A platform team migrating a fraud-detection pipeline from AWS to a multi-cloud footprint recently asked us a simple question that turned out to have no simple answer: "Can we just replicate our IAM policies in Azure and GCP?" The honest answer was no. An AWS vs Azure vs GCP IAM comparison quickly reveals that each provider solved identity and access management with a different mental model -- AWS bolts permissions onto resources via policy documents, Azure layers role assignments on top of Active Directory tenancy, and GCP inherits permissions down a resource hierarchy. Teams that assume parity end up with either over-permissioned service accounts or broken deployments. This guide breaks down the cloud IAM differences that matter for security teams evaluating a multi-cloud strategy, the criteria to weigh, and where third-party tooling -- including Safeguard -- fits into closing the gaps.
Why AWS, Azure, and GCP IAM Aren't Interchangeable
Before comparing vendors, it helps to understand the primitives. AWS IAM is policy-centric: JSON documents attach to users, groups, roles, or resources, and evaluation logic (explicit deny beats allow, SCPs bound at the Organization level) is famously powerful but easy to misconfigure. Azure IAM (technically Azure RBAC, backed by Entra ID) assigns built-in or custom roles to security principals at a management group, subscription, resource group, or resource scope, with inheritance flowing downward -- a model closer to traditional enterprise directory services. GCP IAM also uses inheritance, from Organization to Folder to Project to resource, but leans on predefined roles and a stronger default posture around service accounts and short-lived credentials via Workload Identity Federation. None of these is objectively "better" -- they encode different assumptions about how large enterprises structure accounts, tenants, and projects, and that's precisely why a naive cloud IAM differences comparison based on feature checklists misses the point.
Evaluation Criteria for Comparing Cloud IAM Models
When security and platform teams evaluate IAM across providers -- or evaluate tools that sit on top of them -- these are the criteria that consistently separate a workable multi-cloud identity management program from a fragile one.
Policy Expressiveness and Blast Radius
How granular can permissions get, and how easily can a single misconfigured policy cascade into an account-wide exposure? AWS's condition keys and resource-level permissions are extremely expressive but require discipline; Azure's role definitions are comparatively coarser out of the box; GCP's predefined roles sit between the two, though custom roles can match AWS's granularity.
Federation and Identity Source of Truth
Enterprises rarely want three separate identity stores. Azure's tight coupling with Entra ID gives it a natural edge for organizations already on Microsoft 365. AWS IAM Identity Center and GCP's Workforce Identity Federation both support SAML/OIDC federation to external IdPs like Okta or Ping, but the setup friction and session-lifetime defaults differ meaningfully.
Cross Cloud Least Privilege Enforcement
This is where most comparisons fall apart. Achieving cross cloud least privilege requires normalizing three different permission languages into one risk model, then continuously right-sizing access as usage patterns change. Native tooling in each cloud (IAM Access Analyzer, Azure AD access reviews, GCP Policy Analyzer / Recommender) only sees its own environment, which is why standalone CIEM (Cloud Infrastructure Entitlement Management) tooling exists.
Auditability and Change History
SOC 2 and ISO 27001 auditors want evidence of who changed what permission and when. CloudTrail, Azure Activity Log, and GCP Cloud Audit Logs all capture this, but retention defaults, log format, and correlation with identity events vary enough to complicate cross-cloud reporting.
Non-Human Identity and Workload Access
Service accounts, CI/CD runners, and workload identities now outnumber human users in most environments. How each cloud handles short-lived credentials (GCP Workload Identity Federation, AWS IAM Roles Anywhere, Azure Managed Identities) matters as much as human access control.
Cost and Operational Overhead
Native IAM is "free" in the sense that it's included, but the engineering time to build cross-cloud visibility, run access reviews, and remediate stale grants is a real and often underestimated cost.
AWS vs Azure vs GCP IAM Comparison: Native Capabilities
Comparing the platforms directly on their own terms:
AWS IAM is the most granular and battle-tested of the three, with fine-grained resource policies, Service Control Policies for organization-wide guardrails, and IAM Access Analyzer for detecting unintended external access. Its strength -- flexibility -- is also its biggest operational risk; the sheer number of ways to grant access (identity policies, resource policies, permission boundaries, session policies) makes it easy to lose track of effective permissions without dedicated tooling. Best suited for teams with mature cloud security engineering functions who need precise control.
