By: Daniel Carroll, Field CTO, Dell Federal
AI depends on data that is visible, governed, and usable. For federal agencies, data control is the foundation for scaling AI securely and responsibly.
Key takeaways
- AI-ready infrastructure starts with governed mission data: where it lives, how it is classified, who can access it, and which workloads can responsibly use it.
- Secure AI provides a decision framework for determining which workloads require greater control and which can run in shared, hybrid, or cloud-connected environments.
- IDC found that 69% of government organizations globally have or plan to implement secure AI within 12 months.
AI can only deliver value when agencies can use the right data in the right way. For federal organizations, that starts with visibility: knowing where data lives, how it is classified, how it moves, who can access it, and which AI workloads can responsibly use it. As agencies scale AI beyond early pilots, governed mission data becomes the foundation for trusted, secure, and repeatable AI operations.
Where is the data foundation for AI most important?
This foundation is especially important for priority workloads such as agentic AI, geospatial intelligence, data engineering, mission intelligence, cybersecurity, and high-performance computing, all of which depend on data that is visible, governed, and usable.
Unstructured or siloed data can reduce model accuracy, create compliance gaps, and require costly pre-processing before AI can use it. These are common challenges in federal environments where data spans legacy systems, classification levels, and geographically distributed networks. A strong data foundation helps agencies turn information into governed datasets that support analytics, automation, and decision-making.
Once agencies understand the data foundation behind each workload, they can apply governance controls that match the sensitivity, purpose, and operating environment of the AI system.
How do federal agencies govern AI access?
As agencies expand AI, governance must be built into how each workload is deployed, monitored, and scaled. That means protecting data, overseeing models, supporting auditability, applying zero trust, managing inference operations, monitoring agent behavior, and tracking token consumption and workload utilization. Clear policies and continuous monitoring help agencies keep AI transparent, controlled, trusted, and aligned with mission requirements.
Confidential compute can support this foundation by helping protect sensitive data and models while they are being processed. NVIDIA Confidential Computing, for example, uses hardware-enforced trusted execution environments to ensure that AI model weights and input data remain encrypted even during processing – an important capability for federal agencies handling classified or personally identifiable information.
What is secure AI and why does it matter to federal agencies?
Dell Technologies defines secure AI as the ability to govern, develop, and operate the AI lifecycle with authority over data, compute, models, and policy. Secure AI gives agencies a useful decision framework for data, infrastructure, and model control.
IDC found that 69% of government organizations globally have or plan to implement secure AI within 12 months. For secure AI, strong data governance, quality, and control is the most critical AI platform element.
Secure AI does not require an all-or-nothing architecture. Instead, it helps organizations determine which workloads require greater control – such as those involving sensitive data, strict compliance needs, or mission-critical operations – and which can responsibly use shared, hybrid, or cloud-connected environments for temporary capacity, specialized services, or broader flexibility.
By matching each workload to the right level of control, agencies can shape the infrastructure foundation to keep data, models, and workloads aligned with mission requirements.
How does on-premises infrastructure improve data control?
Bringing AI closer to governed mission data can reduce unnecessary data movement, strengthen control, and support the economics of high-volume AI workloads. Bringing inference on premises can reduce AI costs by 28% to 90%+ for persistent workloads, according to a Signal65 two-year cost model comparing Dell AI Factory with NVIDIA against cloud application programming interfaces. On-premises infrastructure also helps agencies align infrastructure with the demands of AI at scale: data quality, legacy integration, workforce readiness, security, governance, token demand, and cost predictability.
The Dell AI Factory with NVIDIA provides the platform agencies can use to operationalize AI across Dell infrastructure and services, integrated with NVIDIA accelerated computing, NVIDIA networking, NVIDIA AI Enterprise software, and NVIDIA NIM inference microservices. The NVIDIA AI Factory for Government reference design provides guidance for government-grade AI factory architecture, including security, compliance, orchestration, observability, and workload support.
Together, this approach helps agencies build AI on governed mission data and scale priority workloads with greater confidence. The result is a secure, repeatable foundation for return on mission – saving time, improving services, reducing risk, controlling costs, and accelerating decisions. Learn more: https://www.delltechnologies.com/assetlink/doc/en-us/meritalk-dell-nvidia-ai-moves-missions-forward-ebook-dl2bqz-original.pdf.