Four surprising truths about AI in government

With new executive mandates focused on winning in AI, agencies across the federal government are racing to scale AI.

But many will hit a wall of complexity or be stalled by friction in the data pipeline as critical information remains trapped in silos, storage costs spiral out of control, and security risks multiply.

Overcoming this friction requires more than just another tool. It demands a fundamental shift toward a data infrastructure that is optimized, secured, and ready for AI.

With NetApp AI Data Engine (AIDE), a unified, AI-ready data service integrated into our NetApp ONTAP operating system, NetApp is redefining what's possible for enterprise AI.

The old rules of AI no longer apply. So, forget what you think you know. Here are four surprising truths about getting your data AI-ready.

Your AI data can be smaller

To be useful for AI and large language models (LLMs), unstructured data, like text and images, must be vectorized. The process of vectorization can multiply data volume exponentially, creating cost and performance problems at scale. As a result, projects that looked promising in the lab never make it to production.

NetApp AIDE combines NVIDIA AI Enterprise microservices with NetApp’s advanced compression and deduplication to reduce vector data storage requirements. No more runaway storage costs or infrastructure performance bottlenecks. AIDE is changing the game by making large-scale AI practical, affordable, and ready to deploy across the business.

Adding on more security tools doesn’t always make you more secure.

Many civilian and defense agencies are still progressing toward their Zero Trust targets. If your data isn’t managed properly or securely, AI can introduce vulnerabilities. For example, multiple data copies can create vulnerabilities because access permissions don’t always stay with the copied data. As data moves through the AI pipeline, it may not always be properly protected and governed.

Adding more security tools doesn’t reduce risk if security isn’t embedded where the data actually lives.

True AI security cannot be a reactive, downstream checklist. It must be an automated, proactive function of the data itself. Because you can't protect what you can't see, this starts with comprehensive visibility.

NetApp enforces data governance at the storage layer before data enters AI pipelines, providing a proactive, defensible compliance posture rather than reactive downstream controls.

AIDE creates a unified and searchable metadata catalog by automatically discovering and indexing data across the entire hybrid cloud estate. It’s constantly scanning the data for sensitive data types. This enables intelligent, policy-driven guardrails that enforce "condition-action" rules—such as "anonymize if person + email present"—to automatically redact, mask, or exclude sensitive information, such as PII and PHI, before it ever enters an AI workflow.

This flips the script on data governance and compliance, moving it from a downstream bottleneck that stalls innovation to an upstream, automated function of the data fabric itself. It empowers data science teams to innovate and adopt AI safely and at speed, knowing that governance is enforced by design.

You don’t need a different copy of your data for every stage of the AI pipeline

The old way of preparing data for AI involved creating a copy for every project or model. This practice creates a nightmare of data sprawl, stale information, model drift, and erroneous insights.

AIDE uses automated change detection and synchronization to monitor changes in source data and automatically synchronize datasets across the global data estate, whether on-premises or in the cloud. This eliminates redundant copies and ensures that AI models are always trained on the most current and highest-quality data available, dramatically improving their reliability, relevance, and accuracy.

You don't have to overprovision storage to get more AI performance.

Traditionally, scaling infrastructure for AI meant buying compute and storage in lockstep. The need for more processing power often forced the purchase of more storage capacity, leading to inefficient resource allocation and inflated costs.

NetApp AFX is disaggregated storage built for the AI-powered enterprise. AFX eliminates AI storage overprovisioning by allowing you to scale performance independently from capacity, so you can add processing power to handle demanding AI tasks and keep your GPUs busy without paying for empty storage.

From data friction to AI velocity

The journey to production-scale AI is paved with data challenges, but a modern approach changes the game. The solution is a unified data fabric in which curation, security, and performance are integrated by design.

Instead of exploding cost, complexity, and risk, you can actually shrink your data footprint, embed security from the start, eliminate data duplication, and decouple compute from storage.

Want to explore more? Reach out to a member of our Federal team.