The Power of a Private GPT

Guide to harnessing AI for company knowledge management with security and privacy.

If you are reading this guide, you likely:

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Use artificial intelligence as an early adopter in your work and daily life to help you become more productive, inspire, and organize.
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Believe that your company could benefit from the power of AI and have several good ideas on how to do it.
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Don't know where to start. You're exploring options and find little information on the subject.
If you meet at least two of the three criteria, this guide can help you. It is a living guide that we will build and complete based on our practice building private GPTs and the advances that will happen in the field.
1   What is a PrivateGPT, PrivateLLM, or whatever it's called?

A PrivateGPT (or PrivateLLM) is a language model developed and/or customized for use within a specific organization with the information and knowledge it possesses and exclusively for the users of that organization.

This definition contrasts with PublicGPT, which is a general-purpose model open to everyone and intended to encompass as much knowledge as possible.

These two general definitions provide a starting point. But as we progress, we will see that there can be many nuances, as we can have public GPTs used by organizations but on private servers or have some kind of proxy solution that protects your information somehow.

We will see all this later when we delve deeper into the topic.

2   What are the elements of a private GPT?

To align everyone, we present here a basic glossary of important concepts regarding AI, security, and privacy:

  • Large Language Model (LLM): A Large Language Model (LLM) is an advanced AI tool that streamlines your text-based tasks. Leveraging machine learning algorithms, it's adept at understanding and generating text that's remarkably human-like. It is a formidable asset for your business, from answering queries to writing comprehensive reports, translating languages, or even generating creative content.
    Consider the LLM as your high-performing textual processor. It takes in information, processes it, and delivers meaningful and relevant output. What's more, these models are flexible to your needs. For tasks that do not demand high-level privacy, such as drafting a blog post, a public model like GPT-4 excels with its speed and power. However, a privately hosted LLM ensures optimal data privacy and security when handling sensitive information—such as summarizing a patient's medical history.
  • Company knowledge:  We call “company knowledge” all the accumulated data from every corner of the organization. The data can include documents, emails, databases, and other unstructured and structured information types. The GPT aims to learn from this data and generate useful, accurate responses or insights.
  • Servers: This refers to the physical or virtual infrastructure hosted by the private GPT model. Choosing between on-premises and cloud servers can have significant implications for cost, performance, and data privacy.
  • Privacy: Refers to the practices and technologies used to ensure that sensitive data is not exposed or accessed without authorization. In the context of a private GPT, privacy considerations include how the model is trained and how queries to the model are handled.
  • Security: This involves protecting the private GPT and its underlying systems from threats, including unauthorized access, data breaches, and other forms of cyberattacks. Security considerations for a private GPT include data encryption, user authentication, access control, and regular security audits.
3   What can be done with a PrivateGPT?

The applications of a PrivateGPT are the same time, general and specific, and they will constantly be defined and redefined. This may sound paradoxical, but these models behave very differently and provide different value when dealing with general tasks vs. when they have to be specific and concrete.

We have grouped them into 4 general categories:

  • Enhanced Knowledge Management: Private LLMs can automatically sift through and organize vast quantities of unstructured data, enabling employees to access crucial insights more quickly and make informed decisions.
  • Improved Customer Experience: By leveraging natural language processing (NLP), private LLMs can handle customer queries and support requests, providing personalized and contextually relevant responses.
  • Accelerated Innovation: LLMs can assist in generating creative ideas and identifying potential growth opportunities, empowering organizations to stay ahead in competitive markets.
  • Productivity Boost: LLMs can be introduced into employees' workflows to significantly increase the speed of delivery while they enjoy less stressful work environments.
4   What privacy or security considerations should be taken into account?

