Picture a large international bank that wants to employ agentic AI to detect fraud and offer more personalized financial advice to their customers. With AI, the bank can continuously monitor transactions in real time and use AI-powered robo-advisors to provide financial recommendations. But because of regulatory considerations in their industry and the sensitivity of financial data, they must prioritize data privacy and sovereignty in any enterprise AI initiative. They don’t want to miss out on the opportunity with AI, but they also can’t risk exposing sensitive data or intellectual property.
Since the AI boom began, industries that require greater control over their data have been reticent to leverage public AI services due to security and privacy risks, cost concerns and vendor lock-in. Because AI can give enterprises a competitive advantage, more and more companies are exploring private AI infrastructure that allows them to take advantage of the most cutting-edge AI capabilities—like agentic AI—without the risks and costs associated with public AI services.
For organizations like the financial institution in our story, private AI can deliver the best of both worlds—allowing them to pursue all the benefits of AI and use some public AI solutions, while protecting proprietary data on secure, private AI infrastructure they control. In fact, private AI can solve three of the most common enterprise AI challenges:
- Maximizing data privacy while meeting sovereignty requirements
- AI model training and tuning in a secure, dedicated environment that protects data
- AI inference at the edge in a secure manner with low latency
Safeguarding data privacy and sovereignty
Some of the top worries organizations have about using AI include data privacy concerns, security vulnerabilities and lack of control over data and public AI solutions. For organizations in industries with strict compliance requirements like healthcare, finance and legal services, owning and maintaining control of their data is pivotal.
But in the age of AI and multicloud architectures, this can get complicated. International enterprises typically have data distributed to many locations, and moving all that data for AI model training, tuning and inference comes with risks.
With a private AI approach, you can put your infrastructure on-premises or in a secure colocation facility so that you maintain complete control of your data. You can protect proprietary data while still using it for AI training and inference. Only dedicated, private connections are used to move data between locations, clouds and other service providers, so you don’t risk data exposure through the public internet. With private AI, you can also meet data localization and data sovereignty requirements.
For the bank mentioned above, maintaining strict control over sensitive financial data would be non-negotiable. They’d need to customize their AI solution to meet the strict requirements of the highly regulated financial sector.
AI model training and tuning in a secure environment
To train or tune their AI model, the bank could employ a proprietary dataset that includes not only anonymized customer transaction histories but also historical market data, financial reports and regulatory documents. They may also need to bring in external data sources for training from the public cloud and from third parties. Using private AI, they could create a dedicated, high-performance environment for model training with private connectivity to the right data sources. This would enable the organization to bring their model directly to the data instead of moving sensitive data to the cloud.
Whether you’re a service provider creating a new AI model or an enterprise tuning a model that’s created by another model provider, you can safeguard your data by doing AI model training and tuning on secure, private infrastructure. Companies that need to support very large workloads will want to use private hyperscale data centers for training since they safeguard proprietary datasets while delivering the massive compute and networking capacity that’s needed for training. The bottom line: when data privacy and data sovereignty are a priority, you should train and tune your AI models in a private environment with secure connectivity to all the right data sources.
AI inference at the edge
After the bank trains or tunes an AI model on the right dataset, they’d be ready to act on it. Here’s where AI inference comes in. The bank was aiming to use AI for things like real-time fraud detection and risk assessment, and to offer personalized financial advice based on analysis of market trends and customer portfolios.
Assuming they serve customers all around the world, they’d need inference to happen at the edge locations where those customers are. But they’d still need to protect and control their data in motion. Putting private AI infrastructure in edge data centers would give them low-latency connectivity to their customers so they could apply their AI model where data is generated and where their applications are used.
For many organizations, retrieval-augmented generation (RAG) is a popular approach to improving the output of large language models (LLMs). RAG uses inference to retrieve information that provides more relevant context data for an LLM. You can imagine how helpful this might be to a bank given the fast-paced financial industry. It’s a huge advantage for a global bank to be able to enhance their AI model with the latest financial and regulatory data, for example, rather than retraining the model.
If you’re doing AI inference and RAG using sensitive data, data you need to own and control, or data that’s subject to regulation, private AI offers distinct advantages. Enterprises using private AI retain full data privacy, regulatory compliance and control over their AI operations as they apply trained models to tackle the needs of their business.
Why do private AI in a colocation environment?
Once you understand that private AI is the right approach for sensitive and proprietary data you need to control, there’s a question of where to put your private AI infrastructure. Traditional on-premises data centers often struggle to meet the demanding compute and cooling needs of AI workloads. Therefore, putting your private AI infrastructure in a leading colocation facility offers several advantages. Doing private AI at Equinix, for example, gives you access to the required power infrastructure for AI in a place that’s committed to both efficiency and sustainability. You also get access to the latest cooling technologies to support AI—whether air cooling or liquid cooling.
And then there’s the global ecosystem you can connect with here. Thousands of the world’s most prominent clouds, network service providers and enterprises are on our platform, which enables you to connect to your partners and service providers securely and access the datasets you need for AI. With our global presence, you can put AI infrastructure in the core and edge locations that are right for your business and then scale as needed.
Companies like the bank in this story are navigating a complex AI landscape. For many organizations, private AI solutions are needed where public AI services lack the data privacy, control and cost-efficiency they require. If you’re facing one of these enterprise AI challenges, private AI might be the answer.
Visit our website to learn more about private AI solutions from Equinix.
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