Enterprise AI refers to the use of artificial intelligence technologies, including model fine-tuning, inferencing, and retrieval-augmented generation (RAG), to accelerate insights and innovation from data at an organizational scale. Successfully deploying and scaling enterprise AI requires a modern data pipeline built on high-performance AI infrastructure, particularly AI storage optimized for large volumes of unstructured data.

AI infrastructure refers to modern IT architectures engineered to meet the demands of artificial intelligence workloads and data pipelines. AI and machine learning processes involve massive data volumes and sophisticated computational models, which require high-performance, highly secure hybrid storage resources and streamlined data management.

Enterprise AI security addresses the data protection challenges that arise when AI workloads access large volumes of data across on-premises and cloud environments. It encompasses ransomware detection, disaster recovery, data access controls, and governance frameworks to safeguard AI data pipelines end to end.

NetApp storage solutions are certified for use with NVIDIA DGX SuperPOD, the reference architecture for large-scale AI training. NetApp's high-performance file storage integrates directly into the DGX SuperPOD design to provide the low-latency, high-throughput data access that GPU-intensive AI training workloads require. This certification means organizations can deploy NetApp with confidence in validated, production-ready configurations.

NetApp protects AI training data across hybrid environments using a layered approach: FIPS 140–3 validated storage encryption, real-time ransomware detection with automated response, immutable Snapshot copies for rapid recovery, and role-based access controls enforced consistently across on-premises and cloud deployments. NetApp also supports compliance with frameworks including SOC 2, ISO 27001, FedRAMP, and HIPAA, so enterprises can meet regulatory requirements without slowing down AI operations.

NetApp ONTAP, the operating system underlying NetApp's data platform, provides native first-party integrations with AWS, Microsoft Azure, and Google Cloud. This means data can move seamlessly between on-premises storage and any of the three major public clouds without requiring third-party connectors or manual migration. NetApp's unified data management layer ensures consistent data access, governance, and performance regardless of where workloads run.

Legacy infrastructure, including storage systems not designed for AI data pipelines, creates bottlenecks that prevent AI workloads from scaling beyond pilots. Modernizing with NetApp provides the high-throughput, low-latency storage AI requires, integrates with cloud environments to give teams access to the latest AI tools, and enables a storage-as-a-service consumption model through NetApp Keystone, so organizations can scale capacity on demand without over provisioning.