SOC 2 is an AICPA attestation over Trust Services Criteria for a stated period or point in time; ISO 27001 is a certification that an information security management system meets a published standard. Buyers ask for both when an AI cluster will hold weights, prompts, or regulated records. The reports are evidence packages. They are not a substitute for GPU tenancy, key control, or a mapped data path.
Read the scope first. A clean logo on a marketing page does not tell you whether training checkpoints, inference logs, or support jump hosts sat inside the audited system. Enterprise AI diligence fails when security reviews stop at the certificate name.
What SOC 2 and ISO 27001 actually attest

SOC 2 is an attestation, usually delivered as a Type I (point in time) or Type II (period of operation) report. The auditor tests controls the provider nominated against Trust Services Criteria such as security, availability, and confidentiality. ISO 27001 certifies that a defined ISMS exists and is operated against ISO/IEC 27001. Complementary statements and control mappings differ by firm and by scope statement.
Neither document is a product warranty for every GPU job you will run next quarter. The useful question is narrower: which systems, locations, and subprocessors were in scope, and which AI assets were out of scope by design.
| Question |
SOC 2 |
ISO 27001 |
| What you receive |
Attestation report, often with a Type II period |
Certificate plus a statement of applicability |
| Who sets the control set |
Provider-defined controls mapped to TSC |
ISMS controls selected against the standard |
| Best buyer use |
Read exceptions, complementary user-entity controls, and period |
Read scope, locations, and which Annex A themes were declared |
| What it does not prove |
That your model, RAG store, or tenant logs were tested |
That a specific GPU node or job was certified |
| Common AI gap |
Inference traces and support access sit outside the system description |
Training data pipelines added after the last surveillance audit |
GPU operations that still sit outside the report
AI infrastructure creates assets that generic cloud reports under-specify. Model weights, adapter files, KV-cache dumps, evaluation sets, and prompt logs can leave the audited boundary through a debug collect, a shared object bucket, or a vendor support session. The report may be accurate for the tickets it tested and still silent on those paths.
Shared responsibility is the rest of the story. Identity, key policy, dataset classification, and which teams may mount a checkpoint are usually customer controls. A Type II that passed last year does not decide who can kubectl exec into a serving pod this week. If you need HIPAA-ready posture, you still need a defined PHI path and contractual scope such as a BAA where one applies. SOC 2 is not HIPAA, and ISO 27001 is not a guarantee of HIPAA compliance.
Private AI infrastructure is the layer that answers tenancy and residency before you debate report logos. OneSource Cloud designs dedicated, non-shared GPU environments with U.S. data-center options so the production system you describe to an auditor is not a public multitenant pool. The report then has a chance to match the architecture you actually run.
Evidence to request before you trust the logo
Ask for artifacts you can replay, not a slide that says “certified.”
- System description or ISMS scope that names GPU clusters, storage, logging, and support access.
- The period of the last Type II or surveillance audit, plus any exceptions that mention production change.
- A data-flow diagram for weights, prompts, retrieval stores, and backups, mapped to in-scope systems.
- The complementary user-entity controls you must operate, written in language your platform team can own.
- A sample of how a failed control, key rotation, or access review would appear in evidence you can store.
Healthcare and financial reviewers should keep the same vocabulary they already use: HIPAA-ready design, shared responsibility, evidence an auditor can reopen. Healthcare AI infrastructure reviews fail when the packet is only a SOC 2 PDF and never a PHI path. If you also need someone to keep patches, access reviews, and capacity changes from drifting out of the described system, pair the report with managed AI infrastructure.
FAQ
Is SOC 2 the same as ISO 27001 for an AI cloud?
No. SOC 2 is an attestation over a provider’s stated controls and a period or point in time. ISO 27001 certifies an ISMS against a standard. Many serious providers hold both. They still answer different questions. Read scope, exceptions, and complementary customer controls instead of treating the two logos as interchangeable stamps.
Does a SOC 2 Type II mean my LLM workload is in scope?
Only if the system description includes the services, regions, and data stores your workload uses. A report can be clean for a control plane and silent on GPU nodes, object storage for checkpoints, or a separate logging tenant. Map your architecture to the description before you treat the Type II as coverage.
Do these reports replace a HIPAA business associate agreement?
No. A BAA is a contractual allocation of HIPAA obligations where one applies. SOC 2 and ISO 27001 can support a security narrative. They do not make a platform guaranteed HIPAA compliant, and they do not define the PHI path through prompts, retrieval, or fine-tuning data.
How often should we re-review an AI infrastructure provider’s reports?
Re-read when the report period ends, when you add a new data store or region, and when the provider changes subprocessors or support access. AI platforms add features faster than audit cycles. A surveillance certificate can lag a new inference logging pipeline by months.
What should a private GPU buyer do that a report cannot?
Decide tenancy, residency, key custody, and who can reach the box. Those choices determine whether the audited system is the system you run. Dedicated U.S. environments, such as those OneSource Cloud operates, make that mapping shorter than a shared public pool with many undocumented hop-offs.
Summary
SOC 2 and ISO 27001 are useful when you read scope, exceptions, and shared-responsibility gaps. They are weak when used as a substitute for GPU isolation and a mapped AI data path. Start with the architecture you can explain, then attach reports that match it. If you need that architecture as a dedicated U.S. environment, review OneSource Cloud private AI infrastructure before you close diligence on logos alone.