AWS vs Azure vs Google Cloud Identity Models: Accounts, Roles, Policies, Service Identities, and Access

 

AWS, Azure, and Google Cloud all provide identity and access control, but they organize trust, resource scope, and workload identity differently. The concepts transfer; the implementation model does not map one-to-one.

The safest way to compare them is to separate human identity, resource hierarchy, permission assignment, workload identity, and cross-boundary access.

AWS centers access around accounts, principals, roles, and policies

AWS accounts are strong administrative and security boundaries. IAM defines users, roles, and policies inside accounts, while AWS Organizations can group accounts and apply higher-level guardrails.

Roles are especially important because they are assumable identities that provide temporary credentials. Policies can be attached to identities or, for supported services, resources.

AWS account and workload boundaries make identity scope especially visible; the AWS architecture scope shows why permissions, resource policies, role assumption, and cross-account access have to be reasoned about together.

Azure separates identity tenant from resource scope

Microsoft Entra ID provides the identity directory and tenant boundary, while Azure resources are organized through management groups, subscriptions, resource groups, and resources.

Azure role-based access control assigns roles at resource scopes, with inheritance downward. Managed identities allow Azure resources to obtain identities without embedding long-lived application secrets.

The Azure security foundation shows the Azure version of the same problem: identities and permissions interact directly with resource, network, data, and platform security controls.

Google Cloud uses organization, folders, projects, and IAM

Google Cloud organizes resources through an organization, optional folders, projects, and service resources. IAM allow policies can be attached at hierarchy levels and inherited downward.

Service accounts represent workload identities and can receive roles or be impersonated under controlled conditions.

Operational work in the Google Cloud engineer path makes project-level administration and IAM concrete because access decisions affect deployment, troubleshooting, service operation, and automation every day.

Do not equate AWS accounts with Azure subscriptions mechanically

Both can act as important management and billing boundaries, but the surrounding identity systems differ. AWS identities and resource policies are evaluated inside the AWS authorization model, while Azure resource permissions are closely tied to Entra identities and Azure RBAC scopes.

Similarly, a Google Cloud project is not simply an AWS account under another name. Projects sit under the Google resource hierarchy and inherit policy from folders or the organization.

Cross-provider learning should therefore map responsibilities rather than labels.

Compare permission models by four questions

For any platform, ask: Who is the principal? What action is requested? Which resource is targeted? Under what conditions is it allowed?

At business level, the Google Cloud Digital Leader guide links identity and access to governance, ownership, and operating responsibility rather than treating IAM as a standalone technical feature.

Workload identity should avoid embedded credentials

Modern cloud designs increasingly favor temporary, managed, or federated workload credentials instead of static secrets stored in code.

AWS roles, Azure managed identities, and Google service-account-based patterns solve the same general problem with different mechanics.

Developers also need to understand identity. The Azure developer path reflects how application credentials, managed identities, permissions, and service access become part of solution design.

Federation is the bridge for human access

Enterprises commonly keep workforce identities in a central identity provider and federate them into cloud platforms. The cloud then maps authenticated users or groups to permissions.

The Google Cloud developer discussion makes the distinction between application identities and human identities practical because both need authentication, scope, lifecycle, and monitoring but fail in different ways.

Think in trust boundaries, not product names

The transferable skill is identifying where identity is created, where permission is granted, what scope inherits that permission, and how temporary access is established.

In data platforms, the Google data engineer overview shows why IAM is embedded in pipelines, storage, analytics, and orchestration rather than being a security-only concern.

Once those questions are clear, moving between AWS, Azure, and Google Cloud becomes a translation exercise instead of a restart from zero.

Compare the access decision, not the product names

The three clouds organize identity differently, so one-to-one product mapping can be misleading. A better exercise is to ask the same access question in each platform: who is the principal, where is the policy attached or evaluated, what resource scope inherits the decision, how does a workload obtain identity, and which log explains the final allow or deny?

This reveals important architectural differences without treating them as trivia. For example, Azure separates directory administration from Azure resource RBAC, while Google Cloud IAM commonly expresses access through policies on resources with inheritance and service accounts as workload principals. AWS uses identity- and resource-based policy patterns alongside roles and temporary credentials. The portable skill is tracing the decision and proving least privilege.