What is Private AI? Keeping Data Safe from Leaks and AI Training

What is Private AI?

Private AI is like having a personal AI assistant in a vault, it’s an AI system that runs in a secure environment (optionally in your control), ensuring your data stays confidential. In simple terms, Private AI refers to an AI models or platforms set up exclusively for particular group of user or organization, accessible only by them.

Private AI is the opposite of public AI services like ChatGPT, Google’s Gemini, or other consumer chatbots, where everyone shares the same model and your interactions are used to improve the AI for others. The goal of Private AI is to keep your information truly private, safe from unintended leaks and off-limits from being used for AI training without your consent.

Imagine the difference between having a conversation in a crowded coffee shop vs. in a soundproof room. Using a public AI (like a ChatGPT or Claude) is the coffee shop scenario, the AI provider could overhear (or later reuse) what you share. Using Private AI is the soundproof room, the conversation stays between you and your personal AI, with no eavesdropping. In real life, this means when you ask a private AI model a question or give it data, that data doesn’t get sent out to a big tech company’s servers or pooled into a giant cloud model. It stays locked down in a private environment.

Key Statistics on Public AI Risks:

• Nearly 60% of workers have pasted sensitive company data (client records, financial info, etc.) into public AI tools like ChatGPT.
• 68% of employees use generative AI through personal accounts rather than company-approved platforms – a “shadow AI” trend that bypasses IT oversight and heightens leak risks.
• 14 major companies (including Apple, Amazon, Samsung, and JPMorgan) banned or restricted employee use of ChatGPT within months of its launch, due to fears of confidential data leaks (Business Insider, Jul. 11, 2023).
• €15 million fine – In late 2024, Italy’s data protection authority fined OpenAI €15 M for breaching privacy rules by using personal data to train ChatGPT (Reuters, Dec. 20, 2024).
• 78% of enterprises are already using or planning to deploy private, in-house AI models, largely because of concerns around data security and governance.
• 95% of companies acknowledge the need for stronger security measures for generative AI, and 94% are concerned about data protection when using AI tools in business.

Let's dive deep into what is Private AI? why it matters? and how you can adopt it

Private AI vs. Public AI: Why Privacy Matters

To clearly understand Private AI, we can compare it with the public AI services most of us know. Public AI models (think ChatGPT, Claude, Gemini, etc.) are large, general-purpose AI systems hosted and owned by providers. They’re “multi-tenant,” meaning many users are using the same AI model. When you use these services, your prompts and data typically travel over the internet to the provider’s servers. Often, your inputs are logged, stored, or even used to further train the model behind the scenes. This becomes a threat to your data and information when you share personal or business information that is supposed to be confidential, but it ends up getting used for AI Training and leaks into conversations by others.

In comparison, Private AI often runs in a private servers, protected environment, or locally on your device. Okara is one such example where all the open-source AI models are hosted in private servers and the user data and chats are stored with encryption at rest, ensuring no one has access to your data.

The AI model in a private setup are not continually learning from user inputs unless you explicitly want it to. And, a private AI platform will not send your data to an external provider for processing. As a result, your prompts, files, and AI responses aren’t siphoned off to improve someone else’s model or seen by the provider’s engineers.

Let’s break down the key differences in a quick list:

• In summary, public AI is like a one-size-fits-all utility – convenient and powerful, but inherently shared and somewhat beyond your control. Private AI is more like having your own custom machine – dedicated, controlled by you or a provided. Next, we’ll dive deeper into why relying solely on public AI can be risky, especially for sensitive data.

