In 2023, generative AI (GenAI) made a significant impact on the market, transforming how organizations monitor environments, create content, and automate their operations. Businesses quickly recognized its potential, particularly in enhancing customer support experiences and producing content for sales, marketing, and other essential functions. However, they also grew cautious about the risks associated with sharing sensitive data through cloud-based GenAI tools, as this information could potentially be used to train other AI models accessible to competitors. While much attention has been placed on large language models (LLMs), many organizations primarily require AI models that are fine-tuned and customized using their domain-specific data to address their unique business challenges.

In the Asia-Pacific region, our customers frequently inquire about how to leverage the advantages of GenAI while maintaining data privacy and control. Below, we address the five most common questions we receive from them:

1. We want to embrace GenAI tools and applications, but training LLMs is expensive and time consuming. What’s the best approach?

A common misconception about Generative AI is the belief that building and training large language models (LLMs) must start from scratch. This process can be both costly and time-intensive. Instead, a more efficient approach involves leveraging a robust foundation model—whether open-source or commercial—and fine-tuning it using your organization’s private business data. This method offers a cost-effective and practical solution.

The cornerstone of this strategy lies in adopting an open and flexible architectural framework. Such an approach enables you to experiment with various AI models and tailor them to your domain-specific needs. In doing so, the AI model is brought close to your data, ensuring your information remains within your data center under your full control. This guarantees privacy and compliance while allowing you to customize the model securely. This concept is what we refer to as Private AI.

2. We have specific compliance and security requirements for our data and AI applications. How do I ensure that we maintain security and compliance?

Highly Distributed: compute capacity and trained AI models reside adjacent to where data is created, processed, and/or consumed, whether the data resides in a public cloud, private cloud, data center, or the edge. Organisations keep control of their data and AI models, maximizing security and privacy.

Data Privacy and Control: an organisation’s data remains private to the organisation and is not used to train, tune, or augment any public models without the organisation’s consent. The organisation maintains full control of its data without taking on the added risk of data leakage.

Access Control and Auditability: access controls are in place to govern access and changes to AI models, associated training data, and applications. This allows organisations to showcase that they are implementing GenAI in accordance with policies and regulations around the responsible, ethical, and unbiased use of AI. Because the AI model and associated services run adjacent to your enterprise data, you may also benefit by using existing controls, tools, and processes to manage access and audit.

3.AI technology is moving so fast. What if I invest big in a specific AI technology, and then a new one comes to market that would work better for us?

The AI landscape is evolving rapidly, with new technologies, models, services, and tools emerging at a breakneck pace. Organizations benefit most from an open and flexible AI infrastructure that is hardware-agnostic and not tied to a single AI model. Flexibility in working with diverse AI technologies ensures businesses avoid vendor lock-in, enabling seamless transitions between models, services, or tools via simple software updates. VMware Private AI embodies this architectural philosophy by delivering a comprehensive suite of GenAI solutions tailored to diverse environments.

It supports integration with leading industry players such as NVIDIA, Intel, IBM, Hugging Face, open-source repositories, and independent software vendors. Backed by a broad open ecosystem, it offers certified server architectures from major OEMs like Dell, Lenovo, HPE, and Supermicro. Leading global system integrators such as NTT Data, WWT, HCL, Wipro, and Kyndryl further assist organizations in navigating their GenAI adoption journey.

4.AI experts are telling us that GenAI is beyond our reach because we need a massive number of  GPUs to get started. We don’t have the necessary processing power, technical skills and budget so our only option seems to be that we run all our services with public cloud providers. Is that true?

No, it is not true. Many organizations in the Asia-Pacific region frequently hear that achieving meaningful outcomes with AI requires highly specialized expertise, massive financial investments, and access to thousands of GPUs. However, this simply isn’t true. Most organizations can harness AI’s potential by fine-tuning existing models with their own data rather than investing heavily in training custom models from scratch. High-quality foundation models, including open-source options, are readily available for download.

Techniques like retrieval-augmented generation (RAG), which integrates models with secure data sources, offer a practical way to implement AI while retaining complete control over data privacy and compliance. By bringing models directly to their data instead of the other way around, organizations maintain authority over whether their data is used for broader purposes. With this approach, organizations can have a proof of concept (POC) operational within weeks, delivering tangible results without requiring substantial investment or expertise.

5. I have an existing VMware Cloud Foundation (VCF) environment. How can I make the most of my current investment while leveraging GenAI for our organization?

If you already have an existing VCF environment, it is very easy to leverage your current investment to deliver GenAI capabilities to your customers and employees across Asia-Pacific. VCF is the central component in VMware Private AI. While allowing you to bring AI models to the data sources you already have, VMware Private AI maintains privacy, governance, and controls that are already in place—using your existing toolset.

 ACCELERATE AI ADOPTION SECURELY

With AI advancements, there’s no need to compromise on choice, privacy, or control anymore. Private AI equips organizations with all three, allowing them to fast-track AI adoption while ensuring their infrastructure remains future-ready.