Three keys to building a secure, AI-ready infrastructure from core to edge

For most enterprises, AI models aren’t the hard part anymore. Getting infrastructure to sustain them at production scale is. Teams are rapidly embedding AI into their applications and looking to deploy generative and agentic AI use cases and real-time inferencing. But the path from pilot to production stalls when infrastructure can’t keep up with enterprise data.

If data is fragmented, unsecured, or slow to access, data pipelines stall and governance gaps emerge, introducing risks. What worked in isolated environments doesn’t automatically translate to enterprise-scale systems. The root challenge isn’t model quality - it’s data and infrastructure readiness.

While enterprises have invested heavily in building massive data estates, much of this data is unstructured, widely distributed, inconsistently governed, and not ready for AI. At the same time, AI workloads place new demands on compute, storage, and networking – requirements that now also expand into distributed and edge environments. AI demands sustained throughput, low-latency access to large-scale datasets, and the ability to run across environments without introducing operational friction.

Trying to solve this with traditional, siloed approaches or add-on solutions increases complexity, while failing to deliver consistency, security, or reliability. Data copies proliferate, pipelines multiply, and performance is less predictable.

To operationalize AI at scale, organizations need a unified approach that brings AI to the data, standardizes deployment, and maintains control and security across environments. FlexPod, co-developed by Cisco and NetApp, is designed specifically for this shift. As an integrated, validated infrastructure with built-in data services, FlexPod AI enables organizations to run AI workloads where their data already lives, without rearchitecting their environment.

Here’s how that comes together:

Before enterprises can run AI at scale, their data has to be ready and most of it isn’t. Often data is unstructured, fragmented across systems, lacking metadata enrichment, and governed inconsistently. This introduces friction, as teams spend more time locating and preparing data than training models, and pipelines break when data moves between environments.

The traditional approach has been to centralize data and move it to specialized environments where it can be prepared for AI. At scale, this leads to increased latency, costs, and risks while adding new attack surfaces.

A more efficient approach is to bring AI capabilities to the data, enabling teams to:

• Automatically catalog and index data in place
• Enrich data with AI-ready metadata to support and model input pipelines
• Apply governance controls to identify and protect sensitive information without duplicating datasets
• Prepare data for modern workflows such as retrieval-augmented generation (RAG)

FlexPod AI solutions deliver these capabilities by integrating NetApp AI Data Engine (AIDE) into its data layer, enabling organizations to operationalize AI on a unified platform. With AIDE, data is automatically discovered, cataloged, and updated through a metadata view with built-in change detection and synchronization. Organizations can enrich, transform, and prepare data in place using semantic discovery and real-time vectorization, delivering a streamlined data path that eliminates unnecessary data movement and accelerates time-to-value. Meanwhile, policy-driven governance controls protect sensitive information without data movement or duplication.

AI doesn’t just increase demands, it introduces new levels of scale and complexity that traditional IT architectures were not designed to handle. AI and data-intensive workloads require infrastructure that can deliver sustained throughput to GPU compute, scale with massive datasets, and integrate across virtualized and containerized environments. Fragmented infrastructure stacks lead to unstable performance, operational complexity, and long deployment cycles.

However, scaling AI beyond centralized environments introduces additional operational challenges. Edge environments often lack on-site IT expertise, rely on inconsistent infrastructure deployments, and make it difficult to enforce consistent management and policies or synchronize data with core and cloud environments.

Validated, integrated architectures mitigate these risks. With Cisco Validated Designs (CVDs) and NetApp Verified Architectures (NVAs), FlexPod delivers a lab-validated, end-to-end platform that integrates:

• GPU-accelerated compute
• High-bandwidth, low-latency networking
• Scalable, high-performance storage across distributed locations

The result is flexible, predictable performance without bottlenecks, so IT teams can:

• Deploy faster with trusted configurations
• Scale with confidence using validated designs
• Reduce operational overhead associated with integration and fine-tuning

Just as importantly, FlexPod helps organizations establish a foundation that delivers the performance and access need to feed AI pipelines, supplying GPU-intensive workloads with the throughput, latency, and reliability needed to support the full AI lifecycle from training and fine-tuning to inferencing and RAG, without adding silos.

Pushed by real-time decision-making needs and bandwidth constraints, AI workloads are moving to the edge, which requires a consistent security and governance model that protects data and enables access.  Inferencing is being deployed closer to where data is created, such as retail locations, manufacturing facilities, healthcare environments, and remote offices.

Organizations need consistent access controls and governance across environments, enabling multiple teams and workloads to securely share infrastructure while maintaining isolation where needed. Combined with unified management and automation across compute, network, and storage resources, this approach allows organizations to confidently deploy AI applications, including emerging agent-driven workflows, knowing security, compliance, and data protection are enforced consistently across the entire environment.

FlexPod AI helps organizations deploy and scale AI workloads securely by combining a validated, architecture with built-in data protection, access controls, and governance capabilities. FlexPod Edge extends the proven capabilities of FlexPod to these environments by delivering:

• Standardized, converged compute, networking, and storage in a validated architecture
• Centralized fleet management and policy-based automation
• Zero-touch deployment to enable repeatability at scale
• Integrated security with encryption, secure boot, and zero-trust principles

With FlexPod Edge, teams can run AI inferencing and data-intensive workloads locally, while enforcing consistent security policies, controlling access, and maintaining alignment with enterprise-wide governance and operational standards. Data created at the edge can be securely synchronized with core and cloud environments, enabling a unified data strategy across the organization.

AI outcomes are only as secure and reliable as the systems that support them. FlexPod combines NetApp, Cisco, and NVIDIA technologies to deliver a modern AI platform with AI-ready data, on a trusted, validated architecture, from core to edge. This unified portfolio supports organizations at every stage of their AI journey:

• FlexPod AI with NetApp^® AFX, NetApp AI Data Engine (AIDE), and Cisco Secure AI Factory enables large-scale, secure AI pipelines by integrating data preparation, high-performance infrastructure, and end-to-end security
• FlexPod AI Mini provides a streamlined, cost-effective entry point for AI inferencing and RAG workloads, reducing barriers to adoption
• FlexPod Edge extends AI capabilities to remote and edge environments, enabling real-time AI where it matters most

Across these solutions, FlexPod builds on its track record as a secure, validated converged infrastructure that help organizations achieve business outcomes, reduce operational complexity, and accelerate time-to-value.

The organizations that successfully advance AI initiatives are those that can readily access and operationalize their data simply and securely through an integrated architecture. To move from pilot to production at scale, they need infrastructure designed with security at the foundation, optimized for AI pipelines, and built for distributed environments. FlexPod AI and FlexPod Edge deliver exactly that, helping enterprises move beyond experimentation and realize the full value of AI.

Contact your NetApp or Cisco representative to learn how FlexPod can accelerate your path from AI pilot to production.

Explore the latest FlexPod validated solutions built for of your workloads or meet in-person with FlexPod experts at the NetApp booth #7414 at Cisco Live US today.