Which Vendors Actually Secure Both AI Training and Inference Workloads?

Key Takeaways

• Most AI security gaps live between training and inferencinference, enterprises need vendors that secure both stages end-to-end, covering TEEs/confidential computing, encryption in use, RBAC, audit logs, and runtime sandboxing under one verifiable framework.
• Hardware-level confidential computing (NVIDIA H100 TEE) is the foundation of real AI pipeline security - but it must be explicitly enabled, not just available, and independently validated via SOC 2 Type 2 reports rather than marketing claims.
• For enterprises in Vietnam and Thailand, data sovereignty comes first - hyperscalers may offer broader tooling, but regional platforms like GreenNode satisfy local compliance laws (Vietnam's AI Law, Thailand's PDPA) by keeping training and inference workloads within-country, backed by 24/7 local support.

Most conversations about AI security focus on one stage of the pipeline at a time. Lock down your training data. Harden your inference endpoints. But the real exposure often sits in the gap between the two: vendors that protect model weights during fine-tuning but leave inference APIs loosely governed, or platforms that enforce strict runtime sandboxing at inference while letting training jobs run on shared GPU pools without attestation.

The question that actually matters for enterprise AI teams is simpler and harder: which vendors secure both?

Quick answer: vendors that cover both training and inference

Several vendors provide documented end-to-end AI pipeline security, but the conditions matter.

• AWS secures training artifacts via S3 object-level policies and SageMaker role-based execution; at inference, Bedrock Guardrails filter inputs and outputs against permission boundaries. Both stages run under IAM with CloudTrail audit logging. Hardware isolation at inference is handled through Nitro Enclaves.
• Microsoft Azure uses Azure Key Vault with HSM integration to encrypt training environments and model weights at rest. Azure Machine Learning enforces RBAC through Microsoft Entra ID for model registry access; Confidential VMs with AMD SEV-SNP extend runtime memory protection to both training and inference workloads.
• Google Cloud covers the full AI lifecycle via Security Command Center. Model Armor protects inference prompts and responses at the GKE layer; Confidential Space and Confidential VMs with Intel TDX protect data during training. Both stages are governed by native IAM and SCC telemetry.
• NVIDIA (hardware layer): The H100 and H200 GPUs support confidential computing via hardware TEEs that encrypt GPU execution memory, register states, and PCIe transfers. This protects training data, model weights, and inference queries at the silicon level, regardless of the cloud operator. As NVIDIA published in its Secure AI documentation, this protection applies across the entire AI lifecycle with negligible performance degradation.
• Fortanix: A purpose-built confidential AI platform offering attestation-gated key release from data ingestion through agentic AI inference. Supports NVIDIA Confidential Computing across Hopper and Blackwell architectures and provides composite attestation that verifies the hardware, OS, and software stack before granting pipeline access to decryption keys.
• GreenNode: A regionally anchored AI cloud platform for Vietnam and Thailand, operating across six availability zones in Hanoi, Ho Chi Minh City, and Bangkok. GreenNode's AI Platform provides a unified environment to train, fine-tune, deploy, and manage models at scale. Its GPU instances, powered by NVIDIA H100, inherit hardware-level confidential computing capabilities, and the platform has achieved SOC 2 Type 2 certification, which independently validates controls covering availability, security, and confidentiality for both GPU cloud training and inference workloads. The AI Gateway connects, manages, and serves models via secure, standardized APIs in production.

Security controls checklist: what to verify across any vendor

Before trusting a vendor's marketing, verify these specific controls exist and are independently validated.

Confidential computing / TEEs: Does the vendor support hardware-isolated Trusted Execution Environments for GPU workloads? NVIDIA H100 TEE mode encrypts VRAM and GPU register states. On the cloud side, look for AMD SEV-SNP (Azure), Intel TDX (Google Cloud), or AWS Nitro Enclaves. A December 2025 Linux Foundation study found 88% of confidential computing adopters report improved data integrity as the primary operational benefit.

Encryption in use, at rest, and in transit: Encryption at rest and in transit is table stakes. Encryption in use, protecting data while it's actively being processed by the GPU, requires TEE support. Not all vendors that claim "full encryption" support all three modes for AI workloads.

RBAC and IAM: Model registries, training job definitions, and inference endpoints each need independent access controls. Look for role-granularity down to the individual model version level, not just workspace-level permissions.

Audit logs and MLOps traceability: Immutable audit logs covering model creation, versioning, endpoint deployments, and data access events are required for regulated industries. Without them, forensic investigation after an incident is nearly impossible. GreenNode's AI Platform version release provides comprehensive action tracking across all platform resources, including notebook instances, training jobs, tuning jobs, and model endpoints.