The Risks of Public AI: Data Leaks and Privacy Nightmares

Public AI services have opened up amazing possibilities, but they come with serious privacy trade-offs. If you’re a developer or professional feeding real data into ChatGPT or a similar model, you might be exposing information in ways you never intended. Let’s talk about these risks – the “AI leaks” and unwanted training that can happen with consumer AI models:

• Accidental Data Leakage: Perhaps the most immediate worry is that whatever you type into a public AI could leak out. Not necessarily as a literal breach or hack (though that’s also possible), but via the AI itself or its logging. There have been real-world examples: Samsung famously had to ban ChatGPT internally after engineers inadvertently pasted sensitive semiconductor code and notes into it. That data was then stored on OpenAI’s servers, beyond Samsung’s control.
• Unauthorized Training on Your Data: When you use a public AI, you might actually be feeding it new training data. Most AI providers reserve the right to use your inputs to improve their model. In fact, a Stanford study in 2025 found that all six leading AI companies (OpenAI, Google, Anthropic, Meta, Microsoft, and Amazon) by default use users’ chat inputs to train their models.
• Privacy Exposure and Compliance Violations: You might unknowingly violate laws like GDPR, HIPAA, or company policies by uploading certain data to an external service. Why? Because once the data leaves your secure environment, you’ve effectively transferred it to a third party.
• Competitors Gaining an Edge: This is a less obvious but very real risk, by using a public AI with your private data, you might be handing competitive insights to a shared model.
• Frequency of “Shadow AI” Use: Many employees (probably trying to be productive) are pasting company information into AI tools without approval. Surveys indicate roughly 30–38% of employees have uploaded sensitive corporate data to public AI tools like ChatGPT.

To sum up, public AI models are powerful and convenient, but using them with sensitive data is a bit like shouting your secrets in a room monitored by unknown observers.

Next, we’ll look at how Private AI solves these risks, essentially locking down your AI so you can harness it confidently.

How Private AI Keeps Your Secrets Safe

So, how does Private AI avoid the pitfalls we just discussed? The answer is that a private AI setup is built with data protection at its core. Several strategies and technologies combine to ensure that your data stays yours. Here are the key ways Private AI keeps your information safe from leaks and unauthorized training:

• Using Privacy First AI Chats: Okara, for example, offers privately hosted 20+ open-source AI models, has encryption-at-rest for your chats and data, and never trains AI models on your information.
• Self-Hosting (Your AI, Your Servers): One of the fundamental principles of Private AI is that you control where the AI runs. Instead of sending your data off to OpenAI or Google, you run the model on infrastructure you manage. This could be on your on-premises data center, a private cloud (in a Virtual Private Cloud isolated from others), or even a local machine.
• Edge Deployment (Keeping Data On-Site): A special case of self-hosting is deploying AI at the edge – which means running it as close as possible to where the data is generated or needed. This might mean on a factory floor device, on a hospital’s secure server, or on your personal laptop/phone for individual use. Edge AI has two big benefits: privacy and low latency. Because data doesn’t have to travel over the internet to a cloud, you not only avoid exposing it in transit, but you also get faster responses.
• Encryption at Every Layer: Okara (private AI chat platform) generates encryption keys on the client side – your browser/device – and never sees your raw key on their end. Your chat histories and AI conversation logs are stored in encrypted form, so even if someone somehow accessed the database, they’d just find scrambled text. Decryption only happens when you unlock it with your passcode, on your device. This means even the service provider can’t read your messages. In short, encryption ensures that no unauthorized party (or even the hosting service itself) can read your data without your consent.
• Open-Source Models and Transparency: Many Private AI approaches leverage open-source AI models (such as Llama, Mistral, or DeepSeek) specifically for the sake of transparency and control. Open-source models are by definition more transparent: their code and often even their training data are available for inspection.

Now that we’ve seen how Private AI locks down data, let’s explore who benefits the most from this approach and some real-world use cases.

Who Should Use Private AI? (Use Cases for Professionals)

Private AI isn’t just for big enterprises, it has very tangible benefits for a wide range of professionals and teams. Basically, anyone dealing with sensitive, proprietary, or regulated data should consider a Private AI approach. Let’s look at a few examples of who is embracing Private AI and why:

