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Secure AI Infrastructure: 5 Things You Should Never Compromise On
As 45% of enterprises cite security as their top constraint for scaling AI, organizations must prioritize five non-negotiables or risk turning innovation into exposure.
August 30, 2025
By Zach Lemley, Vultr
Many organizations are rushing to deploy AI enterprise-wide without truly evaluating the security posture of their infrastructure. From model theft to runaway costs, infrastructure-level vulnerabilities can derail even the most sophisticated AI strategy. The truth is, what you build on matters as much as what you build.
CIOs and CISOs should be asking: How can we best secure AI infrastructure? To deploy AI securely and successfully at scale, organizations must evaluate their infrastructure choices beyond performance and cost. Security, compliance, and operational trust need to be defining factors.
Here are five non-negotiables for leaders investing in enterprise-grade AI infrastructure.
#1: Build on a Foundation That Defends From the Start
Forty-five percent of enterprises cite security and compliance as their top infrastructure constraint for scaling AI, according to recent S&P Global research .
General-purpose clouds weren't designed for today's complex, high-risk AI workloads. And neoclouds may have raw compute power, but enterprise AI demands more: scalable infrastructure, secure pipelines, global availability, regulatory readiness, and seamless integration.
AI-native infrastructure is secure by design, not retrofitted after the fact. Infrastructure partners should, at the very least, be agile and help keep systems current. Look for partners that offer:
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Tenant isolation by default across compute, storage, and networking.
End-to-end encryption for data in transit and at rest.
Private networking that shields sensitive workloads from the public internet.
#2: Coverage Across the AI Pipeline
From ingestion to inference, AI workloads span a complex pipeline of storage, networking, orchestration, and data. Although 69% of organizations cite AI-powered data leaks as a top security concern, half still lack AI-specific security controls, leaving critical infrastructure dangerously exposed. A layered security model ensures protection at each stage, not just at the endpoints.
Look for providers offering:
Granular IAM and RBAC, ideally integrated with identity systems.
Auditability and observability for ephemeral compute, like spot instances or serverless tasks.
Clear shared responsibility models, defining security ownership across the stack.
Without this depth, a single exposed node can compromise the entire pipeline.
#3: Make Confidential Compute the Default
Traditional protections like encryption at rest and in transit don't defend against in-use threats, the sweet spot for attackers during model training and inference. That's where confidential computing comes in.
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Insist on infrastructure that includes:
Secure enclaves and confidential GPUs (e.g., NVIDIA H100 Secure Mode, AMD SEV).
Remote attestation to verify hardware integrity in real time.
Firmware-level validation, ensuring trust from the silicon up.
This is rapidly becoming the baseline: Within the next 12 months, confidential computing will be a de facto standard, especially for sectors handling highly sensitive data, such as healthcare, finance, and government.
#4: Don't Bet It All on One Chip
Zero-day vulnerabilities and supply chain disruptions frequently target specific chipsets, making overreliance on a single GPU vendor a serious liability. When AI models are pinned to one hardware source, they become a single point of failure. It's no surprise that GPU capacity and performance (55%) and compute availability (54%) now rank among the top concerns for organizations scaling AI. To reduce risk and avoid vendor lock-in, enterprises must diversify their infrastructure stack.
Silicon diversity mitigates this by:
Enabling workload portability across AMD, NVIDIA, and other emerging accelerators.
Offering early access to emerging hardware and supporting pre-launch penetration testing to assess isolation boundaries.
Shifting workloads dynamically to avoid downtime when GPU-specific threats emerge.
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#5: Demand Enforceable Compliance
Adherence to established standards is critical. Be wary of infrastructure partners that cannot demonstrate a robust compliance posture. If a provider can't produce a current SOC 2 or ISO 27001 report — at a bare minimum — they should not earn your trust or your AI workloads.
Insist on:
Documented certifications: SOC 2, ISO 27001, GDPR, HIPAA as applicable.
A clean IP fabric and history of infrastructure integrity.
Transparent incident response plans, updated regularly and shared on request.
The Bottom Line: Ask These Questions Before You Scale AI
Modern enterprise AI workloads are fast, sensitive, and unforgiving. Without the right foundation, speed becomes a liability and innovation becomes exposure. Ask yourself:
Is our infrastructure isolated and encrypted by default?
Do we have audit trails for ephemeral AI workloads?
Are our models running on confidential compute?
Can we seamlessly shift off a compromised chip vendor?
Can our infrastructure provider prove compliance today, not just promise it tomorrow?
If you can't confidently answer "yes" to all five, your AI infrastructure may already be your weakest link. But the good news is you don't have to choose between velocity, security, and cost. With the right infrastructure — built for AI and secure by design — you can have all three.
About the author:
Zach Lemley is Chief Information Security Officer at Vultr.
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