Private Large Language Models (LLMs): Security and Control Over Your Generative AI Workloads

Choose a private large language model (LLM) to ensure data security and governance, tailor user experience, and refine input and output precision — all within your company's domain. You'll have the exclusive control you need, from crafting prompts to integrating the LLM into your custom apps. Keep your data proprietary and your model's insights internal to your enterprise. In this blog, I’ll dive into what you need to know about private LLMs and how to get started.

Open-source initiatives and the rise of serverless architectures have transformed private LLMs into practical tools for businesses. These advancements not only offer a robust framework for data privacy and security but also adapt seamlessly to the specific challenges and unique needs of any organization, lowering barriers to entry and making AI more accessible than ever.

In this blog, we explore:

• What are Private LLMs and How Do They Differ from Public LLMs?
• What are the Benefits of using Private LLMs and the Risks of Not Doing So
• What are the Applications for Private LLMs?
• Private LLMs in Practice: A Case Study
• What are the Steps to Utilizing a Private LLM?

What are Private LLMs and How Do They Differ from Public LLMs?

Large Language Models (LLMs) — such as GPT-3.5 or GPT-4 used by ChatGPT — have transformed how we think about and interact with AI, offering conversational and content creation capabilities. These models can be categorized into two main types: private and public LLMs, each serving different needs and offering distinct advantages.

With private LLMs, you get an exclusive version of a model for use within your organization’s environment. This gives you control over the LLM and how it behaves. Additionally, every input to and output from the LLM remains securely within your organization’s boundaries, ensuring that sensitive information never leaves your corporate environment.

In contrast, public LLMs — such as a GPT hosted by OpenAI — are hosted by a third party outside of an organization’s environment and available to anyone. The upside is that you do not have to manage or maintain the LLM environment, but this openness requires you to send all queries — including potentially sensitive corporate data and intellectual property — to be processed outside your organization’s environment. This approach raises significant concerns about privacy and the ownership of the data exchanged with the public LLM.

Key considerations for adopting an LLM solution

When adopting an LLM solution, security, control, and cost are the primary considerations. These three things determine whether you choose a Commercial Off-the-Shelf (COTS) application or a custom application and whether you use a public or private LLM.

For COTS products, like ChatGPT or Microsoft Copilot, the main advantage is their immediate availability and swift deployment capabilities. With these, your investment is primarily in licensing fees. However, you have limited control over the application’s behavior and features.

Custom applications, in contrast, are designed specifically for your organization, offering full customization. Here, your investment is in development and maintenance, which may take more time initially but provides you with greater control over the application’s behavior.

When it comes to choosing between a public LLM and a private LLM, the decision often rests on your organization’s requirements for data privacy and the desired level of control over the model. While costs may be similar, the trade-offs between the two options are significant.

To illustrate these points, let’s consider how GPT, a popular LLM, is commonly interacted with:

• Through ChatGPT (COTS app + public LLM): Accessible to anyone, this setup is straightforward but offers little-to-no customization and has data privacy concerns.
• Through Microsoft Copilot (COTS app + private LLM): Corporate users can interact with some versions of Microsoft Copilot with confidence that data will remain within their corporate ecosystem. However, there are little-to-no customization possibilities.
• Through custom applications that use GPT hosted by OpenAI (Custom app + public LLM):This approach provides maximum app customization but little control over the LLM’s behavior. Data privacy is also a concern.
• Through custom applications that integrate GPT via a private instance hosted by Microsoft Azure (Custom app + private LLM): This setup combines the benefits of a custom application with the privacy, security, and control of a private LLM.

Whether opting for the ease and speed of COTS applications or the tailored approach of custom apps, and whether choosing the widespread accessibility of public LLMs or the secure exclusivity of private LLMs, it’s a strategic decision that will shape your organization’s interaction with AI technologies.

What are the Benefits of Using Private LLMs and the Risks of Not Doing So?

The shift toward serverless computing and usage-based pricing has transformed private LLMs into a financially viable and strategically flexible option for businesses.

This model not only circumvents the prohibitive costs associated with traditional AI implementations but also mitigates the risk of vendor lock-in, ensuring your business can evolve with technological advances and maintain your competitive edge.

Benefits of private LLMs include:

• Enhanced Data Privacy and Security: Private LLMs provide robust data protection, hosting models within your organization’s secure infrastructure. Data never leaves your environment. This is vital for sectors like healthcare and finance, where sensitive information demands stringent protection and access controls. In contrast, sensitive data must leave your corporate environment to operate with a public LLM, posing severe security concerns.
• Compliance with Regulations: In many industries and geographies, strict regulations govern the storage, movement, and processing of data. By using a private LLM, you ensure that your data never leaves your environment and is processed in the correct locale to comply with these regulations. For industries subject to laws like GDPR or HIPAA, using a public LLM poses the risk of legal repercussions, fines, and reputational damage from non-compliance.
• Customization and Competitive Advantage: Unlike one-size-fits-all public models, private LLMs can be fine-tuned to meet specific business needs, understanding and generating text that aligns with unique industry jargon and processes. This bespoke approach not only ensures more accurate and efficient AI applications but also carves out a competitive edge by offering innovative services that are not feasible with generic AI tools. Failing to leverage such customization can result in inefficiencies and place your organization at a disadvantage as competitors harness tailored AI solutions to enhance operations and customer experiences.

