IDC Spotlight: Data delivery requirements for AI training and inferencing

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The evolving AI landscape demands infrastructure tailored for training and inference, with seamless coordination among network, infrastructure, cloud, AI, and storage teams, alongside standardized environments to ensure cost efficiency and effective deployment. As organizations shift from experimental AI approaches to structured, scalable deployments, this spotlight highlights the increasing importance of flexible architectures, robust security, and high performance across hybrid multicloud environments. Key topics include end-to-end encryption, advanced authentication, observability, and defenses against threats like DDoS attacks and data poisoning.

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Scalable, flexible AI infrastructure:
Organizations are transitioning from experimental AI projects to structured, repeatable deployments, emphasizing architectures that can scale and adapt to both AI training and production workloads across diverse cloud and hybrid environments.
Focus on security and performance:
There is an increasing demand for robust security measures—including end-to-end encryption, authentication, and threat protection—to ensure optimal performance and safe delivery of AI data across distributed platforms.
Integrations and operational coordination:
Successful AI implementation relies on breaking down IT silos and coordinating efforts among network, cloud, infrastructure, and storage teams, with unified platforms supporting secure, standardized environments for distributed AI applications.

IDC Spotlight, AI Training and Inference Infrastructure for Applications and Data Requires Optimized Security and Performance, Sponsored by F5. Doc. #US53901525, Publication Date (November 11, 2025)

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