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Short answer: Start with AWS, Microsoft Azure, Google Cloud and OpenAI-managed services, then eliminate any option that cannot document controls for your exact workload, data classes, regions and legal obligations. A familiar cloud logo or a broad certification is not proof that your AI solution is compliant. The deciding evidence is the product-level scope, contract language and responsibility split you can verify.
Use a written control matrix to record what the provider secures, what your team must configure, which audit reports cover the selected AI service and region, how data is retained or deleted, and how incidents are handled. This process also exposes operational safety gaps—weak identity controls, untested model behavior, missing logs or unreviewed changes—that a compliance badge alone will not reveal.
What “safe and compliant” means for an AI workload
Safety and compliance overlap, but they are not the same decision. Compliance asks whether a service and your implementation meet obligations such as HIPAA, GDPR, PCI DSS or FedRAMP. Safety asks whether the system behaves acceptably in practice: access is restricted, misuse is detected, model behavior is evaluated, sensitive data is protected and changes can be investigated or reversed.
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Certification is evidence, not a compliance transfer
Attestations apply to defined systems, services, locations and time periods. A SOC report may cover a provider’s control environment without covering every model, API, region or feature you plan to use. ISO certificates likewise identify a certified scope. HIPAA requires a covered-entity or business-associate arrangement and appropriate safeguards; simply running code on a cloud platform does not make the application compliant.
Microsoft’s HIPAA guidance states that using Azure or another Microsoft cloud service does not automatically make a customer solution compliant. The same principle applies to every provider: your architecture, configuration, workforce procedures and vendors remain part of the compliance boundary.
Safety is an operating capability
Ask how a provider evaluates models before release, tests for adversarial behavior, detects abuse, records administrative actions and communicates material changes. Also ask what you can configure yourself. A managed model may offer provider-run filtering and monitoring, while you still own identity, prompt and retrieval controls, data classification, human review and incident response.
Define the workload before comparing providers
Write a one-page workload brief before requesting proposals. It prevents a provider’s marketing categories from driving a decision that should be based on your risk profile.
Classify the data
- Public: information that may be processed without confidentiality restrictions.
- Internal: business information that needs authenticated access and ordinary retention controls.
- Confidential: trade secrets, source code, financial records or customer information requiring stricter access, encryption and deletion rules.
- Regulated or sensitive: protected health information, payment data, children’s data, precise location, biometrics or other data governed by a specific law or contract.
For each class, state whether prompts, uploaded files, embeddings, fine-tuning data, logs and generated outputs may contain it. Treat vector indexes, cached responses and evaluation datasets as data stores, not as disposable model plumbing.
Record jurisdictions and residency requirements
List the countries or states where data subjects, users, administrators and support staff are located. Specify where data may be stored, processed, backed up and accessed for support. “Regional availability” can differ between a model endpoint, logging service, safety service and identity system, so request a service-by-service answer.
Describe the AI pattern
Identify whether you need a hosted API, a model deployed in your tenant, fine-tuning, retrieval-augmented generation, an autonomous agent, batch inference or a combination. Note integration points such as object storage, databases, vector search, identity providers, software-development pipelines and monitoring tools. Each added service can change the compliance scope and responsibility boundary.
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Set prohibited uses and human-oversight rules
Define decisions that the system must not make alone—for example, employment, credit, insurance, medical treatment or access to essential services. Specify when a trained person must review an output, how disagreements are recorded and how users can appeal. Confirm that the provider’s acceptable-use and abuse policies permit your intended use; a technically available model can still be contractually unsuitable.
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Map the shared-responsibility boundary
All four candidates describe security as shared responsibility, but the boundary moves with the deployment model. AWS summarizes it as the customer securing “security in the cloud” while AWS secures “security of the cloud.” Google Cloud uses the same basic distinction. Microsoft notes that AI risk changes between SaaS, Azure OpenAI and other deployment types. With a managed AI product, the provider operates more of the stack, but you never outsource your obligation to configure and use it correctly.
