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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesChoose a secure model-hosting platform by matching its documented controls to your workload—not by relying on a general security label. Compare how prompts and outputs are handled, where inference runs, whether you can isolate network access and manage encryption keys, how permissions and activity are audited, and who operates each part of the system. Then verify those controls for the exact model, feature, region, and deployment type you intend to use.
Amazon Bedrock, Google Vertex AI, and Microsoft Foundry document different security controls and scope limitations. None of those documents, on its own, establishes that a service is the most secure choice for every project. The practical question is whether its current configuration and contractual scope meet your requirements—and whether your team can operate it safely.
Start with the data and the consequences of exposure
Before comparing providers, write down what the application will send to a model and what could happen if that information were exposed, retained, routed unexpectedly, or returned to the wrong person. The answer differs between a public-facing assistant answering general questions and an internal tool processing sensitive business records. A platform cannot be judged against an undefined use case.
Describe the information in the actual prompt and response path, not only the database connected to the application. Include user messages, retrieved documents, system instructions, tool inputs and outputs, and any diagnostic or application logs that may capture content. Identify whether the workload has location restrictions, internal security requirements, or legal obligations. The appropriate controls depend on this data sensitivity and on the rules that apply to your organization.
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- Data use and retention: Establish whether prompts and completions are retained, used to train models, or shared with model providers. Ask which logging is enabled by default and what administrators can change.
- Processing and storage location: Specify where data may be stored and where inference may be processed. These are separate questions.
- Access: Identify which people, services, and administrators need access, and at what scope.
- Network exposure: Decide whether the endpoint must be reachable only through a private network and which other outbound paths the application needs.
- Operational ownership: Decide who will configure controls, monitor activity, respond to incidents, and manage changes to models or service features.
Turn those decisions into requirements before you review platform documentation. Otherwise, it is easy to mistake a feature that exists somewhere in a provider’s product family for a control that is available for your chosen model and deployment.
Use one requirements matrix for every platform
Compare candidates against the same workload and record evidence for each requirement. A useful matrix distinguishes between a control the documentation explicitly describes, a control that is conditional or feature-specific, and a point you still need the provider to confirm. Do not treat an undocumented detail as proof that the control is absent—or as proof that it exists.
| Area | What to establish | Evidence to record |
|---|---|---|
| Data use and retention | Are prompts and outputs retained, used for training, or shared with model providers? What logging is enabled, and what can an administrator configure? | The applicable product terms and documentation for the exact service, model, and logging configuration. |
| Location | Where is information stored, and where can inference be processed? Can the deployment route across regions or a wider data zone? | The selected deployment scope and the provider’s description of storage and processing geography. |
| Encryption and keys | Is data encrypted in transit and at rest? Can your organization manage keys for the resources and data involved? | Which traffic and resources are covered, which key options apply, and any limits for the selected deployment. |
| Network boundary | Can the intended endpoint use private connectivity? What public or outbound paths remain? | Endpoint-specific private connectivity documentation and the network design needed to use it. |
| Identity and audit | Can access be scoped by role and resource? Are API calls and administrator actions recorded and exportable? | The permission model, available audit records, and configuration required for the workload. |
| Model supply chain | Who hosts the model? What is known about its container, license, provenance, and vulnerability review? | The model’s source and deployment guidance, including any explicit limits on security scanning. |
| Guardrails | Which input and output filters, sensitive-data controls, and safety monitoring are available? | Feature availability and evidence that the controls can be tested against the application’s use cases. |
| Compliance scope | Does the required commitment cover this service, region, model, and deployment? | The current certification or contractual documentation that applies to that precise scope. |
| Operations and exit | Who patches, scales, monitors, and responds to incidents? How can the application move or shut down? | Named owners, operational procedures, and a tested plan for model or provider change. |
Use a simple status such as “documented,” “conditional,” “unconfirmed,” or “does not meet requirement,” with a source and date for each entry. A score can help prioritize discussion, but it should not conceal a hard requirement: if a workload must remain within a defined processing boundary, a high score elsewhere does not make an unverified location control acceptable.
Distinguish storage residency from inference geography
A regional storage location does not necessarily tell you where a model processes a request. A service may describe where data is stored separately from how inference is routed. This distinction matters when your policy limits processing as well as storage.
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Google Cloud’s Vertex AI documentation lists data residency among controls for supported generative AI features, but the applicable controls differ by feature and model. Check the current row for the precise feature you plan to use; do not infer that a control listed for one generative AI feature automatically applies to another.
