Following President Trump’s announcement of the AI Action Plan, federal agencies are now turning their attention toward building AI-ready infrastructure and advancing U.S. technology leadership.
The AI Action Plan aims to accelerate AI adoption across the federal government and position the U.S. as a global technology leader. Key initiatives include expanding AI deployment within the Department of Defense and establishing an AI Information Center under the Department of Homeland Security to address emerging cybersecurity threats.
The plan also prioritizes scaling domestic infrastructure to meet the growing computational demands of AI and streamlining permitting processes for data centers and AI factories to maintain U.S. competitiveness in the global tech landscape.
Realizing the full scale of this vision will require federal agencies to overcome entrenched roadblocks: siloed data systems, under-prioritized use cases, and gaps in security and compliance. These legacy barriers remain the Achilles’ heel of AI adoption in government.
VAST Data Federal stands ready to support these efforts, already powering the data infrastructure at three Department of Energy sites preparing to build new AI clusters, including the forthcoming supercomputer, Doudna, at NERSC. This marks just the beginning of a broader federal infrastructure modernization effort.
The AI Infrastructure Directive
As President Trump mentioned during his announcement on July 23, America’s AI leadership will be defined by its ability to build infrastructure that converts data into real-time, mission-driven action. In our view, further investment in data centers and AI factories will provide new industrial-scale infrastructure purpose-built to transform raw data into intelligence at speed and scale.
AI factories are optimized for a singular mission: manufacturing intelligence. Unlike traditional data centers, which support a wide variety of workloads, these environments are designed to orchestrate the full lifecycle of AI, from ingesting massive volumes of data, to training and fine-tuning frontier models, to performing high-volume, real-time inference across industries and missions.
This model is gaining traction globally. From the European Union’s coordinated investment in 13 national AI factories to NVIDIA’s DGX SuperPOD deployments in India, Japan, and Norway, the world is rapidly organizing around this new paradigm. America’s edge in this race hinges not just on AI models themselves, but on the infrastructure that supports their development and deployment.
But as AI moves from experimentation to production, the bottleneck is shifting from compute to data. The faster an organization can turn raw, distributed, unstructured data into usable intelligence, the faster it can develop differentiated AI applications.
The State of Federal AI: Promise Meets Pain Points
While the AI Action Plan sets the directive for agencies to follow, most organizations are still in early AI experimentation phases, often with inconsistent data infrastructure and no clear roadmap to scale.
Furthermore, major hurdles remain, including:
Siloed Data Systems Many agencies operate in data silos across environments not designed for AI, making it difficult to fine-tune models or operationalize retrieval-augmented generation (RAG) at scale. Constantly copying data across environments creates not only inefficiency, but security risks as well.
Prioritizing Use Cases Without modern data platforms, federal leaders struggle to evaluate and prioritize AI initiatives based on real mission impact. Proof-of-concept projects languish instead of scaling into production without clear data visibility that will power these projects.
Security and Compliance Gaps The infrastructure in many agencies lacks the robust security and auditing capabilities needed to meet evolving federal mandates. Agencies must comply with Zero Trust Architecture, FIPS 140-3 validation requirements, and other stringent validations like the DoDIN APL, but too often, AI tools and data platforms fail to meet these standards natively, creating bottlenecks before deployment begins.
Recently, I had the opportunity to speak with Federal News Network about these and other obstacles for agencies approaching AI-readiness:
VAST Data Federal: Powering Agencies for the Future
VAST Data Federal delivers the unified, AI-ready data infrastructure federal agencies need to meet America’s AI Action Plan head-on, whether training foundation models at scale, deploying agentic AI in the field, or powering real-time intelligence from sensor to screen.
The VAST Data Federal Platform unifies storage, database, and data warehouse capabilities into a single, exabyte-scale architecture - enabling high-throughput training, low-latency inference, and real-time data feedback loops. At a time when winning the AI race is imperative, VAST brings commercial success and proven scale to the mission-critical needs of government, transforming the data layer from passive storage into active intelligence infrastructure that advances American AI leadership.
Today, VAST Federal serves as the data infrastructure backbone for some of the public sector’s most innovative and critical initiatives, including the U.S. Department of Veterans Affairs Million Veterans Program; and the MITRE Federal AI Sandbox, which provides researchers and developers across multiple federal government agencies with access to accelerated computing infrastructure and software for training LLMs and experimenting with other generative AI tools to develop AI-enabled applications.
Supporting Federal Security and Governance
The VAST Federal Data Platform is the only ZTA-compliant and DoDIN APL-certified data platform for AI. This demonstrates VAST Data Federal’s ability to deliver best-of-breed, information security software to solve mission-critical federal challenges.
Our platform delivers robust attribute-based access control (ABAC) capabilities that span both structured and unstructured data, a critical requirement for federal agencies handling sensitive information, and aligning with zero trust principles. When working with RAG-based generative AI applications, ABAC controls dynamically filter content chunks at response time—ensuring only authorized information appears in AI outputs, maintaining security without sacrificing performance or user experience.
Streamlining AI Agent Deployment
One of the biggest pain points federal organizations deal with is the complexity of operationalizing AI. That’s why we engineered the VAST DataEngine component to simplify AI agent deployment and management.
Agencies can deploy, manage, and scale AI agents in production environments without the typical integration headaches. In fact, one defense agency reduced its AI deployment time from months to days while maintaining strict compliance with federal mandates.
VAST DataEngine captures the semantic essence of data as it enters the system, ensuring AI models deliver accurate, contextually relevant insights that drive mission success.
Is Your Data Infrastructure Ready?
The time to act is now; don’t wait for a mandate. Let’s build the infrastructure for American AI leadership together.
To learn more, register for upcoming VAST Federal events in Denver, Colorado or National Harbor, Maryland for in-person learning and networking. You can also join our technical experts on Cosmos to continue the discussion.
