It’s no secret that business leaders are energised by the possibilities of generative AI. The dialogue around it is intense and there’s an urgency to figure out how it can help their companies. They have questions around where to start, how to scale, and how to harness the knowledge from public AI models while protecting sensitive information such as IP.
That’s where enterprise-level AI comes into play with its combined use of private AI and data and training models in the public cloud. Private AI gives companies the control to capitalise on the benefits of AI while keeping their sensitive data safe and confidential. Like private cloud, private AI must be operational in non-public environments so businesses can use their proprietary data while retaining complete control.
Enterprises that have successfully transformed digitally are now deploying their digital infrastructures to advance enterprise AI. Their leaders follow three critical strategies to get AI right:
- Structure data architectures for governance, privacy, and residency.
- Use interconnection for hyperconnectivity.
- Choose sustainable AI and use AI for sustainability.
They also choose the ecosystems that connect companies with the partners, systems, and tools they need to deploy their enterprise AI infrastructure in the right places.
Benefits of private AI
Private AI is AI built for an organisation’s exclusive use while maintaining control over its models and data. Additional benefits of private AI include:
- Improving workload latency.
- Reducing regulatory risk.
- Cost predictability.
To run specific workloads, integrating a hybrid multi-cloud infrastructure for private AI simplifies access to multiple cloud and edge environments.
Let’s take a closer look at the three essential strategies for deploying digital infrastructure that help companies enable private AI.
Structure data architecture for governance, privacy, and residency
Private AI requires lots of data from multiple sources—private, public, and third party—to create better business outcomes. Once companies build the necessary data lakes and deploy infrastructure to feed data into AI engines, they must format that data for easy consumption.
Private data can be exposed through leakage when using training models hosted in a public AI infrastructure. Having an appropriate data architecture helps companies maintain control and ownership over their data and supports compliance with data sovereignty and regulatory requirements. It also helps optimise costs by reducing the need for data duplication across multiple cloud service providers.
Data architectures should be designed for the seamless flow of information with three crucial components:
- Governance: Establish processes for collecting, storing, processing, and managing data to ensure data quality and regulatory compliance.
- Residency: Determine where to store specific data sets—on-premises, in colocation data centres, or in the cloud—and in what country.
- Privacy: Safeguard data to protect sensitive information and meet global data protection regulations.
Private AI requires new data architectures and patterns capable of integrating advanced technologies. Establishing an effective data architecture begins with creating a central data repository, enabling data to be moved from the edge to the cloud and back while ensuring complete control. This approach allows companies to effectively manage their data across various AI services while retaining control over their data.
Use interconnection for hyperconnectivity
Private AI amplifies connectivity requirements, demanding low latency for workloads that require real-time processing. Deploying a hybrid multi-cloud environment with virtual private network connections—otherwise called interconnection—shifts data sharing off the public internet to private networks, and connects workloads and data worldwide. Interconnection provides hyperconnectivity to and from the digital core, ecosystems, and the edge. This aims to help enterprises reach more participants, partners and services, from clouds to data marketplaces.
Private interconnection supports data architecture movement patterns that include:
- Enabling data ingestion from multiple sources.
- Optimising data transfer speeds between cloud and private resources.
- Accelerating the distribution and automation of real-time actionable insights.
Using interconnection and cloud-adjacent storage helps ensure edge access to data, a secure perimeter, and real-time inference.
Choose sustainable AI and use AI for sustainability
Private AI requires more computing power, so sustainability is a primary concern over how companies are consuming or building private AI infrastructure.
Sustainable AI integrates environmental efficiencies in the design, development, and deployment of AI systems across their lifecycle. Sustainable AI includes making algorithms, models, and forecasts more energy efficient. To achieve this, companies need to examine how colocation data centres provide efficient infrastructure and invest in green energy.
More powerful IT equipment, higher density deployments, edge computing, and the demand for greater efficiency are all driving the need for more advanced cooling techniques. Advanced liquid cooling technologies—like direct to chip—enable businesses to more effectively cool the powerful, high-density hardware that supports compute-intensive workloads like AI.
Data centre operators are also exploring other ways to reduce total energy consumption without impacting the safe operation of IT infrastructure, such as adopting the recent guidance from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), stating that A1 (enterprise-level) class equipment can now safely operate at higher temperatures.
Conversely, businesses are using AI to reduce operational carbon emissions and accelerate climate action programs. For example, AI is helping airlines optimise flight paths to reduce fuel usage. Biodiversity companies are also using it to uncover more nature-friendly solutions.
