By Dario Perez, VP Federal Civilian and SLED, Cloudera

Artificial intelligence presents a transformative opportunity for government, from enhancing national defense readiness to improving citizen services and enabling data-driven decision-making at scale. However, to move from promising pilots to scalable, mission-aligned deployments, agencies must focus on what lies beneath the surface: trusted data and infrastructure.

Cloud adoption has been underway in the Federal government for over 15 years, and every agency has made meaningful progress. However, as the Forrester State of Cloud in Government, 2025 report highlights, realizing the expected value of cloud remains a significant challenge. While many agencies have migrated workloads to the cloud, environments often remain fragmented, hybrid strategies are underdeveloped, and legacy systems continue to limit how data is accessed, analyzed, and secured. These gaps directly undermine an agency’s ability to make data actionable, especially at the scale and speed that AI demands.

At the same time, expectations from agency leadership are rising. CIOs and IT teams face growing pressure to demonstrate meaningful operational returns on AI investments – quickly. But AI isn’t a box to check or a tool to bolt on. It requires a deliberate, step-wise approach rooted in three fundamentals: clearly defining the mission problem; ensuring access to accurate and trusted data; and modernizing the infrastructure to enable secure and reliable access to data wherever it may be.

To enable mission-based AI decision making, government agencies must rethink their modernization strategies. In practice, that means developing and implementing a data strategy that encompasses both cloud and on-premises data centers into integrated hybrid multi-cloud environments.

This approach shifts agencies from fragmented datasets to governed, action-ready data ecosystems, taking one-off pilots and transforming them into AI deployments that directly support core agency goals.

Hybrid Multi-Cloud: The Foundation for Modernization and AI Readiness 

Managing agency data via a single pane of glass through a hybrid multi-cloud environment is no longer a compromise – it’s a strategic enabler. Today’s tools provide the flexibility and control needed to manage data as a critical strategic asset without disrupting operations.

For government agencies, hybrid multi-cloud offers a practical and scalable path to AI readiness by:

• Enabling gradual modernization. Keep mission-critical processes as they are and make incremental movements without disrupting continuity.
• Staying in compliance. Agencies can’t afford downtime or risks to the data assets in their care. A hybrid cloud strategy aligns with security and compliance mandates, including FedRAMP and DoD IL5/6.
• Offering strategic roll-out. A hybrid cloud strategy allows agencies to prioritize and deploy workloads across on-prem, public cloud, and edge environments as needed.

Early adopters have already leveraged hybrid multi-cloud environments for various agency use cases, including:

• Accessing real-time analytics in field operations, ensuring that agencies are aligned, synced, and acting on the best available information.
• Training AI models in multi-cloud environments, offering controlled visibility far beyond what was possible when data was siloed and hard to access.
• Maintaining operational continuity across disconnected or contested networks, powering confidence and resilience across the board.

By designing infrastructure with hybrid multi-cloud in mind, agencies can ensure interoperability, scalability, and resilience – all key attributes to realizing their AI-driven missions. To scale AI beyond prototypes, the public sector needs more than cloud capacity. They need control and clarity.

Trustworthy, Reliable Data: The Core Enabler of AI Readiness

Once the infrastructure is in place, the next imperative is ensuring the data is usable, trusted, and well-managed. Poor data quality, governance gaps, and silos are still among the top barriers to successful AI deployment because AI outputs are only as strong as their inputs.

As such, for AI to work at scale, government agencies need to prioritize:

• Comprehensive audits. Who accessed the data, including when, why, and how. Tracking data lineage and ensuring accuracy is possible through metadata and audit tools.
• End-to-end traceability. Where did the data come from, and were any filters, parameters, or prioritizations added along the way? If so, can they be trusted and defended? Modern data “lakehouses” are the preferred architecture for AI as they support unified structured and unstructured data and streaming data across environments and support AI training without forcing risky or inefficient data movement.
• Visibility through a single pane of glass. Accounting for distributed data means they need observability and control from a unified point.

With well-governed, high-quality data, agencies reduce the risk of model drift, bias, or unreliable insights, and increase their ability to confidently act on AI outputs. But as AI expands across mission-critical functions, data security becomes the thread connecting every part of the infrastructure.

Security and Trust: The Prerequisite for Cross-Domain AI

Before full deployment at scale, government agencies must review touchpoints and patch vulnerabilities. Public trust and mission success both depend on building AI that is not only powerful, but also secure by design.

That means:

• Adopting zero-trust architectures that enforce strict identity and access controls, even inside the perimeter.
• Encrypting all data, regardless of whether it’s at rest and in motion, while applying segmentation to reduce the blast radius in the event of a breach.
• Building secure-by-design AI pipelines that allow for testing and deployment while offering a way to monitor for drift.

In a crisis, there’s no time to engineer collaboration on the fly. Secure cross-domain interoperability must be built in from the start. By bringing AI to the data, instead of moving data across environments, agencies reduce exposure while preserving speed and mission relevance.

AI is reshaping how government agencies operate, but only if the foundations are solid. Success depends not on speed, but on sequencing: modernizing infrastructure, governing data, and securing every layer of the AI pipeline.

Agencies that take a step-wise, mission-aligned approach – starting with hybrid cloud, followed by trusted data practices and secure data access – will be best positioned to scale AI effectively. This isn’t just about technology. It’s about transforming how government agencies deliver value, build public trust, and advance their missions.

With a thoughtful roadmap in place, AI can move from isolated experiments to enterprise-wide transformation.