business risk involving data protection, compliance and financial exposure.

A governed alternative

The more effective approach is to give employees AI they can use confidently within their everyday workflows. OneAdvanced AI is a private, closed and encrypted LLM service embedded in the software employees already use. Data stays under UK control and is not used to train external models.

Retrieval-augmented generation (RAG) grounds responses in your own documents, while private workspaces support secure collaboration. Sector-specific AI agents can help with tasks ranging from clinical coding to complaints handling and risk management. Usage monitoring and governance controls give IT teams greater visibility, without the need to buy GPUs or hire a machine-learning team

Who needs a private LLM?

Across AI for regulated sectors, organisations face the same challenges: protecting sensitive data, avoiding costly errors and meeting regulatory requirements. Therefore, a private LLM is essential for:

Healthcare

Healthcare organisations handle highly sensitive clinical data. IBM’s 2025 study puts the average cost of a healthcare data breach at $7.42 million. A private, sovereign AI environment can help protect this data and meet data residency requirements. See OneAdvanced AI for healthcare.

Legal

For legal firms, client confidentiality and privilege leave little room for uncontrolled AI use. Private LLMs help legal firms protect sensitive information and maintain oversight of how AI is used. See OneAdvanced AI for legal.

Public sector

Government organisations handle large volumes of sensitive citizen data and face strict procurement and security requirements. Increasingly, that includes knowing where AI is hosted and how data is handled. OneAdvanced AI for government is designed for this environment.

Education

Education providers manage sensitive learner data, including information about children and young people, alongside safeguarding records. A private AI environment can help them reduce administrative workloads, while keeping this information protected within a controlled environment.

Private LLM deployment models compared

Here are four realistic routes. They differ less in security potential than in who carries the operational load.

Model	Control	Cost profile	Effort and skills
On-premises	Highest: hardware, weights and data in your walls	High capital; lower running cost at steady volume	High: GPUs, MLOps, patching
Private cloud (VPC)	High: isolated tenant	Lower entry cost; scales with usage	Medium: you run models and access
Hybrid	Tiered by data sensitivity	Hardest to price and govern	High: two environments
Managed sovereign platform	High: contractual and architectural controls	Predictable subscription	Low: no ML team needed

What does it take to deploy private AI?

Deploying private AI models require the following key elements to get reliable results and real business value from AI.

Infrastructure and hardware

Running an AI model in-house requires enough GPU memory and processing power to support it. Hardware is only part of the cost; organisations must also account for security, monitoring, data retrieval and ongoing maintenance.

Skills and ongoing cost

You will need people to patch, secure, evaluate and update the model indefinitely. Deloitte’s survey identifies regulatory pressure (48%) and talent gaps (40%) as key obstacles to enterprise AI adoption. Specialist skills and ongoing support can add significantly to the cost.

A ready-made sovereign platform

Embedding AI through a specialist provider can reduce the need to build and maintain the infrastructure yourself. MIT’s NANDA research found that AI solutions from specialised vendors and partners succeeded roughly 67% of the time, compared with lower success rates for internal builds.

A data house in order

Whether you build or embed AI, its value depends on the quality and accessibility of your data. Connecting finance, HR and procurement data across core business systems gives AI a stronger foundation. Start with our guide to what is ERP.

Private LLM vs public AI

The choice between public AI tools and a private LLM affects how your organisation protects data, meets compliance requirements, and controls costs as AI use grows. Here’s how the two compare:

Dimension	Public AI	Private LLM
Infrastructure	Shared, third-party, multi-tenant	Dedicated, enterprise-controlled (single-tenant or VPC)
Data retention & logging	Limited transparency, provider-defined	Fully visible and enterprise-controlled
Data usage	May be stored/used for provider models	Private, encrypted, and enterprise-controlled
Control	Limited updates/deployment control	Full configuration and lifecycle control
Data exposure risk	Increased due to external processing	Minimised through isolated, encrypted environments
Expertise	General-purpose, limited domain context	Domain- and enterprise-aware outputs
Governance	Limited oversight	Detailed audit trails, strong governance
Compliance	Limited, not enterprise-regulated	Aligned with enterprise security and regulatory standards

How to choose the right approach?

Before adopting a private LLM, ask these eight questions with your IT, security, legal and business teams to determine which approach best fits your organisation.

