Author: Brian Letort, Head of Data Office and Platform AI, Digital Realty

Today’s business leaders are seeking enterprise artificial intelligence (AI) solutions that will increase operational efficiency, cut costs, improve strategic decision-making, and support customer engagement.

Gartner estimates that by 2026, over 80% of enterprises will have used generative AI (GenAI) through application programming interfaces (APIs) or by deploying applications — a significant increase from fewer than 5% in 2023. Yet, with today's rising concerns around data privacy, enterprises need AI solutions to keep their customer and company data and assets safe.

Private AI offers this solution. Here's what private AI is, why it benefits organizations concerned about data privacy, and how IT leaders can maximize their private AI adoption.

What is private AI?

Today's popular GenAI models, like ChatGPT and Copilot, can help enterprises accomplish many tasks, such as analyzing data, interacting with customers, powering output-matching applications, and more. 

However, these AI models lack transparency into where a user's data goes or how that data is used, which sparks privacy concerns. Eager business leaders using GenAI models might be unaware that private, sensitive information could be shared with third parties or used in AI training.

 But enterprise leaders now have the option to use private AI architecture, which restricts queries and requests to a company's internal database, SharePoint, API, or other private sources. This approach often incorporates Retrieval-Augmented Generation (RAG) to securely interact with large public models and provides organization-specific, informed responses.

With private AI, organizations can utilize the benefits of AI and large language models (LLMs) while ensuring the safety of their input data.

Private AI vs. public AI

We’ve reached a tipping point where AI is advanced enough for widespread adoption, and its increasing democratization is transforming both business and society. But we must look past what an AI model can do to how it’s trained, where its data comes from, and where that data lives.

Today's public GenAI models have been trained on public data sets — for example, GPT-3 was trained on 45 terabytes of data from the CommonCrawl dataset.¹ They also continue to be trained on new data that users submit into the models.

This poses a challenge for companies who want to roll out AI initiatives but are concerned about keeping their IP and customer data private. Information input into a public AI model is then stored in a third-party database and made available to third-party providers. Business leaders believe that the biggest risk of adopting GenAI is inaccuracy (56%), but secondary to that is cybersecurity risk (53%), IP infringement risk (46%), and personal or individual privacy risk (39%).

Organizations that understand the value of keeping data safe are looking at solutions that ensure privacy, control, and efficiency — one of which is private AI models.

How retrieval-augmented generation (RAG) facilitates private AI

Adopting private AI doesn’t mean that an organization must build its own in-house ChatGPT and the foundational LLMs from scratch to keep its data private. RAG is designed to maintain security, compliance, privacy, and data sovereignty while using AI. 

Instead of querying public data sets, an AI model first queries a company’s internal databases, document libraries, and other system information— like querying a company’s intranet. Once the AI retrieves those results, it then appends them to the query and sends it to the public model. With RAG, organizations can secure their data and keep it from being used in a public manner while also receiving robust responses from complex public LLMs.

Organizations can take this one step further by appending their own organizational data to a public AI model and fine-tuning the model with their company's specific data. Of course, this will be more expensive and complex than RAG, yet it is likely an option we’ll see enterprises take in the future.

Why enterprises today should adopt private AI

Private AI can offer several benefits for IT leaders who are investigating new enterprise AI solutions, including:

Data privacy: Private AI’s biggest benefit is data privacy, as it offers organizations who prioritize keeping sensitive information safe a way to leverage the power of AI.
Control: Private AI offers increased control over how enterprise data is used, how it’s accessed, and who accesses it. Instead of sending internal data into public models with the potential for exposure, organizations can keep control over their data, with guardrails up where needed.
Speed and ease: By using private AI, organizations can easily query internal backend systems — databases, customer relationship management (CRM), and enterprise resource planning (ERP) — through a natural language interface, gaining insights and answers from their data quickly and efficiently through automated processes.
Regulatory compliance: By controlling where data is and how it’s used, organizations can ensure that they’re keeping compliant with privacy laws and regulations, like the Health Insurance Portability and Accountability Act (HIPAA), California Consumer Privacy Act (CCPA), and General Data Protection Regulation (GDPR).
Customer trust: Considering that 85% of consumers want to know a company’s data privacy policies before making a purchase and 46% will consider changing brands if a company is unclear about how it uses customer data, organizations can increase customer trust by increasing their data privacy through private AI.
Competitive advantage: As organizations build their AI capabilities, they can gain a competitive advantage by fostering innovation and building customer trust. Creating domain-specific large language models trained on internal data can serve as "knowledge hubs," further enhancing this advantage.

How enterprises can launch and scale private AI

Creating the best environment in which a private AI infrastructure can thrive comes with some preparation and awareness, including the following first steps:

Start with data quality: IT leaders can start by ensuring the quality of their enterprise data because AI needs good data to run efficiently. Data should be clean, consistently organized, and as fresh and near-real-time as possible. This may mean having to implement new data governance and hygiene practices.
Create the right environment: When preparing their technological infrastructure for AI adoption, 59% of business leaders say they’re only moderately prepared, slightly prepared, or not prepared at all.² Think of AI like the cloud, which requires increased computing, network, and storage infrastructure. Power density requirements for AI can be upwards of ten times more than what traditional data centers consume.

Look to high-performance computing (HPC) infrastructure to run AI applications, especially High-Density Colocation solutions that can support the power, security, cooling, and compliance requirements for efficient private AI infrastructure.

To mitigate data gravity concerns, take a distributed approach to processing data at the edge. Leverage a private AI exchange across a global private data fabric to embrace options for interconnectivity and data transport to a broader ecosystem of AI infrastructure, datasets, services, and networks.

As IT leaders consider these needs for private AI, they should also ask if their legacy IT infrastructure will be able to support it, how to integrate it into legacy systems, and if they’ll need to invest in new platforms or infrastructure to support evolving private AI ambitions.
Build the team and partner with the experts: IT and engineering teams will collect and prepare the data for private AI offerings. IT leaders may also need AI or software engineers as they evolve the AI model, whether building a natural language interface, a simple web app, or a bot. 

Because of data privacy and access management, IT security teams should be involved in planning. IT leaders should also leverage the right ecosystem of partners as they evolve their AI strategy and look externally to partner with experts who know the unique infrastructure AI requires.

Preparing for the future of enterprise AI

IT leaders are seeking ways to safely use private data with GenAI, whether for text generation, customer service, or analysis and decision-making.

As AI solutions become more widespread through off-the-shelf or SaaS products, private AI will become the way forward for organizations prioritizing data privacy and wanting to keep their assets, IP, and customers safe.

We’re seeing the evolution of enterprise AI before our eyes. To learn how to future-proof your IT infrastructure, read our latest whitepaper: AI for IT Leaders.

Footnotes:
1. Brown, T., et al., Language Models are Few-Shot Learners, July 22, 2020. https://arxiv.org/pdf/2005.14165.pdf
2. Deloitte LLP, Deloitte’s State of Generative AI in the Enterprise Quarter One Report, January 2024. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-report.pdf