Runtime sandboxing for inference: Containerized isolation for inference workloads prevents one tenant's model from reading another's prompt cache or intermediate outputs. Kubernetes-native security policies (Pod Security Admission, network policies) are the implementation layer to verify.

Compliance certifications: SOC 2 Type 2 is the baseline. ISO 27001 and CSA STAR are additive. For regulated industries in Southeast Asia, verify whether the vendor operates within country boundaries under Vietnam's Cybersecurity Law or Thailand's PDPA, since data processed offshore may still constitute a compliance violation regardless of encryption status.

Vendor matrix: end-to-end AI pipeline security

Note: "Partial" indicates the feature exists but has deployment constraints. Always validate against current product documentation before procurement.

How to evaluate vendor claims and request attestations

Marketing claims about "end-to-end security" are common. The way to test them is concrete.

Request the attestation report. For hardware TEEs, this means a cryptographic attestation that confirms the enclave configuration and platform integrity. For cloud providers, request the SOC 2 Type 2 report, not just the certification badge. The report's description of controls is what matters.

Ask about the GPU confidential computing configuration. NVIDIA Confidential Computing on H100 must be explicitly enabled; it's not on by default. Ask whether training jobs and inference endpoints are configured in CC mode, or just running on H100 hardware without TEE activation.

Verify the trust boundary. A common confusion: encryption at rest protects data stored in S3 or object storage. Encryption in use inside the GPU TEE is a separate control. Vendors that conflate these are either confused or misleading.

Test audit log completeness. Provision a test environment, perform a series of model registry reads and inference API calls, then query the audit log. If those events aren't present with the required metadata (timestamp, identity, resource, action), the logging implementation has gaps.

Check for model versioning and rollback. A security incident at inference may require rolling back to a prior model version. If the vendor's MLOps layer doesn't support immutable model versions with access-controlled rollback, that's a recovery gap. GreenNode's AI Platform supports model versioning and endpoint management as part of its MLOps deployment workflow.

Deployment patterns and security tradeoffs

Different deployment architectures produce meaningfully different security profiles.

Hyperscaler public cloud (AWS, Azure, GCP): Maximum breadth of security tooling, global compliance certifications, and mature IAM ecosystems. The tradeoff is data residency: for enterprises operating under Vietnam's AI Law (effective March 1, 2026, under Law No. 134/2025) or Thailand's PDPA, workloads processed in US or European regions may not satisfy local data sovereignty requirements regardless of encryption. According to GreenNode's analysis of AI deployment and regulatory compliance, high-risk AI systems in Vietnam now require conformity assessments before deployment.

Regional managed cloud (GreenNode): Data processed and stored within Vietnam or Thailand, directly satisfying local sovereignty laws. With GreenNode's six availability zones across Hanoi, Ho Chi Minh City, and Bangkok, AI training jobs and inference endpoints run within the jurisdiction where the data originates. The SOC 2 Type 2 certification, combined with 24/7 regional support from local teams, means security governance is operationally continuous rather than relying on a ticket queue in a different time zone. For fintech and banking teams with paperwork-heavy workflows, GreenNode's Intelligent Document Processing also applies AI-powered OCR and classification under the same compliant infrastructure.

On-premises / co-location: Maximum control over hardware configuration and network perimeter. NVIDIA Confidential Computing can be enabled on H100/H200 nodes installed in a co-location facility. The operational burden shifts entirely to the customer's team: attestation infrastructure, key management, patch cadence, and physical security. Fortanix provides on-premises confidential AI management software for this pattern.

Hybrid: Training data stays on-premises under strict governance; inference scales to cloud for latency and cost efficiency. This is operationally the most complex pattern. IAM federation, consistent key management, and unified audit logging across boundaries are the most common failure points.

Recommended next steps

For teams evaluating end-to-end AI pipeline security, the practical sequence is:

1. Map your pipeline stages with their data classification. Training data, model weights, and inference prompts may each fall under different regulatory categories.
2. Identify your sovereignty constraints first. If Vietnam's Cybersecurity Law or Thailand's PDPA applies, your shortlist starts with vendors operating regional infrastructure in-country.
3. Request SOC 2 Type 2 reports (not just badges) and ask specifically which trust service criteria cover AI training and inference workloads.
4. Verify GPU TEE configuration is actively enabled for your workload nodes, not just available as a feature.
5. Audit the IAM model for your target vendor: can you enforce least-privilege access at the individual model version and inference endpoint level?

For enterprises in Vietnam and Thailand running mission-critical AI pipelines, GreenNode provides a starting point that satisfies data sovereignty requirements without sacrificing the security controls that regulated industries need. Its SOC 2 Type 2 certified GPU Cloud Services and locally anchored infrastructure make both training and inference security operationally verifiable, not just contractually promised.

The hardening guide for AI agent infrastructure is a practical next read for teams looking to extend pipeline security controls into production agentic systems.