Federal agencies are entering fiscal 2027 with a familiar challenge: advancing artificial intelligence initiatives while continuing to modernize legacy systems, strengthen cybersecurity and operate under tight budget constraints.
For Jeremy Wilson, public sector CTO at EDB, the common thread connecting those priorities is data. Agencies increasingly find that the success of AI and cybersecurity initiatives depends less on the technology layered on top and more on the foundation underneath, he said.
“AI is a data problem before it’s a model problem,” Wilson said. “Agencies are recognizing that AI progress is determined less by access to models and more by the readiness of their data.”
Wilson said many agencies still struggle with data that is fragmented across multiple environments and difficult to operationalize at scale. That challenge has led technology leaders in government to question where AI workloads should run and how they can best interact with mission data.
“Our whole thesis is: Bring the AI to the data, not the other way around,” he said. “If your mission data is in Postgres, and increasingly it is, you can stand up vector search, retrieval-augmented generation and AI agents on that same governed platform.”
Critical need #1: Maintaining data sovereignty
As agencies expand AI use cases, concerns about security, privacy and control over government data are driving increased interest in sovereign AI architectures.
Wilson said agencies cannot afford to lose visibility into where sensitive data resides or who can access it.
“Agencies can’t send citizen data, law enforcement data or classified-adjacent data to whatever inference endpoint happens to be cheapest,” he said. “Sovereignty means you control where the data sits, where the models run and who touches both.”
To address those concerns, EDB has embraced a model-to-data approach that allows agencies to run AI models where mission data already resides rather than moving data to external services. Wilson said this architecture enables agencies to build agentic AI capabilities within controlled environments and reduce reliance on large public language model endpoints.
He added that sovereign AI is increasingly becoming a business imperative as well as a security requirement.
“Our Sovereignty Matters research found organizations that are deeply committed to sovereignty realize up to five times the return on investment on AI,” Wilson said.
Critical need #2: Governing data in an AI era
While much of the federal conversation around zero trust focuses on identity, devices and networks, Wilson said agencies can overlook the role of data.
“Data is one of the five pillars in both the Cybersecurity and Infrastructure Security Agency’s maturity model and the Defense Department’s reference architecture. Yet, it’s often the least mature,” he said. “You can have perfect identity and network segmentation, but if the database itself isn’t enforcing least privilege and protecting data at rest, you haven’t finished the job.”
Wilson said protecting the data layer requires strong access controls, encryption, auditing capabilities and adherence to federal security requirements. Those measures have become increasingly important as agencies place greater emphasis on software supply chain security and compliance.
“Post Executive Order 14028, agencies are accountable for their software supply chain,” he said, noting the growing importance of software bills of materials and automated compliance capabilities.
Critical need #3: Planning for long-term modernization opportunities
The push to modernize legacy technology environments also is prompting agencies to reevaluate long-standing database investments.
Wilson said many federal organizations remain dependent on proprietary platforms that increase costs and complicate modernization efforts. He pointed to growing interest in open source technologies as agencies seek greater flexibility and lower operating expenses.
“We built deep Oracle compatibility into EDB Postgres Advanced Server precisely so agencies don’t have to rewrite every application to escape proprietary licensing,” he said.
Wilson cited the Forest Service as an example of an agency that used modernization efforts as an opportunity to move to a lower-cost and more high-performing platform. The result, he said, is an open and portable technology foundation that didn’t replace one form of vendor lock-in with another.
Critical need #4: Building a foundation for fiscal year 2027 priorities
At a time when agencies are under pressure to demonstrate measurable returns from their AI investments, Wilson said federal tech leaders should focus less on individual tools and more on the underlying data infrastructure supporting them.
Wilson said a data-first foundational approach will determine how successfully agencies can translate technology investments into mission outcomes over the next several years.
“Every initiative on the federal agenda — from AI adoption and zero trust to customer experience and legacy retirement — lands on the data layer eventually,” he said. “If that layer is open, secure, sovereign and AI-ready, everything above it gets easier and cheaper.”
Bringing AI to the data: A different approach to agency AI
Traditional artificial intelligence deployments often require moving data to a separate platform for analysis and model training. That can create additional governance, security and compliance challenges, EDB’s Jeremy Wilson says.
A model-to-data approach flips that equation:
Step 1: Keep mission data where it already resides.
Step 2: Apply governance, security and access controls at the data layer.
Step 3: Run AI capabilities, including vector search, retrieval-augmented generation and AI agents, within that controlled environment.
Step 4: Deliver AI insights while maintaining control over sensitive government data.
Potential benefits for agencies:
- Reduced data movement
- Stronger data sovereignty
- Simplified governance
- Lower security risk
- Faster deployment of AI capabilities
See how the Forest Service is using EDB Postgres AI
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