1. Which data will the AI touch?

Why it matters: Check whether it will reach personal data, special category data such as health records (which UK GDPR treats more strictly), legally privileged material, or confidential financial and commercial information. The more sensitive the data, the stronger the controls you need, and a data protection impact assessment is likely.

2. Does data need to stay in the UK?

Why it matters: UK GDPR restricts transfers of personal data abroad unless there’s an adequacy decision or an approved safeguard. Contracts, NHS arrangements and public-sector procurement often require UK hosting outright.

3. Is there a data processing agreement in place?

Why it matters: UK GDPR (Article 28) expects a written contract with any processor. It should cover documented instructions, confidentiality, security, sub-processors, deletion or return of data, and audit rights. Consumer terms of service rarely do this, so check the contract.

4. Who sees prompts and outputs, and is it logged?

Why it matters: Prompts often contain personal data, so the logs themselves need protecting. You need to know who in your organisation and at the provider can see them, how long they’re kept, and whether you can audit them.

5. Is your data ever used for training?

Why it matters: Look for a contractual commitment, not just a settings toggle. Ask whether it covers fine-tuning, human review and telemetry as well as model training, and whether opt-out or off-by-default applies.

6. Can you run it in-house, long term?

Why it matters: Be realistic about year two and three, not just launch. You need people for MLOps, security patching, model updates, evaluation and out-of-hours support. Ask who owns it, and what happens if one key person leaves.

7. Is governance built in, such as ISO 42001 alignment?

Why it matters: ISO/IEC 42001 is the international standard for AI management systems, covering policies, risk assessment, roles, monitoring and continual improvement. For a provider, ask for the certificate and its scope. Internally, you need an AI use policy, approved use cases, access reviews and output checks that carry on after go-live.

8. What is the total cost over three years?

Why it matters: Compare the options on the same basis: hosting and compute, hardware refresh, licences, specialist staff, maintenance, security and compliance work, integration, training and support, plus growth in usage. Staff time is the cost most often missed.

Common mistakes to avoid

Getting the most from a private LLM needs you to avoid a few common mistakes.

• Assuming cloud always means insecure: A well-architected private cloud, with encryption, isolation and strict access control, can match a server room. Ask who controls the data, under what contract and in which jurisdiction.
• Treating governance as a go-live task: Governance must continue after launch. Regular access reviews, clear policies and ongoing evaluation of AI outputs help maintain control. ISO 42001 provides a framework for managing AI responsibly.
• Underestimating maintenance: Models, software dependencies and security threats evolve. Budget for patching and evaluation as an ongoing operational cost.
• Starting with the model, not the workflow: Starting with the model rather than the business problem can lead to impressive demos but limited adoption. Begin with the workflow and the data it uses, then choose an architecture that meets your business needs and security requirements.

Conclusion: Make private AI an easy choice

Private AI no longer means choosing between security and productivity. Whether you build, buy or embed, the goal is the same: keep control of your data, how it is used and where it is hosted.

The real value comes when people can use AI confidently, with security and governance built in from the start. OneAdvanced AI makes this possible, delivering governed, UK-sovereign AI without the complexity of building and managing your own infrastructure.

Frequently Asked Questions (FAQs)

What is a private LLM and how does it work?

A private LLM runs in an environment your organisation controls, so prompts and data are not exposed to, or used to train, external models. It typically uses RAG to answer from your own documents, with access controls and logging.

Does a private LLM help with GDPR/UK GDPR compliance?

It helps but doesn’t guarantee compliance. Processing under your control, with a proper contract and UK hosting, makes your obligations easier to evidence. You still need governance, a DPIA where required, and clear policies.

How much does it cost to deploy a private LLM?

It depends on the route. Self-built, on-premises deployments mean hardware, specialist staff and maintenance. Private cloud scales with usage. A managed sovereign platform offers a predictable subscription. Compare three-year costs, including people.

Can a private LLM use our own company data safely?

Yes, and that is a major benefit. With RAG (retrieval augmented generation), the model answers from your documents and systems without that data training external models. Permissions should mirror existing access rules, and interactions should be logged.

Is on-premises AI more secure than cloud AI?

Not automatically. On-premises offers maximum physical control, but security depends on patching, access control and monitoring. A well-architected private cloud or managed sovereign platform can be equally secure with less operational burden.