Despite the tremendous potential of private LLMs, companies face significant challenges to adoption when it comes to data privacy and security. The main challenges that corporations encounter when adopting LLMs include the following:

  • Data Access Control: It's crucial to prevent unauthorized individuals from retrieving sensitive information when querying the LLM. This can be achieved by excluding real data from the training dataset and conducting semantic searches using embeddings, coupled with robust and detailed access control policies.
  • Third-Party LLM API Providers: Transmitting confidential data to an LLM API provider carries inherent risks. While service level agreements (SLAs) may offer sufficient protection in some circumstances, there are scenarios, especially those involving highly sensitive data, where data transmission outside the company's virtual private network (VPN) isn't just impractical but could be deemed illegal or a breach of specific data compliance regulations. In such cases, utilizing open-source, on-premises LLMs may be the only feasible solution.
  • Data Encryption: It's crucial to safeguard all data, whether it's stored or transmitted, with robust encryption methods. This will prevent unauthorized access and changes to the data while it's stored or during transmission.
  • Regular Audits: Routine audits and evaluations are necessary to spot potential vulnerabilities and enhance security measures on an ongoing basis. This will help maintain strong security and stay ahead of the evolving threats.
  • Compliance with Regulations: It's imperative that the deployment of private LLMs complies with all applicable data protection laws and regulations. This includes laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), as well as industry-specific standards like the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry Data Security Standard (PCI-DSS).
5   How can I implement a PrivateGPT in my company?

At this point, you will know that we can only give you one answer: IT DEPENDS.

There are many factors that influence the development and complexity of a project of this nature. First, it depends on the type of LLM you can use, whether you can work with third-party models (more powerful, but requires data to be sent to their API) or you must use an Open Source one (rock-solid privacy, as it can operate in your data center, but less powerful). It will also depend on your company's databases, knowledge base, and accessibility. Finally, it will depend on your privacy and security needs and criteria.

If you're looking for a self-service product, tailored to your sector, with everything you need ready to use, you'll have to sit and wait. However, there are many projects and technologies that are already developed and ready to use, so developing a custom solution has an acceptable development time and can evolve easily and quickly.

With this in mind, when looking to implement a privateGPT solution, there are three scenarios:

  • A self-service product that satisfies your needs: As we've seen, this option is still limited. Many digital products have incorporated Artificial Intelligence elements into their features, but it's hard to find products that cover different sources of knowledge and do it smoothly and securely tailored to each use case. There are very specific use cases, but they are exceptions for now.
  • Develop your solution internally: there are very interesting open-source initiatives and products that are gaining ground. There are also organizations that can provide support to your internal developers in the implementation of their products. But there are some limitations. The main one is that you need to have a strong technical team and specialized in Artificial Intelligence. The world of low-code and no-code is far from being able to cope with complex corporate use cases and advanced security and privacy.
  • Work with a development partner: The last option is a hybrid, and it's the one we're carrying out with our clients. We treat PrivateGPT as a hybrid between service and product. We develop custom solutions, leveraging all the accumulated knowledge, use cases, and projects and products we're developing. All of this accelerates the definition, development, and implementation process exponentially. There are weeks between the definition and the first usable prototypes. From there, there will be a period of iteration and adjustment. But these are quite short horizons.

If you want us to collaborate with your company, please get in touch with us through the application form below, and we will contact you back.

If you want to know more about initiatives, projects, or innovations, ours or from the market, we share a lot of content in our social networks or AI innovation newsletter.

If you have any other questions, comments, or suggestions, you can find out how to contact us on our website.

Check AI Innovation newsletter

What is a privateGPT, PrivateLLM, or whatever it's called?

A private GPT (or PrivateLLM) is a language model developed and/or customized for use within a specific organization with the information and knowledge it possesses and exclusively for the users of that organization.

This definition contrasts with PublicGPT, which is a general-purpose model open to everyone and intended to encompass as much knowledge as possible.

These two general definitions provide a general starting point. But as we progress, we will see that there can be many nuances, as we can have public GPTs used by organizations but on private servers or have some kind of proxy solution that protects your information somehow.

We will see all this later when we delve deeper into the topic.

Elements of a private GPT

To align everyone, we present a basic glossary of what we will take into consideration when explaining during the guide:

Large Language Model (LLM): A Large Language Model (LLM) is an advanced AI tool that streamlines your text-based tasks. Leveraging machine learning algorithms, it's adept at understanding and generating text that's remarkably human-like. It is a formidable asset for your business, from answering queries to writing comprehensive reports, translating languages, or even generating creative content.

