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advisory@euroshield.tech
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+41-793070650
advisory@euroshield.tech
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AI Workload & GPU Cluster Security

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"Prevention is cheaper than a breach"

AI Workload & GPU Cluster Security — Securing the Infrastructure That Trains the Models.

99.9%

Threat detection and prevention rate

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EuroShield advises hyperscale operators, sovereign AI programmes, colocation providers hosting AI tenants, GPU-cloud developers, and institutional investors on the security architecture of AI training and inference infrastructure. We are engaged as independent advisor — on the owner’s or tenant’s side of the table — across design review, fabric architecture, tenant-isolation strategy, supply-chain integrity, operational security, and board-grade risk governance.
The AI-infrastructure layer is now a distinct security domain. The economics of training a frontier model, the sovereign-strategic weight of sustained GPU capacity, the concentrated value of model weights and training data, and the opacity of the GPU and interconnect supply chain have produced a threat surface that did not exist three build cycles ago. Attacks on this layer are no longer theoretical: model-weight exfiltration, training-data poisoning, inference-pipeline manipulation, tenant-to-tenant side-channel exposure on shared fabric, and compromised firmware on GPUs and switches are all now documented real-world concerns.
Work is aligned to IEC 62443 where AI infrastructure is deployed inside industrial or regulated environments, ISO/IEC 27001 and 27019, ISO/IEC 27090 (AI security guidance), NIST AI RMF 1.0 and NIST SP 800-218A, the EU AI Act (Regulation 2024/1689), the EU Cyber Resilience Act for in-scope hardware and firmware, NIS2 Article 21, and evolving guidance from ENISA, NCSC, BSI, and ANSSI on AI-system security. For sovereign programmes, national AI-infrastructure frameworks (IndiaAI, UAE NAS, Saudi SDAIA, Swiss federal AI strategy) are integrated as design inputs.
Vendor-neutral, by commercial structure. We do not resell GPUs, networking hardware, AI-infrastructure software, or managed AI services. NVIDIA (DGX, HGX, BlueField DPUs, Spectrum-X, Quantum InfiniBand), AMD (Instinct), Intel (Gaudi), Broadcom (Tomahawk, Jericho), Arista, Cisco, Juniper, Marvell, Supermicro, Dell (PowerEdge XE), HPE Cray, Lenovo ThinkSystem, and adjacent platforms are evaluated on merit.

Why AI Infrastructure Is Its Own Security Domain

Concentrated value at rest and in training. A frontier model's weights, a bespoke fine-tuned enterprise model, or a proprietary training-data corpus can represent hundreds of millions of euros of capital committed — concentrated in assets of modest file size. The extraction economics favour a determined adversary.

Shared-fabric side channels. GPU interconnects (NVLink, InfiniBand, PCIe, CXL), shared NICs, smart-NIC DPUs, and multi-tenant orchestration layers introduce side channels — cache, memory, fabric, and power — that mature IT isolation controls do not address by default.

Opaque hardware and firmware supply chain. GPU boards, BMCs, cable assemblies, optical transceivers, switch ASICs, and associated firmware travel through long, often opaque supply chains. Tamper, counterfeiting, and firmware-implant risk is elevated.

Regulatory convergence under AI Act and CRA. The EU AI Act imposes security and robustness obligations on high-risk AI systems; CRA imposes vulnerability handling and disclosure on products with digital elements; NIS2 covers AI-infrastructure operators as essential entities.

AI-Infrastructure Threat Modelling & Risk Assessment

  • AI-specific threat modelling: model extraction, weight exfiltration, training-data poisoning, model-supply-chain compromise, inference-pipeline manipulation, prompt-injection at the infrastructure layer
  • Tenant-isolation risk assessment on multi-tenant GPU clusters — side-channel, covert-channel, and direct-access vectors quantified against workload sensitivity
  • Supply-chain risk modelling across GPU, interconnect, network, storage, and management-plane components — including geographic and export-control considerations
  • Insider-threat and privileged-access risk for AI operators, cluster administrators, and research-access users
  • EU AI Act risk classification of hosted AI systems and the security obligations that flow to the infrastructure layer