For Amazon Bedrock, verify the model and region you intend to use alongside applicable service terms and configuration. Do not generalize a regional availability statement into a guarantee about every processing, logging, or related service path.
Check what encryption and key-management claims cover
“Encrypted” is not enough detail to decide whether a service meets a requirement. Ask which information is protected in transit, which stored resources are covered, and whether the customer can manage keys for those specific resources. A key option documented for one resource type or feature should not be assumed to cover every prompt, output, log, or model-related artifact.
AWS documents TLS 1.2 for inter-network traffic in Amazon Bedrock and KMS key options for some resources. Its documentation also describes encryption at rest. Confirm which data and resources these statements cover in your intended deployment and what configuration is required. For Google Vertex AI, verify customer-managed encryption key availability in the control matrix for the exact feature and model. Microsoft Foundry documents customer-managed keys; establish the scope of that support for the service and data involved.
Customer-managed keys can help an organization apply its own key-management requirements, but their presence is not a substitute for checking identity permissions, key lifecycle responsibilities, or the exact resources protected. Put those implementation details into the matrix instead of treating the phrase “customer-managed keys” as a complete security outcome.
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Make private networking and identity controls concrete
A private endpoint is useful only if it applies to the endpoint and deployment your application will call, and if the rest of the application’s traffic is controlled appropriately. Ask what private connectivity is available for that precise service path, how the application reaches it, and what egress routes remain. Do not assume that a private connection eliminates every other route to or from the workload.
AWS describes IAM-based permissions and PrivateLink for Amazon Bedrock. For the deployment you select, check the permissions needed by the calling application and administrators, and verify the private-connectivity setup in the service documentation. Google’s Vertex AI documentation includes VPC Service Controls for supported generative AI features. Confirm availability for the exact feature rather than treating that control as universal across Vertex AI. Microsoft’s model-catalog guidance recommends virtual networks and Azure Policy to reduce risk from model containers that may not have been scanned for vulnerabilities.
For each candidate, document which identities can invoke the model, change its configuration, view logs, or administer the surrounding resources. Then check whether activity records can support your organization’s monitoring and investigation needs. The comparison is not complete just because a provider offers role-based permissions or audit facilities somewhere in its cloud: verify the relevant permissions, records, and configuration for the specific service.
Assess model and container supply-chain risk
Model hosting can involve more than the cloud provider’s own managed infrastructure. Where a catalog offers third-party models or containers, establish who supplies the model, what deployment artifact is used, and what is known about its provenance, license, and vulnerability review. These facts can affect both technical risk and whether the model is appropriate for your intended use.
Microsoft’s Foundry model-catalog guidance warns that not all model catalog containers are scanned for vulnerabilities that could exfiltrate data. It recommends virtual networks and Azure Policy to reduce risk. This is a specific reason to review the container and deployment guidance rather than assuming every catalog entry has undergone the same security checks. Apply the same scrutiny to other services: check what the provider documents for the model you plan to host, and mark any assurance you cannot establish as unconfirmed.
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Supply-chain review should be a selection criterion when a project depends on a particular third-party model. If key information about the artifact or its security review is unavailable, decide explicitly whether network and policy controls sufficiently reduce the risk for your workload, or whether the uncertainty rules out that model.
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Test guardrails against the application, not the feature list
Filtering and safety features can reduce certain risks, but a feature name does not establish that a guardrail will handle your application’s specific inputs and outputs. Identify what you need to control—for example, what information may enter a prompt, what kinds of outputs should be blocked or reviewed, and what events need monitoring. Then verify which controls the selected model and feature actually support.
Evaluate controls using representative application cases, including the types of content or requests that matter to your project. Record what each guardrail is intended to do, how it is configured, and what happens when it blocks or flags a request. The available platform documentation does not establish a universal guardrail feature set or comparative effectiveness across Amazon Bedrock, Vertex AI, and Foundry, so do not select a platform based on an assumed ranking.
Read compliance claims at the scope that matters
A compliance claim only answers your question if it covers the service, region, model, and deployment you will use—and if the applicable commitment matches your organization’s requirement. A cloud-wide or provider-level statement may not establish coverage for every feature in a hosted-model workflow.
Before approval, identify the precise certification or contractual commitment you need and obtain the current documentation that applies to the intended configuration. If a claim’s scope is unclear, treat it as unresolved rather than inferring coverage from a related service. Compliance documentation is one input to a security decision, not evidence that the customer’s own configuration is secure.
