Your team is almost certainly using AI already. Whether it's ChatGPT for drafting emails, Gemini for research, or Copilot for code — public AI tools have become part of the daily workflow. But every time someone pastes company data into one of these tools, that data leaves your network.
For many organisations, that's an unacceptable risk. Here's a clear-eyed comparison of private and public AI.
What Happens to Your Data in Public AI
When an employee uses ChatGPT, Claude, or Gemini through their standard web interfaces:
- Every prompt is sent to the provider's servers (typically in the US)
- The provider processes your data on their infrastructure
- Conversations may be stored for service improvement
- Data may be used for model training (depending on the plan and settings)
- You have limited visibility into how data is handled after submission
Even with enterprise plans that promise not to train on your data, the fundamental issue remains: your data is processed on someone else's infrastructure.
The Comparison
| Factor | Public AI | Private AI |
|---|---|---|
| Data location | Provider's servers | Your infrastructure |
| Data exposure | Sent externally | Never leaves your network |
| Training risk | Possible (varies by plan) | Zero — your model, your data |
| Audit trail | Limited or none | Full logging of every interaction |
| Customisation | Generic responses | Trained on your specific data |
| Offline access | Requires internet | Works airgapped |
| Cost model | Per-user or per-token | Fixed infrastructure cost |
| Regulatory compliance | Depends on provider | Full control |
The Real Risks for Business
Regulatory Exposure
If your organisation handles data covered by GDPR, FCA regulations, SRA rules, or NHS data governance, sending that data to a third-party AI provider could constitute a data breach. Even if the provider has a Data Processing Agreement, you're still transferring data outside your controlled environment.
Intellectual Property Leakage
When employees paste proprietary code, business strategies, financial models, or client information into public AI, that intellectual property is now on someone else's servers. Samsung famously banned ChatGPT after engineers leaked source code through the platform.
Shadow AI
Even if your organisation hasn't officially adopted AI, your employees are probably using it anyway. This "shadow AI" is the biggest risk — uncontrolled, unmonitored, and invisible to your security team.
The question isn't whether your team is using AI. It's whether you have any control over how they're using it.
What Private AI Looks Like
A private AI deployment means:
- An open-source language model (like LLaMA) running on your own servers
- MCP servers connecting the AI to your internal tools and databases
- A web interface your team accesses just like they would ChatGPT
- Full audit logging of every query and response
- Role-based access controlling who can use which AI capabilities
- Fine-tuning on your company's data for domain-specific accuracy
From the user's perspective, it feels the same as using ChatGPT. From a security perspective, it's fundamentally different.
When Public AI Is Fine
To be fair, public AI tools are perfectly adequate when:
- You're working with non-sensitive, publicly available information
- You're using it for general knowledge queries
- No client, patient, or proprietary data is involved
- Your industry has no specific data handling regulations
But if any of your work involves sensitive data — and for most businesses it does — private AI is the responsible choice.
The Cost Question
Public AI seems cheaper upfront: £20/month per user for ChatGPT Plus, or pay-per-token for API access. But costs scale with usage, and enterprise plans with better security guarantees are significantly more expensive.
Private AI has a higher initial investment (infrastructure + deployment), but the ongoing cost is fixed. For organisations with heavy AI usage, private deployment often works out cheaper within 12-18 months — and you get security, customisation, and control that public tools can't match.
Making the Switch
Moving from public to private AI doesn't have to be all-or-nothing. Many organisations start with a private deployment for their most sensitive use cases (legal, financial, HR) while keeping public tools for general tasks. Over time, as the private system proves its value, usage naturally shifts.
Talk to us about deploying private AI for your organisation. We'll help you understand the options and find the right approach for your security requirements and budget.