01/14/2025 | Press release | Distributed by Public on 01/14/2025 13:05
In our many conversations with public sector agencies, we've noticed an interesting trend in recent years: We still hear from agency CIOs and CTOs, but we often find ourselves working with chief data officers as well. The role itself isn't new, but CDOs are increasingly responsible for guiding their agencies' digital infrastructure priorities.
It's no secret what's driving this shift. New technologies are emerging that make data more valuable than ever-but only if agencies have the infrastructure needed to properly capture, store, process and move that data.
At the same time, new threats are emerging that put data at risk. This means that CDOs must strike a delicate balance between utilizing their data while also protecting and controlling it. They'll face a number of challenges as they attempt to walk this tightrope.
Harness digital infrastructure for business advantage.
DOWNLOAD THE GUIDEGovernment agencies have faced a mandate to modernize their data centers for years now. In the U.S., the Data Center Optimization Initiative (DCOI) was established by the Office of Management and Budget (OMB) back in 2016.[1] The key priorities of the DCOI include improving cybersecurity and reducing inefficiency.
Even without the external push to modernize, the drawbacks of on-premises data centers are clear. For one thing, they weren't designed to support emerging technologies like AI. Changing that would require deploying powerful hardware, energy-efficient infrastructure and advanced cooling capabilities. This would be expensive and difficult-if not impossible-for government agencies to do on their own.
Traditional agency data centers are also isolated. From a security perspective, isolated doesn't necessarily mean protected. You need access to an ecosystem of security partners that can help you address emerging threats, and you need to be able to connect with them on demand. You can't do this from inside a legacy data center.
In contrast, agencies can easily access the high-performance data centers they need on a global colocation platform. Leading colocation providers like Equinix know what it takes to modernize a data center because we're constantly evolving our designs to adapt to industry trends and meet the changing needs of our customers. Agencies can benefit from this expertise and access the latest innovations just by becoming a colocation customer.
Also, a leading colocation platform serves as a digital gathering place. At Equinix, our customers have low-latency access to an ecosystem of thousands of partners and service providers because those partners and service providers are already at Equinix themselves. This ensures that as agencies work to protect their data, they don't have to do it alone.
Quantum computing is one example of an emerging technology that will drive new threats in the years to come. QuintessenceLabs, a quantum security specialist, recently deployed its Trusted Security Foundation (TSF) key and policy manager appliance inside the Equinix IBX® data center in Canberra. Innovative security partners like this are available at Equinix, but out of reach for agencies still running traditional on-premises data centers.
Many agencies think of the public cloud as an alternative to remaining in their on-premises data centers. While it's true that the cloud can play a valuable role in IT modernization for government, agencies need to be careful about when, where and how they use cloud services.
Federal agencies need scalable infrastructure to run data-intensive workloads like AI model training and predictive analytics, and the cloud can help with that. However, they also need to ensure they can run those workloads without placing their sensitive data at risk. Of course, working exclusively with FedRAMP-authorized cloud providers can help agencies run these workloads with confidence, but that still leaves the question of where to find accredited cloud resources. At Equinix, the leading FedRAMP-authorized clouds are available via low-latency cloud on-ramps, helping agencies access the secure cloud services they need, when and where they need them.
Agencies can also build a hybrid multicloud environment using cloud adjacent infrastructure. This can help them make the most of cloud resources while also protecting against potential high costs and vendor lock-in. Instead of moving data into cloud native storage, they can build a storage environment that they maintain control over. They can deploy that storage in proximity to the cloud regions of their choice, so they'll be able to quickly move copies of certain datasets into the cloud when it makes sense to do so.
AI is another powerful example of an emerging technology that presents new opportunities and challenges for federal agencies and their data.
It's easy to see why agencies and enterprises alike are so excited about AI. But there are caveats: Success with AI requires a lot of data. It requires the right infrastructure and strategy to support that data. And perhaps most importantly, it requires you to meet data privacy and governance requirements. In the early days of ChatGPT, several enterprises learned the hard way what can happen when employees use large language models (LLMs) without considering data privacy. It goes without saying that government agencies can't afford to repeat those mistakes.
This is where private AI can help. Private AI means building AI models for your private use, trained on your proprietary data only and residing on infrastructure that you control.
On the right platform, you can build the distributed components of your private AI infrastructure to ensure you aren't putting data privacy or sovereignty at risk. This includes AI inference at the edge. You can deploy inference pods solely within specific borders to meet sovereignty requirements. However, this doesn't mean your inference workloads would be isolated. They can be fully connected to the rest of your distributed AI infrastructure using private, dedicated interconnection that keeps data protected while in motion.
Private AI doesn't just protect the data you feed into your AI models; it can also help with your data provenance requirements. If you're using AI models to guide your critical decision-making, then you need to be able to explain why the models return the results they do. In order to get that transparency, you need to know what data the models were trained on and whether the original source of that data is trustworthy. This is essentially impossible to do with publicly available LLMs that scrape their training data from across the entire internet. In contrast, a private AI approach allows you to limit your training data to trusted sources only.
Success with AI also requires the ability to connect with the right ecosystem partners on demand. The Equinix ecosystem includes many leading AI hardware providers. We've worked with these partners to ensure our joint customers can easily access the AI-ready services they need, while maintaining control over their private data.
Whether you're looking to modernize your data centers, make the most of multicloud services or tap into the power of AI, Equinix has the global colocation platform and dense partner ecosystem to help you do it.
To learn more about our unique approach to distributed digital infrastructure, read our vision paper The future of digital leadership.
[1] Data Center Optimization Initiative (DCOI), Center of Expertise for Energy Efficiency in Data Centers.