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"AI Data Center & AI Factory Security Blueprint"

Securing AI data centers requires protection across GPU clusters, Kubernetes workloads, and inference APIs. This white paper explores a layered security architecture addressing prompt injection, lateral movement, data poisoning, and compliance risks in private LLM environments. Read the full white paper for the blueprint.

As enterprises deploy private AI infrastructure and large language models, they face new risks that traditional security frameworks can't address. AI data centers are vulnerable to training data poisoning, model theft, prompt injection, lateral movement in GPU clusters, and compliance challenges under government and industry regulations like the EU AI Act, GDPR, PCI-DSS, etc.

This white paper outlines a security blueprint for AI data centers, AI factories, and neoclouds, including:

• Applying Zero Trust to AI workloads, APIs, and Kubernetes
• Securing GPUs with hardware-embedded DPU-level firewalls
• Aligning AI security with NIST AI RMF and Gartner AI TRiSM

Read the security blueprint white paper for key insights and guidance

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