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In June, it will be seven years since the Office of Management and Budget released the initial federal data strategy.
Just this past March, it marked two years since the launch of the first government-wide policy on artificial intelligence.
Both milestones signaled a change in how federal agencies utilized data to enhance their core missions.
Establishing a modern data strategy was a foundational step for agencies before they could explore and deploy AI solutions. Organizations needed to establish frameworks for data governance and data management before venturing into AI experimentation.
Dealing with massive amounts of data remains a constant challenge, yet over the past seven years, many agencies have advanced sufficiently to confidently begin testing AI applications for automating routine tasks, analyzing essential mission data, and interacting directly with the public.
As AI adoption grows, agencies must ensure these tools are secure and reliable.
Unsurprisingly, specialists indicate that high-quality, reliable data is the primary factor determining an organization’s success with AI.
“The progress in AI has made us realize the need for specific data governance at the National Nuclear Security Administration, which must address the various classifications of data we handle,” remarked Karen Sutton, Chief Technology Officer at NNSA within the Department of Energy, during the panel Mission-Ready AI: Where Data Strategy Meets Real-World Impact. “For several years, an NNSA unit has led ‘DOE data days.’ We’ve watched this event grow from presenting 4 or 10 different teams to the point where this year we had to reject applicants. It’s great to see so many people tackling data problems as we work to refine our strategic approach.”
Sutton explained that these DOE data days also highlight the fact that as AI usage expands, the sheer quantity of data within the department grows. She noted this awareness is pushing NNSA to prioritize data quality, ensuring data is curated and accessible across its diverse program offices, laboratories, plants, and sites.
“The goal is to address data hurdles and review current processes and department-wide initiatives so teams can benefit from one another’s work. From an enterprise perspective, we need a unified strategy to share information more effectively than in the past,” she stated. “We also process a large volume of data across both classified and unclassified systems. We need a data strategy applicable to numerous locations and networks. Our priority is to enforce rigorous data governance, manage our systems effectively, control data quality, and ensure that any decisions are grounded in the best possible information.”
Transitioning to a Strategic Ally
Managing data at massive scale is a central challenge for the Office of Inspector General for the Postal Service.
Berivan Demir Neubert, Director of Analytics Operations and Governance for the USPS OIG, stated the office ingests 110 petabytes of data annually, requiring a highly selective approach to selecting data for internal use.
“We must ensure it aligns with our mission to uphold the integrity, accountability, and efficiency of the U.S. Postal Service and its regulator,” Neubert stated. “After transitioning to a more modern data infrastructure, we migrated it into a more tightly governed environment. Our focus remains on continuous improvement and planning next steps. Our goal for the coming year is to mature from being a data provider reacting to requests from investigators and auditors to becoming a strategic partner.”
Embracing a strategic role includes safeguarding data with role-based and attribute-based access measures. Neubert emphasized that these controls are vital as generative AI tools become more prevalent and user-friendly.
“We have numerous application programming interface (API) connections to private sector data to enrich our work for our agents and auditors,” she added. “Over the last few years, we have invested time in tagging documents and populating comprehensive metadata for all data stored within the OIG.”
By integrating security, governance, and access controls, agencies foster a robust data culture spanning data owners, AI developers, and data scientists.
Maintaining AI’s Precarious Balance
Brent Hansen, Chief Technology Officer at Optiv + Clearshark, noted that strong data oversight and transparency enable agencies to accelerate AI adoption and foster mission innovation.
“This puts significant emphasis on mastering fundamentals: data is our most valuable asset. It requires us to revisit and refine data curation, understand pipelines, and connect data owners with data scientists and DevSecOps teams,” Hansen explained. “Higher maturity in governance leads to much faster progress. Understanding visibility, access rights, and data compatibility is extremely beneficial.”
A key takeaway from all three experts was the importance of slowing down to speed up.
Hansen emphasized that investing in foundational data governance, classification, and tagging allows agencies to integrate and scale AI tools more quickly than skipping ahead.
“Deploying AI is a delicate operation. Data must be secured, and progress should be cautious, following established guardrails while collab-orating with CIOs, security teams, and data owners as responsible stewards of both data and cybersecurity,” he explained. “When there is harmony and a shared framework, it acts as the guiding principle—the ‘bible’ for all stakeholders—to ensure responsible governance and cyber-safe practices. While we must proceed responsibly, there is no denying that we can move faster today than anyone imagined even two years ago.”
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