A Guide to Understanding Private AI: Securely Managing Corporate Data

As businesses increasingly recognize the potential of artificial intelligence (AI), the focus has shifted towards Private AI—a solution designed to work securely with corporate data while respecting privacy and data governance protocols. This guide explores the nuances of Private AI, including private installations of large language models (LLMs), Retrieval-Augmented Generation (RAG), and fine-tuning models.

Understanding Private AI

Private AI, or private artificial intelligence, has become a pivotal concept for tech companies in recent times. This guide will demystify Private AI and explain how it contrasts with public AI models.

Public AI models, such as those powering ChatGPT, Microsoft Copilot, and Google Gemini, owe their rapid adoption to their versatility and accessibility. These generalist models are trained on vast public datasets, making them applicable across diverse scenarios.

Many enterprises leverage these models to enhance productivity by generating documents, developing software, and organizing information. According to Goldman Sachs, generative AI could boost global GDP by 7%, with The European House Ambrosetti predicting up to an 18% increase in Italy’s GDP. Ignoring this AI revolution could jeopardize a company’s competitiveness. However, using generalist public models often falls short for enterprises, presenting significant risks.

Why Companies Need Private AI

Companies need Private AI for four main reasons:

1. Reliable Answers from Verified Information: Ensuring responses are based on accurate, company-specific data.
2. Confidentiality of Proprietary and Sensitive Information: Keeping sensitive corporate data private.
3. Integration into Corporate Software and Processes: Seamlessly embedding generative AI functionalities.
4. Service Continuity and Performance Guarantees: Assuring consistent performance and service availability.

Private AI and Grounding on Corporate Data

For businesses, the accuracy and reliability of AI responses are paramount. Public LLMs are trained on a wide array of public internet data, lacking knowledge of company-specific manuals, processes, and financials. Consequently, these models can generate errors, fabricate information, or produce nonsensical outputs (known as AI hallucinations).

Businesses need AI that provides grounded responses based on verified sources. This grounding ensures the AI’s feet are firmly on the ground, offering reliable information drawn from trusted company data.

Protecting Confidential and Personal Information

Many public AI models’ terms of use allow researchers to analyze user interactions to enhance the models. This, coupled with potential security breaches, poses a risk of exposing sensitive information to unauthorized parties.

From a data governance and privacy law perspective, public LLMs should be treated as external cloud services not controlled by the company, increasing the risk of unintentional disclosure of trade secrets or personal information.

Generative AI in Corporate Software

Integrating AI into business workflows goes beyond simple chatbot interactions. For instance, having customer interaction summaries integrated into a CRM during a call or an automated dashboard identifying problematic social media comments exemplifies practical AI applications.

Although public model APIs enable such integrations, they come with limitations and unpredictabilities, such as usage caps, latency issues, and service availability concerns. Additionally, changes in API usage policies or technical specifications by providers can disrupt corporate software functionalities.

Three Approaches to Implementing Private AI in Business

To address these challenges, businesses can adopt Private AI, ensuring proprietary information remains confidential and allowing for detailed control over functionality, updates, and service availability. Depending on goals and resources, companies can choose from three approaches:

1. Private Installation of a Generalist Model

Several powerful open-source LLMs are available, supported by major companies ensuring reliability and updates. Notable examples include Meta’s Llama-2, Mistral, Falcon LLM, and xAI’s Grok. These models can be installed and configured on a company’s infrastructure using tools like Ollama, providing a private and secure AI solution.

2. RAG: Enhancing Private AI with Corporate Data

Retrieval-Augmented Generation (RAG) connects LLMs to corporate data sources, enabling the model to provide responses based on specific company documents and databases. This involves converting user requests into queries that search corporate data, with the results informing the AI’s response. RAG can also access public data sources, making it a powerful tool for quickly synthesizing and analyzing dispersed information.

3. Fine-Tuning a Model

When retrieving information isn’t enough, fine-tuning allows the model to acquire expertise in a specific domain or modify its response style by exposing it to new training data. This process, although computationally intensive, results in a model capable of providing precise, context-aware responses without constant external data queries. Fine-tuned models have static knowledge from their training period but can be combined with RAG for dynamic information needs.

Necessary Infrastructure for Private AI

Implementing Private AI requires a robust infrastructure capable of supporting model training, data storage, and secure access. Companies must invest in powerful computing resources, data management systems, and secure networks to ensure optimal performance and security of their Private AI solutions.

Private AI represents a significant advancement for enterprises looking to harness the power of AI while maintaining control over their data. By choosing the right approach—be it private installations, RAG, or fine-tuning—businesses can achieve secure, reliable, and efficient AI integration tailored to their specific needs.