GPU Fabric & Cluster Architecture Review

  • GPU fabric architecture assessment: NVLink, InfiniBand, RoCE, PCIe, CXL topology review against performance, isolation, and failure-mode requirements
  • Multi-tenant fabric segmentation: partition strategies, network isolation, tenant-scoped storage, management-plane separation
  • Smart-NIC and DPU architecture review: BlueField, Pensando, and equivalents as security control planes
  • Management-plane architecture: BMC, iLO, iDRAC, DC-SCM, Redfish, and out-of-band access — the layer most frequently under-secured
  • Storage architecture for training data, checkpoints, and model artefacts: access control, encryption, tenant-scoping, audit-trail integrity
  • Orchestration and scheduler security: Kubernetes, Slurm, Run.ai, Determined AI

Tenant Isolation on Shared AI Infrastructure

  • Hard-tenancy, soft-tenancy, and multi-instance-GPU (MIG) architectures evaluated against tenant trust model
  • Cryptographic tenant isolation: confidential computing on GPU (NVIDIA CC, AMD SEV, Intel TDX where applicable), attestation architecture, key-management design
  • Fabric-level isolation: InfiniBand partitioning, VLAN/VXLAN segmentation, NVLink partitioning, firewall enforcement between tenant domains
  • Side-channel analysis: power, cache, memory, interconnect, and thermal side-channel exposure assessed against realistic adversary capability
  • Sovereign-isolation and data-residency architecture for regulated and national-security-sensitive tenants

Model & Data Integrity

  • Model-weight protection at rest: encryption, HSM integration, access control, tamper-evident storage
  • Model-weight protection in transit: secure model-loading pipelines, cryptographic attestation, integrity verification against training provenance
  • Training-data integrity: provenance tracking, poisoning-detection strategy, audit-trail architecture
  • AI-BOM (AI bill-of-materials) design: documented provenance for base models, training data, fine-tuning data, and dependency chain
Hardware & Firmware Supply-Chain Integrity

GPU, switch, NIC, cable, optical, and BMC supply-chain risk assessment
Tamper-evidence and receipt-inspection protocols for high-value hardware
Firmware integrity verification: GPU, BMC, switch, NIC firmware against vendor signatures and known-good baselines
SBOM and HBOM (hardware bill-of-materials) collection and verification
Secure-boot, measured-boot, and attestation architecture across the cluster
EU CRA vulnerability-handling and disclosure obligations for in-scope components
National regulatory disclosure: BSI and BAFA (DE), ADEME (FR), BFE (CH), UAE Net Zero 2050, Saudi Green Initiative, India BEE reporting
Operational Security & Incident Response

AI-specific SOC use-cases: anomaly detection on fabric traffic, abnormal model-access patterns, unexpected weight-movement
AI-specific incident response: model-compromise playbooks, training-job-integrity response, weight-exfiltration scenarios
Tabletop exercises for AI-infrastructure scenarios: state-actor model-extraction attempt, insider-driven weight exfiltration, supply-chain firmware implant

Outcome

An owner or tenant leaves a EuroShield AI-workload security engagement with
A threat model that reflects AI-infrastructure reality — model extraction, weight exfiltration, training-data poisoning, and shared-fabric side channels named, calibrated, and owned.
Tenant isolation engineered, not asserted — hard-tenancy, cryptographic, fabric-level, and side-channel isolation designed against the trust model of the tenant mix.
Model and data integrity protected end-to-end — weights, training data, fine-tuning artefacts, and inference pipelines protected at rest, in transit, and in use.
A supply-chain integrity posture that survives scrutiny — SBOM, HBOM, firmware verification, and tamper-evidence protocols engineered against export-control, sanctions, and sovereign-programme requirements.
Detection content that sees AI-specific attacks — fabric anomalies, weight-access anomalies, orchestration-layer abuse, and model-integrity deviations.
Regulatory posture aligned across AI Act, CRA, and NIS2 — one compliance surface, one evidence chain, one board-grade risk narrative.
A commercial position the owner or tenant defends — AI-infrastructure risk named, quantified, and governed at the level its consequence class requires.
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