Adopting a private LLM represents not just an advancement in technology but a strategic move to safeguard sensitive corporate data and intellectual property, streamline operations, and maintain a competitive edge. – Patrick Vinton

What are the Applications for Private LLMs?

Understanding the strategic advantage of a private LLM is crucial, especially for safeguarding sensitive corporate data and intellectual property. While both private and public LLMs provide robust frameworks for leveraging AI’s power across various applications, private LLMs distinguish themselves by offering enhanced security, customization, and the ability to leverage proprietary data effectively.

Here are key areas where private LLMs are a better choice to public LLMs:

• Personalized Experiences: Private LLMs enable organizations to deliver exceptionally personalized services and recommendations to both customers and employees. By analyzing user preferences and history within a secure framework, these models facilitate communications that are relevant, engaging, and use consistent industry and organizational vernacular.
• Secure Data Processing: Where data sensitivity is paramount, private LLMs offer a secure environment for data analysis and processing. This ensures adherence to compliance standards while protecting against data breaches and unauthorized access, a critical advantage in handling sensitive data and intellectual property.
• Innovative Product Development: Private LLMs empower companies to push the boundaries of creativity and innovation. By leveraging corporate data and assets, these models support the development of groundbreaking products and content, enabling organizations to maintain a competitive edge in their respective markets.
• Efficient Operational Automation: From automating administrative tasks like scheduling and email management to more complex operations such as supply chain optimization, private LLMs can significantly increase efficiency.

Opting for a private LLM not only capitalizes on the benefits of AI but does so with an unparalleled level of security and alignment with your organization’s strategic goals. By choosing a private model, you integrate AI deeply into your operations, ensuring that every facet of your business benefits from insights and efficiencies that are uniquely tailored and securely managed.

Private LLMs in Practice: A Case Study??

Enhancing Operational Efficiency and Personalization at Analytics8 with Generative AI

Analytics8 embraced generative AI and private LLMs to optimize internal operations and foster a consistent but personalized customer experience. Many initiatives focused on deploying AI-driven solutions across various facets of the organization, including:

• HR Assistant: Implementing a chatbot equipped with comprehensive knowledge of all employee policies, guidelines, and benefits to promptly address inquiries. This enables Analytics8 to manage its expanding workforce without proportionally increasing its HR department.
• Personalized Training and Onboarding: Utilizing AI to tailor the assimilation process for new hires, making institutional knowledge, delivery methodologies, and technical resources readily accessible.
• Enhanced Sales Support: Implementing generative AI assistants to aid sales teams by customizing and personalizing interactions with clients consistent with our core messaging and value proposition.
• Streamlined Contract Management: Employing AI for contract generation and review, ensuring consistency and efficiency by minimizing manual parts of the process.

Analytics8’s strategic integration of private LLMs not only streamlined internal processes but also established new standards for operational efficiency and client engagement, showcasing the impact of advanced technology on modern business practices.

What are the Steps to Utilizing a Private LLM?

To effectively utilize a private LLM in your organization, a structured approach is essential. These steps emphasize not only the strategic requirements but also the technical groundwork necessary for successful implementation.

1. Identify Use Case: Begin by clearly defining the problem or opportunity your organization aims to address with a private LLM. This critical first step involves understanding your stakeholders’ needs and how a private LLM can meet those needs more effectively than other solutions, whether it’s for enhancing customer service, streamlining internal processes, or generating unique content.
2. Determine Hosting: Deciding where to host your private LLM affects performance, cost, and scalability. Hosting options range from setting up your own servers to leveraging cloud-based services provided by hyperscalers like AWS , Azure , or GCP . These platforms often offer serverless computing options, allowing you to run your LLM without the operational complexities of managing servers while providing flexibility and cost efficiency.
3. Technical Specifications and Requirements: As you consider hosting, it’s also crucial to prepare your infrastructure to support the demands of a private LLM:

• Computing power: Ensure your setup has the computing resources needed for training and inference tasks.
• Storage capacity: Verify your systems can securely and efficiently store the large volumes of data used and generated by the LLM.
• Security measures: Implement robust security protocols, including encryption and access controls, to protect sensitive information.
• Integration capabilities: Make sure your existing systems and applications can seamlessly integrate with the LLM, using compatible APIs, data formats, and communication protocols.
• Maintenance and monitoring tools: Plan for ongoing monitoring and maintenance with the right tools for logging, performance tracking, and troubleshooting.
• Compliance and regulatory adherence: Ensure your technical setup adheres to relevant data protection, privacy regulations, and corporate data governance policies.

1. Prototype: Create a preliminary version of your solution to test the feasibility and effectiveness of the private LLM in addressing your identified use case. Prototyping is essential for gathering feedback, setting expectations, making adjustments, and refining your approach.
2. Build Application: The final step is to fully integrate the private LLM into your environment and making it production ready. This includes developing the user interface (if applicable), implementing the security measures identified earlier, and ensuring the application complies with relevant data protection regulations.

Successfully utilizing a private LLM combines technical expertise with strategic planning and continuous evaluation, ensuring that the solution remains aligned with your organization’s evolving needs and objectives.

This article was originally published in Analytics8.com and authored by Patrick Vinton .