| Provider | Provider-side responsibilities described in its materials | Controls your team still owns | Boundary questions to document |
|---|---|---|---|
| AWS | Protection of the underlying infrastructure and managed service components within the stated service scope. | Data, applications, operating systems where applicable, firewall configuration, encryption choices, traffic protection, identities and workload configuration. | Which AI and adjacent services are covered; which regional controls are available; who configures logging, keys, network paths and model access. |
| Microsoft Azure | Safeguards for in-scope Azure services and platform operations; Microsoft describes a responsible-AI standard and shared AI-risk management. | Your solution design, identities, data handling, prompts, integrations, human oversight and settings. The split changes between SaaS, Azure OpenAI and other deployments. | Is the exact service in the HIPAA or other compliance scope? Which controls are inherited, and which must your team implement and test? |
| Google Cloud | Underlying infrastructure and platform security within the documented service boundary. | What you bring to the cloud: data, applications, access policies, configurations, keys, network controls and use of model outputs. | Does the selected Gemini or other AI product, edition and region have the required attestation? Which connected services fall outside it? |
| OpenAI-managed business services | Operation of the managed product, documented security controls, audit logging features, data-residency options and provider-run safety work within the product scope. | Account and workspace administration, user permissions, data submitted, retention choices, integrations, output review and your own legal and policy controls. | Which product and region are covered; what retention and deletion settings apply; whether a BAA is available for your product; how logs and connected systems are handled. |
Turn this table into an architecture record with a named owner for every control. If nobody can explain how a control is tested, it is not complete merely because a provider lists it on a trust page.
Compare audit evidence and certification scope
Request current, downloadable evidence rather than relying on a general compliance logo. The evidence package should identify the legal entity, service names, regions, assessment period, exceptions and complementary customer controls.
| Evidence or obligation | What it can demonstrate | What it cannot demonstrate by itself | Your verification question |
|---|---|---|---|
| SOC 1, SOC 2 or SOC 3 | Independent examination of specified controls; SOC 2 is commonly focused on security and related trust criteria. | That your configuration is correct, every AI feature is included or a law’s requirements are fully met. | What exact product, subservice organization, region and period are in scope, and what customer controls are assumed? |
| ISO/IEC 27001, 27017, 27018 or 27701 | A certified management system or privacy and cloud-security practices within the certificate scope. | Automatic compliance for your application, a guarantee of model behavior or coverage of an unlisted service. | Can the provider supply the certificate, statement of applicability and service/region scope? |
| HIPAA and a BAA | Contractual and operational support for handling protected health information when the selected services are in scope. | Compliance of your workflows, workforce, configurations or vendors outside the covered service. | Is the exact AI service named as eligible, and will the provider execute a BAA for your account and use case? |
| GDPR support and a DPA | Contract terms, processing roles, transfer mechanisms and privacy commitments relevant to European data protection. | That your lawful basis, notices, data minimization, rights handling or cross-border assessment is complete. | Who is controller or processor for each flow, where are subprocessors listed, and how are deletion and access requests handled? |
| FedRAMP, PCI DSS or sector-specific programs | Evidence that defined services meet the program’s requirements at a specified authorization or assessment level. | Coverage of every model, region, connector or workload design. | Does the authorization list the exact service and deployment, and are your customer controls documented? |
Google Cloud’s trust materials list ISO/IEC 27001, 27017, 27018 and 27701, SOC 1/2/3, PCI DSS, FedRAMP resources, GDPR alignment and HIPAA resources. Gemini Enterprise documentation describes product-specific certification support; check the edition and region rather than inferring coverage from the broader Google Cloud portfolio.
OpenAI reports an independent SOC 2 Type 2 examination and ISO/IEC 27001:2022 and 27701:2019 certifications for systems supporting its API and business products. Confirm that the particular product, region and processing mode you will buy are inside the stated scope.
AWS provides a control catalog and service-specific compliance information. Use the service list and regional availability, not the overall AWS certification portfolio, to determine what your architecture can inherit.
Examine data governance in detail
Training and secondary use
Obtain a written answer about whether prompts, files, fine-tuning data, embeddings, metadata and outputs are used to train or improve models. OpenAI states that business products do not use organizational data for training by default. Verify the product terms and any opt-in settings, and require equivalent clarity from other providers.
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Document default retention for request content, safety logs, administrative logs, backups and support tickets. Determine whether you can set zero or limited retention, how deletion propagates to replicas and backups, and what legal or security exceptions apply. Test deletion with a sample record and retain the provider’s response as evidence.
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Residency and cross-border access
Separate storage residency from processing residency and from administrator or support access. Ask whether safety classifiers, abuse-monitoring systems, telemetry and subprocessors operate in different locations. If transfer-impact assessments or standard contractual clauses are required, include them in the DPA review.
Encryption and key control
Confirm encryption in transit and at rest, key-management options, rotation, separation of duties and revocation behavior. If your policy requires customer-managed keys, verify that the AI endpoint, logs, vector store and backups all support them; a key option for object storage may not cover a managed model service.
