Understanding AI Models

What you need to know:

• ‍Public AI: When you use public AI the information you input will be used to train and improve the models. This presents a risk for businesses. Proprietary data, trade secrets, and confidential information could be exposed and used to train a model accessible to others, including competitors. ‍
• Private AI: Since the model is trained and deployed in a secure, closed environment, your data never leaves your control. This keeps sensitive information confidential and allows the AI to learn solely from your organization's internal data, turning it into a competitive advantage without compromising security. ‍
• Embedded AI: These are AI tools built directly into the applications you already use. Before embracing the convenience they offer, you must ask a crucial question: Do you understand the privacy policy and data-sharing practices of these tools?

Understanding AI Models

The headlines about AI are unavoidable these days. From the rise of tools like ChatGPT and Gemini to the growing concern about their use, artificial intelligence is reshaping our world. But beyond the public eye, AI is quietly being integrated into the technology we use every day, changing how we work, learn, and interact with information. Understanding the difference between public AI, private AI, and embedded AI is crucial to navigating this new landscape. Each has distinct advantages and risks and recognizing them empowers you to make smarter decisions about how you use these powerful tools.

Public AI

Public AI refers to AI systems that are widely accessible and available to anyone. They are often trained on vast amounts of public data and are designed for a broad range of uses. The data you input into these tools might be used to further train their models or it could be exposed to other users. ChatGPT, Gemini, and Perplexity are some well-known examples of public AI.

Why Use Public AI

Publicly available AI tools are readily available and easy to use, they are not expensive to implement, and they are consistently innovating. And let’s face it, they can be fun to play with.

The primary advantage of public AI is accessibility. This accessibility eliminates the need for complex setup and specialized technical knowledge. Additionally, public AI models are often available for free or with low-cost subscriptions.  

Another major benefit is the sheer scale and power of these models. Public AI models are trained on vast, diverse datasets, often curated from the entire internet. The continuous feedback from a large user base also helps these models improve over time, leading to more accurate and reliable results. Users can consistently benefit from the latest advancements without any additional effort or investment.

Risks of Public AI

Public AI presents significant challenges related to privacy and control. User data and prompts are often sent to the cloud for processing, raising concerns about data security and confidentiality. While service providers typically have privacy policies in place, the risk of data breaches or the potential for data misuse remains a concern.  

Users also have limited control over the model's behavior and performance. They are subject to the service provider's terms of use, pricing structures, and algorithmic biases. The black box nature of these models means users cannot inspect or modify the underlying code. Limiting their ability to customize the AI's output or understand why it behaves in a certain way.

Private AI

Private AI is an AI system that is developed and operated within a secure, controlled environment, typically by a single organization. The primary goal is to protect proprietary and sensitive data. These systems are trained on an organization's unique, internal data, ensuring that information never leaves its secure environment.

An example is Microsoft 365 Copilot, which can be configured as a private AI. It is designed to use your company's data, like documents in SharePoint or emails in Outlook, without exposing that information to the public models. This ensures that your confidential information remains secure.

Why Use Private AI

Private AI is characterized by a high degree of customization, privacy, and control.

One of the most significant advantages of private AI is data privacy. Since the AI operates on a local machine and is not connected to a public network, data used for training and inference remains secure and is not shared with third parties. This is particularly appealing for sensitive applications like personal health monitoring, and financial management.  

Private AI models can be customized to a user’s specific preferences, biases, and data patterns. The user also has the freedom to experiment with different algorithms and parameters without facing the constraints of a public API or the costs associated with cloud computing.

Risks of Private AI

While a private AI environment keeps data within your company's network, it isn't automatically safe. The biggest risks come from failing to manage access controls and user permissions. This can lead to sensitive information being accidentally exposed.

• Risk of Accidental Access: An AI with broad access can easily bypass your existing security. An intern could ask the AI to "summarize company finances" and get confidential data they could not access on their own.  ‍
• Risk of Bypassing Security: If your private AI is not configured with role-based access control (RBAC), it might have permissions that are too broad.  ‍
• Risk of No Visibility: Without a strong policy requiring logging and auditing, you'll have no idea who is accessing what data through the AI

To make a private AI environment truly secure, you must build a strong policy that restricts what data the AI can access based on the user's permissions, not just on whether the data is in the company network.

Another Way AI is in your Data

Beyond AI we think about with ChatGPT or internal systems, a more subtle trend is the embedding of AI capabilities within everyday third-party productivity tools. Note-taking apps, scheduling assistants, project management software, and even word processors are becoming intelligent partners that anticipate user needs and automate tedious tasks.  

Benefits of Embedded AI

The most immediate benefit of this trend is a boost in productivity. AI-powered note-taking apps can automatically join virtual meetings, transcribe the conversation in real-time, and generate a summary of key discussion points. They can even identify action items and assign them to specific people, all without a single keystroke from a human participant. This allows meeting attendees to focus entirely on the conversation, rather than splitting their attention between listening and typing.  

This integration also provides a level of contextual intelligence that was previously unimaginable. For example, a note-taking apps use ai to connect related ideas and documents, helping users see patterns and make connections they might have missed. An AI-powered project management tool can analyze a team's tasks and deadlines to proactively flag potential bottlenecks or suggest a more efficient workflow. By acting as a digital memory and a proactive assistant, these tools help users transcend the limitations of their own cognitive load.

Risks of Embedded AI

The integration of AI comes with its own set of challenges, particularly regarding privacy and data security. When a user grants an AI note-taker access to a meeting, they are giving a third-party service access to a potentially sensitive conversation. While many companies have good privacy policies, the risk of data breaches or the potential for a third party to use this information to train its own models remains a significant concern.  

The Future of AI

As AI becomes more sophisticated and easier to embed, we can expect to see an even deeper integration into the tools we use daily. This will lead to even greater efficiency gains and new levels of personalized assistance. However, it also demands a more critical eye from both users and developers regarding the trade-offs between convenience and control, and the importance of data privacy in a world where AI is no longer a separate application, but an invisible partner in every digital action.

The future of business and personal productivity is inextricably linked with the strategic adoption of AI.

We encourage you to reflect on your current use of these technologies and consider the potential for deeper integration within your organization.

If you are interested in learning more about how you can integrate AI into your business, book a meeting with one of our IT Experts.

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