Consider the LLM as your high-performing textual processor. It takes in information, processes it, and delivers meaningful and relevant output. What's more, these models are flexible to your needs. For tasks that do not demand high-level privacy, such as drafting a blog post, a public model like GPT-4 excels with its speed and power. However, a privately hosted LLM ensures optimal data privacy and security when handling sensitive information—such as summarizing a patient's medical history.

Company knowledge:  We call “company knowledge” all the accumulated data from every corner of the organization. The data can include documents, emails, databases, and other unstructured and structured information types. The GPT aims to learn from this data and generate useful, accurate responses or insights.

Servers: This refers to the physical or virtual infrastructure hosted by the private GPT model. Choosing between on-premises and cloud servers can have significant implications for cost, performance, and data privacy.

Privacy: Refers to the practices and technologies used to ensure that sensitive data is not exposed or accessed without authorization. In the context of a private GPT, privacy considerations include how the model is trained and how queries to the model are handled.

Security: This involves protecting the private GPT and its underlying systems from threats, including unauthorized access, data breaches, and other forms of cyberattacks. Security considerations for a private GPT include data encryption, user authentication, access control, and regular security audits.

What can be done with a Private GPT?

The applications of a private GPT are both general and specific, and they will constantly be defined and redefined. We have grouped them into 4 general blocks that summarize them well.

1. Enhanced Knowledge Management: Private LLMs can automatically sift through and organize vast quantities of unstructured data, enabling employees to access crucial insights more quickly and make informed decisions.

2. Improved Customer Experience: By leveraging natural language processing (NLP), private LLMs can handle customer queries and support requests, providing personalized and contextually relevant responses.

3. Accelerated Innovation: LLMs can assist in generating creative ideas and identifying potential growth opportunities, empowering organizations to stay ahead in competitive markets.

4. Productivity Boost: LLMs can be introduced into employees' workflows to significantly increase the speed of delivery while they enjoy less stressful work environments.

What privacy or security considerations should be taken into account?

Despite the tremendous potential of private LLMs, companies face significant challenges to adoption when it comes to data privacy and security. The main challenges that corporations encounter when adopting LLMs include the following:

1. Data Access Control: It's crucial to prevent unauthorized individuals from retrieving sensitive information when querying the LLM. This can be achieved by excluding real data from the training dataset and conducting semantic searches using embeddings, coupled with robust and detailed access control policies.

2. Third-Party LLM API Providers: Transmitting confidential data to an LLM API provider carries inherent risks. While service level agreements (SLAs) may offer sufficient protection in some circumstances, there are scenarios, especially those involving highly sensitive data, where data transmission outside the company's virtual private network (VPN) isn't just impractical but could be deemed illegal or a breach of specific data compliance regulations. In such cases, utilizing open-source, on-premises LLMs may be the only feasible solution.

3. Data Encryption: It's crucial to safeguard all data, whether it's stored or being transmitted, with robust encryption methods. This will prevent unauthorized access and changes to the data while it's stored or during transmission.

4. Regular Audits: Routine audits and evaluations are necessary to spot potential vulnerabilities and enhance security measures on an ongoing basis. This will help maintain strong security and stay ahead of the evolving threats.

5. Compliance with Regulations: It's imperative that the deployment of private LLMs complies with all applicable data protection laws and regulations. This includes laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), as well as industry-specific standards like the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry Data Security Standard (PCI-DSS).

How can I implement a private GPT in my company?

If you've been paying attention up to this point, you'll know that we will give you one answer: IT DEPENDS.

Everything will depend on the type of LLM you want to use, whether you can work with third-party models or want to use an Open Source one. It will also depend on your company's knowledge and its accessibility, as well as your privacy and security needs and criteria.

If you're looking for a closed product, adapted to your industry, and with all the criteria you need to be implemented, you'll have to sit and wait.

But at the same time, powerful solutions can be developed today.

The most important thing is to have the right partner and ask the right questions.

We are already developing Private GPTs for various institutions and corporations worldwide, but maybe we are not the right fit for your project.

That’s why, we have developed a questionnaire to guide you in the type of questions you need to ask yourself and to find your right partner in this adventure.

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