• Software Developers & Engineers: Developers often want to use AI coding assistants or chatbots to help with debugging, generating code, or reviewing algorithms. However, pasting company source code or confidential system logs into a public AI is risky (as the Samsung case showed). With Private AI, devs can safely use AI to analyze code within their secure environment.
• Researchers & Data Scientists: Researchers in fields from pharma to finance deal with extremely sensitive data (think drug discovery results, or market trading strategies). These professionals can’t risk uploading unpublished findings or trade secrets to ChatGPT. Private AI allows them to use powerful language models to, say, summarize research papers, draft reports, or even analyze experimental data, while keeping that data on internal servers.
• Journalists & Media Professionals: Investigative journalists and analysts frequently handle embargoed information, unreleased reports, or the identities of confidential sources. Such data is incredibly sensitive. Journalists have started using AI for tasks like summarizing long documents or transcribing/interview prep – but using a public AI raises the specter of those confidential details living on some corporation’s server. Private AI provides a solution: a reporter can have an AI writing assistant on their local machine or a secured server to help draft articles or analyze documents, without any risk of the scoop leaking out. For instance, imagine a journalist has thousands of pages of government records; a private AI could help summarize and highlight key points, and because it’s running in a closed environment, none of those records leak to the outside world. Media organizations are exploring private AI tools so that their fact-checking, summarizing, and content creation can be sped up by AI without compromising source protection or exclusivity.
• Legal Teams and Compliance Professionals: Lawyers and legal analysts work with confidential client information, contracts, case files, and often personal data protected by laws. They also have to worry about attorney-client privilege – if they use an external AI service, are they waiving privilege? With Private AI, law firms and in-house legal teams can safely use AI to streamline work.
• Product Teams and Businesses with Proprietary Data: Beyond specific professions, any business that has “crown jewels” data – proprietary strategies, algorithms, customer info – stands to gain from Private AI. Teams can build AI-powered tools (like chatbots or analytic engines) that leverage their unique data internally. For example, an e-commerce company might have a private AI chatbot trained on their product database and customer FAQs to assist support agents. By keeping it private, they ensure competitors can’t learn from their data and that no customer data leaks.

Next, let’s look, Okara.ai a real example of a private AI platform in action.

Meet Okara.ai, Private AI with Open-Source Models

Okara.ai is an example of a platform built for secure, private AI interactions, leveraging open-source models under the hood.

What is Okara?

It’s essentially a private AI workspace where you can chat with AI models (like you would with ChatGPT) and even generate images, but with the assurance that everything stays confidential. Okara lets you switch between over 20 models, including popular open-source large language models like Meta’s Llama (e.g., Llama 3), Mistral (a powerful 7B model), DeepSeek (known for coding and reasoning tasks), Qwen (another advanced model), GLM, and more – all in one interface. It also offers image generation using models like Stable Diffusion (e.g., Diffusion 3.5 Large) and Qwen Image.

Conclusion: Is Private AI Worth It?

For anyone concerned about the privacy and security of their data, the answer is a yes. Private AI allows you to leverage artificial intelligence without data leaks, compliance breaches, or helping your competitors. It’s the best of both worlds: you get AI’s efficiency and creativity, and you keep full control of your information.

In evaluating worth, consider the following:

• What’s the cost of a leak or violation? If the data you handle is sensitive (client data, intellectual property, etc.), even a small chance of exposure via a public AI could mean huge losses or penalties. Private AI largely eliminates that risk by design.
• Do you want to future-proof your AI usage? Private AI means you’re building internal capability and knowledge. You won’t be subject to sudden API price hikes or policy changes that might happen with third-party AI services. You can scale usage without escalating costs linearly.
• How important is trust for your stakeholders? Whether it’s customers, clients, or regulators, being able to say “we never send your data to an external AI” is a strong trust signal. In an era where data misuse headlines are common, that assurance can be priceless.
• Do you need specialized AI knowledge? If you have niche domain data, a private model fine-tuned on it can outperform generic models. That’s value added directly to your operations, with no one else reaping the spillover benefits.

In conclusion, Private AI is absolutely worth it for those who value their data. It’s a strategic investment in doing AI the right way: responsibly, securely, and in a manner tailored to your needs. Public AI tools have their place for generic tasks and public info, but when it comes to your codebase, confidential documents, or customer data, Private AI is the path forward. With solutions like Okara.ai and a vibrant open-source model ecosystem (Llama, Mistral, DeepSeek, and more), making the switch is easier than ever.

Further Read -- A Beginner's Guide to Open Source Models