Subprocessors and deletion from connected systems
Review the current subprocessor list and notice period for changes. Map every connector—support desk, storage, observability, identity and payment service—to its own retention and residency terms. Your deletion procedure must cover the complete path, not only the model request.
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Review contracts before technical approval
Ask legal and security reviewers to examine the data-processing addendum, business associate agreement where applicable, service terms, acceptable-use policy and incident-response commitments together. Important clauses include:
- Processing instructions, purposes and restrictions on provider use of your data.
- Retention, deletion, backup and return procedures at termination.
- Subprocessor disclosure, objection rights and change notifications.
- Incident definitions, notification deadlines, cooperation duties and evidence access.
- Audit rights, independent-assessment reports and regulator cooperation.
- Data-residency commitments and approved transfer mechanisms.
- Allocation of intellectual-property, confidentiality and output-related responsibilities.
- Service suspension or abuse-investigation powers that could interrupt a critical workflow.
Healthcare buyers need a BAA and must confirm that the selected AI service is listed as in scope. Microsoft documents a HIPAA BAA for eligible Azure services; OpenAI documents BAA support for applicable healthcare products. Neither statement removes your obligation to configure and operate the workload correctly.
Evaluate operational safety, not just paperwork
Model evaluation and adversarial testing
Ask for evaluation methods covering hallucination, prompt injection, data leakage, unsafe content, bias and tool misuse. Require a test set that reflects your language, domain and edge cases. OpenAI describes adversarial testing and continuing safety monitoring; treat those provider controls as one layer and run your own acceptance tests before release.
Identity and least privilege
Require single sign-on, multifactor authentication, role-based access and short-lived credentials where available. Separate developers, production operators, reviewers and auditors. Restrict which users can upload regulated data, change system prompts, create agents, connect tools or export logs.
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Capture authentication events, administrative changes, model and endpoint identifiers, policy decisions, tool calls, data-access events and user feedback. Verify whether logs include sensitive content, how long they are retained and whether they can be exported to your security information and event-management system. OpenAI documents audit-log capabilities for business products; confirm the exact event types and retention available to your plan.
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Abuse monitoring and guardrails
Understand the provider’s automated abuse detection, rate limits, content filters and escalation process. Then add application-level controls: input validation, retrieval-source allowlists, output schemas, personally identifiable information redaction, transaction limits and human approval for consequential actions. Make guardrails fail closed when a dependency is unavailable.
Change management and rollback
Ask how model versions, safety filters, embeddings and APIs are announced and deprecated. Pin versions when possible, test updates in a staging project and maintain a rollback path. Record who approved each change and which evaluation results supported release.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Provider-by-provider decision notes
AWS
AWS is a practical fit when your organization already operates its regional services, identity model and control catalog there. Its shared-responsibility guidance places infrastructure protection with AWS while leaving customers responsible for data, applications, operating systems where applicable, firewall configuration, encryption and traffic protection. The benefit is a broad set of regional services and established operational skills; the risk is assuming that an inherited platform control covers a customer-configured AI pipeline.
Before selecting AWS, list every AI, storage, network, logging and key-management service in the design. For each one, record the applicable compliance scope, region, customer-side configuration and evidence you will retain. Validate that model endpoints, safety services and observability components meet the same residency and regulatory requirements.
Microsoft Azure
Azure provides HIPAA and HITECH safeguards for in-scope services and offers a HIPAA BAA. Microsoft also states clearly that Azure use alone does not make a solution HIPAA compliant. Its responsible-AI material treats risk as shared and says the division changes between SaaS, Azure OpenAI and other deployment types.
Azure is strongest for teams that can align Microsoft identity, policy, monitoring and data services with the AI workload. Obtain the in-scope service list for the exact region and deployment, then map your application controls—especially data flows, prompt handling, human review and logging—to the BAA and other contractual terms.
Google Cloud
Google Cloud describes security as shared responsibility: Google protects the underlying infrastructure while customers secure what they bring to the cloud. Its trust center lists a wide range of certifications and regulatory resources, but product-specific support still needs verification.
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Choose Google Cloud when its data, analytics, identity and regional capabilities fit your architecture. For Gemini Enterprise or another managed AI product, check the edition, region, connected services and certification scope individually. Do not infer that a platform-level attestation automatically covers every model feature or integration.
