Data readiness and securing hybrid storage as NetApp introduces a system to manage artificial intelligence workloads at LEAP 2026

As enterprise artificial intelligence projects shift from limited experimentation to large-scale production, the real operational dilemma for companies is moving from model selection to whether data infrastructure can feed these models securely and smoothly. NetApp, an intelligent data infrastructure provider, announced its participation in the LEAP 2026 conference at the Riyadh Exhibition and Convention Center in Malham, showcasing its unified storage and data protection platform designed to support advanced workloads across hybrid and multicloud environments.

Storage integration and access governance now serve as the foundation for turning enterprise data repositories into immediate operational value for AI models.Suhail Hasanain, senior regional director and general manager for NetApp in the Middle East and Africa, explains that competitive advantage now hinges on the speed of accessing, governing, protecting, and delivering data to AI wherever it resides, particularly as accelerating investments reshape the infrastructure landscape globally and in Saudi Arabia, coinciding with the kingdom designating 2026 as the Year of AI.

The rapid adoption of AI solutions has dispersed enterprise data across multiple platforms, creating operational complexity around centralized management and information security. The showcased solutions focus on proactive defense, using AI itself to detect threats autonomously and accelerate recovery from outages. Saeed Al-Zahrani, regional director for NetApp in Saudi Arabia, noted that securing data is no longer confined to perimeter defense, but now requires embedding autonomous safeguards directly within the data layer to ensure business continuity.

This technical shift carries direct implications for engineering teams and digital transformation leaders across the Gulf and the Middle East, requiring a comprehensive review of logging practices and workload processing. In practice, relying on unified storage platforms across multicloud environments reduces the need to build complex data pipelines between different clouds, lowering public cloud consumption bills and mitigating data leak risks when feeding sensitive business data into generative models.

Operational standards across the region are shifting toward strict governance over data pipelines, requiring technology leaders to balance model training and inference requirements with security policies that guarantee immediate system recovery during failures. They emphasize that deploying cutting-edge models loses its operational value without an infrastructure capable of managing and securing data where it rests.