Microsoft Entra ID (Azure AD) with Azure RBAC benefits enormously from Microsoft's directory heritage, making it a natural fit for enterprises already standardized on Microsoft 365 and Active Directory. Conditional Access policies and Privileged Identity Management (PIM) for just-in-time role activation are genuinely strong differentiators for reducing standing privilege. The limitation: RBAC's built-in roles are often too broad, and organizations frequently end up assigning Contributor or Owner more liberally than they should because building precise custom roles takes real effort.
Google Cloud IAM stands out for its clean resource hierarchy and its early, strong push toward Workload Identity Federation, letting workloads authenticate without long-lived service account keys -- a meaningful security win over static key sprawl. IAM Recommender proactively suggests permission right-sizing based on actual usage, which is a genuinely useful built-in capability the other two clouds don't match as directly. The tradeoff is a smaller ecosystem of third-party integrations and less enterprise-directory depth than Azure.
Third-Party and Cross-Cloud IAM Tooling
Because none of the native offerings above unify identity across all three clouds, most multi-cloud organizations layer on additional tooling.
Okta and Microsoft Entra ID as an external IdP remain the most common choices for federating human identity across AWS, Azure, and GCP via SAML/OIDC, giving a single sign-on source of truth even when each cloud's authorization model stays separate. The limitation is that federation solves authentication, not authorization -- it doesn't tell you what a federated user can actually do once inside each cloud.
HashiCorp Vault is widely used for dynamic secrets and short-lived cloud credentials across providers, reducing standing access for machine identities. It requires meaningful operational investment to run and maintain, and it addresses secrets/credential issuance more than ongoing entitlement visibility.
Wiz and Orca Security both offer CIEM capabilities as part of broader cloud security posture management platforms, surfacing overprivileged identities and toxic combinations of access across AWS, Azure, and GCP from a single dashboard. Their strength is breadth -- IAM findings sit alongside vulnerability and misconfiguration data -- though that breadth can mean less depth on IAM-specific remediation workflows compared to point solutions.
SailPoint brings identity governance rigor (access certifications, segregation-of-duties checks) to cloud entitlements, which is valuable for regulated industries with formal access-review requirements, but it originated in workforce identity governance and can feel heavyweight for teams that just need day-to-day cloud entitlement cleanup.
Each of these tools is worth evaluating on its own merits, and many organizations run more than one -- an IdP for federation, plus a CIEM or posture tool for entitlement visibility. None of them, on their own, was purpose-built to connect identity risk back to what's actually shipping in your software supply chain.
How Safeguard Helps
Safeguard doesn't try to replace AWS IAM, Azure RBAC, or GCP IAM -- it helps security teams see and act on the identity risk that those native systems, and even general-purpose CIEM tools, tend to treat as a separate problem from software supply chain security. In practice, that means:
- Correlating entitlements with what code and pipelines actually touch. Safeguard maps which identities -- human and machine -- have access to build systems, artifact registries, and deployment targets across AWS, Azure, and GCP, so overprivileged CI/CD service accounts don't get evaluated in isolation from the pipelines they run.
- Supporting cross cloud least privilege as an ongoing practice, not a one-time audit. Rather than a point-in-time report, Safeguard continuously tracks drift in permissions granted to build and deployment identities, flagging when access exceeds what a workload's actual behavior justifies.
- Providing a single, consistent view for multi-cloud identity management across providers with genuinely different permission models, so security teams don't have to manually reconcile AWS policy documents, Azure role assignments, and GCP IAM bindings to answer "who can deploy to production."
- Prioritizing findings by supply chain impact, so an overprivileged identity with access to a signing key or release pipeline is triaged differently than one with access to a low-risk internal tool -- context that generic entitlement scanners typically miss.
If your organization is navigating an AWS vs Azure vs GCP IAM comparison as part of a broader multi-cloud or supply chain security initiative, the right answer usually isn't picking a single "winner" among the three IAM models -- it's building visibility and least-privilege discipline that spans all of them. Safeguard is built to make that visibility a byproduct of securing your software delivery pipeline, not an entirely separate program.