To Succeed With Private AI, Start by Building the Right Data Architecture

Hear what four industry experts have to say about balancing AI data use against control and privacy

To Succeed With Private AI, Start by Building the Right Data Architecture

To capitalize on the power of AI without placing your sensitive data at risk, you need a private AI strategy. This means building or fine-tuning your own AI models, hosting them inside a protected environment and training them on your proprietary data sets. Unlike public AI services such as ChatGPT, private AI models won’t place your sensitive data at risk of exposure.

With the right data architecture, you can get the right data to the right places to fuel your AI models, without sacrificing control over that data. This data architecture should be built around an authoritative data core. This data core is not a traditional storage silo; instead, it’s made up of a series of interconnected environments hosted in strategic distributed locations.

This allows you to deploy storage infrastructure wherever you need it to meet your data sovereignty and residency requirements. Also, by using private, dedicated network connections instead of the public internet, you can avoid placing your data privacy at risk. This means your data can seamlessly flow from various sources to various processing locations, all without ever leaving your hands.

Crucially for AI workloads, the right data architecture can help your data reach the cloud whenever the need arises. Since the authoritative data core is cloud adjacent—meaning it’s hosted in proximity to low-latency cloud on-ramps from multiple providers—you can incorporate public cloud services into your enterprise AI strategy without having to host your data in the cloud. As a result, you can take advantage of the flexibility and scalability of public cloud infrastructure while avoiding the potential drawbacks—including performance issues, increased regulatory risk and lack of cost predictability.

To help you better understand why the right data architecture is so essential to private AI success, we brought together four industry experts to share their insights on the topic. Read on to learn their thoughts.

The Equinix Indicator

In the first volume of The Equinix Indicator industry experts share their thoughts on Digital Infrastructure and Private AI.

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Private AI allows businesses to use data while retaining control

Leanne Starace, SVP Global Solutions Architecture & Engineering, Equinix

Private AI must be operational in non-public environments, allowing businesses to use their proprietary data while retaining full control. The data architecture must account for the unique requirements of distributed AI workloads, such as hosting disparate workloads in different locations and maintaining a secure perimeter around that data. This necessitates a hybrid multicloud architecture. As data sources vary and change over time, enterprises need to architect infrastructure that provides secure and efficient access to various data sources.

Adapting IT infrastructure to new governance laws

Yves Mulkers, Founder, 7wData – @YvesMulkers

In the future, enterprise AI will see a shift towards hybrid data architectures, balancing on-premises control of private, sensitive data with cloud-based analytics. Key indicators for enterprises to watch include compliance with evolving data privacy laws, efficient data residency management, secure cross-border data transfers, and agility in adapting IT infrastructure to new governance models. Modernization efforts will involve integrated teams and a shift towards product and platform models, leveraging DataOps for efficient data management. This approach will ensure flexibility and compliance in a rapidly changing digital landscape.

We’ve entered a new era of data ownership

Jo Peterson, VP Cloud & Security Services, Clarify360 – @cleartechtoday

With the advent of AI processed and derived data, we’ve entered into a new era of data ownership that is complex and includes multiple stakeholders. Data providers, AI developers and end users will complicate the ownership picture. The legal frameworks governing data ownership around this evolving class of data will struggle to keep up, given the pace of the technology. AI data represents its own set of security challenges. We’ll see tools that automatically encrypt or partially anonymize private data before it even enters pipelines. If possible, data has become even more valuable and the management, security and storage of that data will require more thought and planning than it ever has.

Maintain control, security, governance and privacy over your data

Elias Khnaser, Chief of Research at EK Media Group – @ekhnaser

AI accelerated a second wave of cloud migration, and it also solidified multicloud adoption. Savvy organizations will adopt cloud adjacency to enable access to a rich ecosystem of services, reduce hyperscaler lock-in, and enable multicloud for AI and other workloads. The key to unlocking this formula is colocating your data. Doing so enables you to maintain control, security, governance and privacy over your data, and it allows you to point services from different providers to data that is centralized outside of any single provider. It does not eliminate lock-in, but it significantly reduces it and gives you the highest degree of control and security.

Learn more about the future of private AI

Check out the Equinix Indicator for more expert insights and resources about private AI and the role digital infrastructure will play in enabling it. You’ll learn how to effectively navigate the challenges of implementing your private AI strategy, including deploying the necessary network infrastructure and doing private AI sustainably.

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