Public vs. Private AI?
Most organisations are using AI. Far fewer are using it safely. The distinction between public AI and private AI is one of the most important decisions a business leader can make right now, and most are making it by default, without realising it.
What Public AI actually means
When your team uses ChatGPT, Claude, Gemini or any cloud-based AI tool, they are using public AI. Everything they input … their typed or audio prompts, the documents they upload, the context they provide … is sent to a third-party server, processed by an AI model that someone else owns, and handled under terms of service that most people have never read.
The AI model runs on the vendor's infrastructure. Your data goes there too. What happens to it after that depends on the vendor’s policies, which change, which vary by plan, and which are ultimately beyond your control.
For personal use, this is often fine. For business use involving client data, supplier data, financial information, IP, legal matters, or anything sensitive, it carries real risk.
What Private AI means
Private AI means the model runs inside your environment. Your data never leaves. Not to a cloud provider, not to an AI model vendor, not to anyone. The processing happens on infrastructure you control, whether that's on-premises hardware, a private cloud, or a secure edge device.
This isn't just a privacy preference. It has concrete implications:
Data ownership: private AI ensures that your sensitive corporate and personal data, and IP remain within your control. This reduces the risks associated with sharing data with external entities.
Regulatory compliance: private AI solutions comply with data protection laws such as GDPR, HIPAA, GLBA, the Privacy Act. Adhering to these regulations is vital to avoid legal consequences.
Tailored AI solutions: private AI allows for customised AI models tailored to your organisation's requirements. Unlike public AI, which may offer generic solutions, private AI enables you to develop models specifically optimised for your business needs.
Cost effectiveness: private AI offers more predictable costs, especially at scale. By investing in your own AI infrastructure and data management, you can avoid the unpredictable expenses associated with public AI services.
Strategic independence: with private AI, you maintain strategic independence over your AI initiatives. By controlling your data and AI models, you can make decisions that align with your organisation's long-term goals and objectives.
Audit trail: ensures that your AI inputs and outputs are fully auditable and accountable.
What isn’t Private AI?
There are thousands of software providers who claim to keep data private, but what they really mean is that they de-identify personal information (PII) before the data goes online to the model provider or to the cloud. These techniques may solve PII but they don’t protect other private, third party or confidential info or IP.
How does Private AI work?
All data intake and preprocessing stays in your environment.
AI model is trained locally using centralised or federated learning.
Privacy first inherent via encryption and anonymisation.
Deployed in controlled, offline infrastructure according to your policies.
Audit and accountability with full visibility of model behaviour.
Nuances worth knowing
Private AI doesn't mean inferior AI. The open model ecosystem such as Llama, Mistral, Qwen, Kimi and others have matured to the point where self-hosted models compete directly with frontier proprietary models, and often far more economically.
And “private” doesn't automatically mean on-premises. A well-configured private cloud deployment, where your data is isolated and processed only on infrastructure you control, can meet the same standard. The test is simple: can anyone outside your organisation access what goes in and what comes out? If the answer is yes, or even maybe, it's not truly private.
Practical questions for business leaders
Ask your team this week: when you use AI tools at work, where does the data go? If the answer is uncertain, that uncertainty is itself a risk worth addressing.
Organisations building durable AI capability are doing so on private foundations. They're not sending their most sensitive work to third-party infrastructure and hoping the terms of service protect them. They're building AI capability they own and control because in a (volatile) world where data is a competitive asset, control of that data is non-negotiable.