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Understand shared responsibility before launch
Cloud security is shared. AWS puts the principle plainly: “Security is a shared responsibility between AWS and you.” In its description, AWS secures cloud infrastructure, while the customer is responsible for security in the cloud. The customer’s choices depend on data sensitivity, organizational requirements, and applicable law. The same practical distinction matters when evaluating managed model hosting: provider controls do not remove the customer’s obligation to configure the service and protect its data.
Make the division of work explicit. The provider may operate parts of the underlying service, while your team still chooses how the application connects, who receives access, what information enters a prompt, and how the surrounding workload is monitored. The exact boundary is service-specific; confirm it in the applicable documentation instead of assuming that “managed” means the provider handles every security decision.
Assign an owner for each required control, including configuration, review, and incident response. If no one is responsible for a control, it is not a reliable safeguard in practice, even if the platform offers it.
A practical selection process
- Write the workload profile. Record the models and features needed, the information sent and returned, the permitted processing and storage geography, and the consequences of exposure or interruption.
- Separate mandatory requirements from preferences. Mark any location, access, key-management, or compliance condition that would disqualify a service if it cannot be verified.
- Build the comparison matrix. Use the same questions for each candidate. Attach current provider documentation to every confirmed claim; label conditional and unresolved points plainly.
- Check the exact deployment. Verify controls against the chosen model, feature, region, and deployment type, not just the provider’s general product overview.
- Review configuration and ownership. Confirm who will set permissions, network boundaries, logging, keys, and guardrails, and who will maintain them.
- Run a focused security review. Test the application’s intended flows and guardrails, review available activity records, and confirm the model or container’s documented supply-chain status where relevant.
- Decide and document residual risk. Record which requirements are met, what remains uncertain, why the choice is acceptable, and who has authority to accept any unresolved risk.
- Revisit after changes. Recheck documentation when the model, feature, region, deployment scope, or relevant service configuration changes. Provider availability and control coverage can change over time.
How the documented options differ
The following comparison is deliberately limited to points described in official provider documentation checked on October 9, 2026. It is not an independent security audit or a claim that one provider is categorically more secure. For every row, confirm current applicability to the intended model and deployment.
| Platform | Documented points | What to verify for your project |
|---|---|---|
| Amazon Bedrock | AWS describes a shared responsibility model, TLS 1.2 for inter-network traffic, encryption at rest, KMS key options for some resources, IAM permissions, monitoring, and PrivateLink. AWS says customer prompts and outputs are not shared with model providers or used to train base models, subject to current product terms and configuration. | Confirm the exact model, region, resources covered by encryption and key options, logging settings, current data-use terms, and applicable compliance scope. |
| Google Vertex AI | Google’s control documentation lists data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency for supported generative AI features. | Check the current control-matrix row for the precise feature and model. Availability differs; do not assume all listed controls apply across Vertex AI. |
| Microsoft Foundry | Microsoft documents global, data-zone, and regional deployment scopes, customer-managed keys, and Azure geography for stored data. Its model-catalog guidance warns that not all containers are scanned for vulnerabilities and recommends virtual networks and Azure Policy to reduce risk. | Confirm the chosen deployment scope and inference route, distinguish processing from storage location, and review the model’s container guidance and applicable controls. |
These are starting points for verification, not equivalent guarantees. The platforms describe controls at different levels of detail in the cited documentation, and the availability of a control can depend on the selected feature or deployment. The official materials reviewed do not establish a complete like-for-like ranking of their security, nor do they document a universal answer for every project’s retention, compliance, or operational requirements.
Choose for operational fit and a credible exit
A platform with appropriate controls may still be a poor fit if your team cannot maintain them. Include the work of configuring access, managing network paths, reviewing logs, validating guardrails, and responding to incidents in the selection decision. Identify which tasks the provider performs and which remain with your organization for the service you plan to use.
Also decide how you would change models or providers if requirements change. Record which parts of the application depend on provider-specific APIs or features, what information or configuration would need to move, and how you would disable the old endpoint. This is not a claim that any particular migration is easy; it is a practical way to ensure a security or service change does not leave an undocumented model path active.
Finally, set a review trigger. A new model, feature, region, deployment scope, or material configuration change can alter which controls apply. Revisit the matrix against current official documentation before relying on a changed deployment for sensitive workloads.
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