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OpenAI-managed services
OpenAI reports SOC 2 Type 2 and ISO/IEC 27001:2022 and 27701:2019 certifications for systems supporting the API and business products. It documents audit logs, data-residency options, fine-grained controls, encryption, adversarial testing and ongoing safety monitoring. Business products are described as not using organizational data for training by default.
OpenAI can fit teams seeking a managed model experience with provider-operated safety systems and minimal infrastructure management. Confirm the exact product, region, retention mode, administrative controls, connected tools and BAA eligibility before processing regulated information. You still own user authorization, data minimization, output validation and decisions made from model responses.
A procurement process that produces defensible evidence
- Define the workload and data classes. List inputs, outputs, logs, embeddings, fine-tuning files, users, jurisdictions, retention limits and prohibited uses.
- Identify mandatory obligations. Mark whether HIPAA, GDPR, PCI DSS, FedRAMP, contractual confidentiality or sector rules apply to each data flow.
- Shortlist compatible services. Compare AWS, Azure, Google Cloud and OpenAI-managed products only where the required model pattern, region and integrations are available.
- Build the responsibility matrix. Assign every control to the provider, your team or both, with an owner and test method.
- Collect current evidence. Request SOC reports, ISO certificates, product compliance matrices, service and region lists, subprocessor disclosures and independent audit details.
- Negotiate terms. Review the DPA, BAA, retention and deletion clauses, residency commitments, incident notices, audit rights and regulator cooperation.
- Run a controlled technical trial. Test identity, least privilege, key management, logging, monitoring, abuse controls, model evaluations, prompt-injection resistance and rollback.
- Record residual risk. Put unresolved gaps, compensating controls, acceptance dates and accountable executives in the architecture decision record.
- Reassess continuously. Recheck scope when a model, region, connector, contract or regulatory interpretation changes; schedule periodic access and deletion tests.
Questions to put in the provider questionnaire
| Area | Specific question | Evidence to retain |
|---|---|---|
| Scope | Which exact AI product, model, edition, region and deployment are covered by each certification or authorization? | Current certificate, report, service list and scope statement. |
| Responsibility | Which controls are inherited, which are customer-configured and which are jointly operated? | Control matrix with named owners and test procedures. |
| Data use | Are prompts, files, outputs, embeddings or logs used for training or service improvement, and can that behavior be disabled? | Product terms, configuration record and provider confirmation. |
| Retention | What are the defaults and maximum periods for content, safety logs, audit logs, backups and support records? | Retention settings, deletion test and contractual clause. |
| Residency | Where are data stored, processed, backed up and accessed by support staff or subprocessors? | Regional architecture, subprocessor list and transfer terms. |
| Healthcare | Will the provider sign a BAA for this product, region and use case? | Executed BAA and eligible-service list. |
| Incidents | What triggers notification, how quickly will you be informed and what cooperation and evidence are provided? | Incident clause, playbook and contact path. |
| Safety | How are models evaluated, abuse monitored and high-risk changes communicated? | Evaluation summary, abuse policy, change notices and test results. |
| Exit | How do you export data, revoke access and prove deletion when the service ends? | Export format, termination checklist and deletion certificate or report. |
Common selection mistakes and fixes
Mistake: choosing the broadest certification portfolio
Fix: Require a product-and-region scope statement and identify every out-of-scope connector.
Mistake: treating a BAA or DPA as a complete compliance program
Fix: Map your own access, workforce, risk-analysis, retention and incident procedures to the legal requirements.
Mistake: ignoring logs and secondary data
Fix: Include prompts, outputs, embeddings, telemetry, safety events, backups and support tickets in the data inventory.
Mistake: assuming provider safety filters replace application controls
Fix: Add retrieval allowlists, schema validation, redaction, rate limits, human approval and monitoring tailored to your use case.
Mistake: approving a model without an update plan
Fix: Pin versions, stage upgrades, rerun adversarial and quality tests, and keep a rollback route.
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Fix: Give each control an accountable owner, measurable test and review date in the architecture record.
How to make the final decision
Use a pass/fail gate before assigning any commercial score. Reject a service if the required product or region is out of scope, the provider will not supply necessary contractual terms, retention cannot meet policy, or your team cannot implement the customer-side controls. Among the survivors, choose the provider that minimizes residual risk and operational complexity for your specific workload—not the provider with the longest certification list.
A sound decision record should show: the workload and data boundary; the selected model and region; inherited, customer and shared controls; audit and contract evidence; safety-test results; known gaps; compensating measures; and the person who accepted each remaining risk. Revisit that record whenever the model, deployment, data flow, provider terms or applicable law changes.
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