Private AI is fast becoming a must-have for any business that wants to use artificial intelligence safely. Every business wants to use AI, but not every business wants to understand the risk that comes with it. Over the past year, countless organisations have rushed to experiment with public AI tools. Staff have copied data into online chatbots, shared documents for analysis, and used prompts that would make a data-protection officer wince.
It feels innovative until you realise what has just happened. Sensitive information has left your secure environment and entered someone else’s training system. That is not progress. That is a potential data breach.
The Public AI Problem
Public AI systems are designed to learn from what people type into them. Every prompt, every upload, every correction helps train the model. It sounds harmless, but for businesses handling client data, contracts, or financial records, it is anything but.
Once data is submitted to a public model, you have no control over where it goes or how it is used. It might be stored, analysed, or reused in ways you cannot see. Even if the provider claims to protect user privacy, the data is still leaving your environment, and that breaks the basic rule of information security: keep control of your data.
Several companies have already learned this lesson the hard way. Some banned public AI tools outright after internal data was accidentally exposed. Others faced awkward questions from clients about where their information might have ended up.
Why It Matters More Than Ever
The privacy challenge is not new, but it is becoming far more serious. Regulations like GDPR, ISO 27001, and the upcoming EU AI Act are tightening expectations around data handling and algorithm transparency. Organisations will soon be required to prove that they know exactly how their AI systems make decisions and where the data sits.
If your AI lives on a public server, that is impossible. You cannot audit what you do not own.
The irony is that many businesses adopted AI to reduce human error and improve compliance. Yet by using public tools, they have introduced a much larger risk.
The Rise of Private AI
The smarter approach is what we call Private AI. Instead of sending information to external platforms, you keep everything within your own secure environment. The AI runs where your data already lives, under your controls and your governance.
This is not about limiting innovation. It is about building it safely. Private AI allows you to experiment and automate without exposing confidential information. It gives you all the benefits of automation and learning, but none of the fear that comes with data leakage.
At askelie® we built our platform, ELIE, around that principle. ELIE never sends your data to public models. It works entirely within your environment, whether that is on-premise or in your chosen private cloud. You stay compliant with GDPR and ISO 27001 while still using advanced AI to streamline operations.
How Private AI Works in Practice
With ELIE, each function is isolated, secured, and auditable. Nothing is shared or reused without explicit permission. You decide what data is processed, who can access it, and how long it is retained.
For example:
- intELIEdocs extracts and validates data from documents without sending a single file outside your network. It handles invoices, contracts, and forms in seconds, all within your secure boundary.
- askTARA reviews supplier risk and compliance information while keeping every record private. No supplier data is exposed to third-party systems.
- askVERA supports accessibility needs by converting information into Easy Read formats without ever using public AI engines.
Each tool within the ELIE platform applies the same rule: your data stays yours.
Why Public AI Creates a False Sense of Security
One reason many teams still use public AI tools is convenience. They are quick, free, and seem harmless. You ask a question, get an answer, and move on. But those answers come at a price.
Public AI systems store input to improve their models. They are not designed for confidentiality. They are designed for scale. When you upload data, it becomes part of something you do not control.
You would never email a client’s contract to a stranger for advice, yet that is effectively what happens when you paste the same document into a public chatbot.
Another problem is accuracy. Public models do not understand your business rules or compliance obligations. They generate responses based on probability, not policy. That might be fine for creative writing, but it is dangerous for regulated industries.
The Business Case for Private AI
Private AI is not just a security decision. It is a business decision. The more control you have over your data, the more value you can extract from it.
When AI is embedded inside your organisation, you can train it on your documents, your policies, and your workflows. It becomes more accurate and relevant over time. You gain speed without losing compliance.
The other benefit is cost predictability. Public AI often charges per query or token. Private AI, such as ELIE, runs on fixed subscriptions tied to usage, so you can scale with confidence.
Building Trust with Clients
Clients increasingly ask how their data is used and whether AI plays a part in processing it. A clear answer backed by a Private AI framework builds trust. It shows that you treat data protection as a responsibility, not an afterthought.
At askelie® we see this every day. Organisations using ELIE for contracts, education, HR, and supplier management can show their clients exactly how the system works and where the data stays. That transparency is becoming a key selling point.
How to Start the Shift
Making the move from public to private AI does not need to be complex. Here is how to begin.
1. Audit your current AI usage.
Identify where staff are using public tools and what kind of data they are entering. This helps you assess the level of exposure.
2. Set a clear policy.
Create simple internal guidance on what can and cannot be shared with public systems. Most employees do not mean to create risk; they just need clear boundaries.
3. Choose a secure platform.
Implement a Private AI platform like ELIE that lets you automate safely inside your own environment.
4. Communicate the change.
Explain to clients, partners, and staff that you have taken steps to protect their data through a Private AI strategy.
Conclusion
Public AI is easy to use but difficult to trust. It offers quick results but at the cost of control. As regulation tightens and clients demand proof of responsibility, the era of copy-and-paste AI is ending.
Private AI is the future. It is secure, compliant, and built for real business use. It allows teams to automate without compromise and innovate without fear.
At askelie® we believe privacy and intelligence should go hand in hand. With ELIE, you can have both.
The question for every business in 2025 is simple. Are you still sending your data into the public AI unknown, or are you ready to take control with Private AI?