Azure Policy vs AWS SCPs: How Cloud Governance Works Differently

1. The Governance Problem Both Clouds Are Trying to Solve

When organizations first migrate to the cloud, the priority is speed, getting workloads running, teams unblocked, and infrastructure provisioned. Governance frameworks are often treated as something to sort out later. The problem is that "later" tends to arrive as a security incident, an unexpected bill, or a compliance audit with no clean answers.

Uncontrolled cloud environments create three specific problems. First, security gaps emerge when teams provision resources without consistent guardrails open storage buckets, misconfigured network rules, and overly permissive access policies become common. Second, cost overruns accumulate silently when there is no enforcement around resource types, regions, or tagging standards. Finance teams are left reconciling bills they cannot explain. Third, compliance failures surface when regulated workloads are deployed in regions or configurations that violate data residency or industry requirements often without anyone realizing it until an audit begins.

Cloud governance at an infrastructure level means defining what is allowed, what is not, and ensuring that definition is enforced automatically rather than through manual review. It is not a policy document sitting in a shared drive. It is code and configuration that enforces organizational rules directly at the point of resource creation or modification.

Both Azure and AWS recognized this problem early, but they solved it differently. Azure built its governance model around resources the idea that every object deployed in Azure should be evaluated against a set of rules regardless of who deployed it. AWS built its governance model around identity the idea that controlling what an account or user is permitted to do is sufficient to control what gets deployed.

This philosophical difference has practical consequences. Azure Policy intercepts resource operations and evaluates them before or after deployment. AWS Service Control Policies set the outer boundary of what IAM identities within an account can do. Neither approach is wrong, but they operate at different layers of the stack, enforce different things, and leave different gaps.

Understanding that difference is not just an architectural consideration. It directly affects how teams design governance frameworks, where they place controls, and how much they can trust those controls in a regulated environment.

2. How Azure Policy Works

Azure Policy is the native governance engine in Azure. It allows organizations to define rules that apply to resources across their entire Azure environment and enforce those rules automatically, consistently, and at scale.

Where Azure Policy sits in the hierarchy

Azure organizes resources in a layered structure: Management Groups sit at the top, followed by Subscriptions, then Resource Groups, and finally individual Resources. Azure Policy can be assigned at any of these levels. When assigned at a Management Group, a policy applies to every subscription and resource beneath it. This top-down inheritance model means a single policy assignment can govern an entire organization without configuring anything at the individual subscription level.

Policy definitions, initiatives, and assignments

A policy definition is the rule itself it describes what condition to evaluate and what effect to apply when that condition is met. An initiative is a collection of policy definitions grouped together to address a broader compliance goal, such as enforcing all controls required by ISO 27001 or CIS benchmarks. An assignment is the act of applying a definition or initiative to a specific scope in the hierarchy.

The separation between definition and assignment is important. The same policy definition can be assigned to multiple scopes with different parameters. A rule that restricts allowed VM sizes, for example, can be applied to production subscriptions with strict limits and to development subscriptions with more flexibility using the same definition with different configuration.

The four core effects

When a policy condition is met, Azure applies one of several effects that determine what happens:

• Deny blocks the resource operation entirely. The deployment fails and the resource is not created.
• Audit allows the operation to proceed but flags the resource as non-compliant in the Azure Policy compliance dashboard.
• Append adds additional fields or tags to a resource at deployment time without blocking it.
• Deploy If Not Exists triggers a secondary deployment when a resource is created or updated commonly used to automatically attach diagnostic settings or monitoring agents to resources that would otherwise be missing them.

Compliance evaluation

Azure Policy evaluates resources both at the point of deployment and on a recurring basis for resources that already exist. This means a resource that was compliant when first deployed may later be flagged as non-compliant if the policy definition changes. The compliance dashboard gives a real-time view of compliance state across all assigned scopes, broken down by policy and resource.

Built-in vs custom policy definitions

Azure ships with hundreds of built-in policy definitions covering common security, cost, and compliance scenarios. These cover everything from requiring encryption on storage accounts to restricting deployments to approved regions. Custom policy definitions are written in JSON and follow the same structure as built-in ones. They are necessary when organizational requirements are specific enough that no built-in definition covers them adequately.

Built-in policies are the right starting point for most organizations. Custom definitions become necessary as governance requirements mature and edge cases emerge that generic rules cannot address.

3. How AWS Service Control Policies Work

AWS Service Control Policies are the primary governance mechanism within AWS Organizations. They define the maximum permissions available to accounts and organizational units within an AWS Organization, acting as a ceiling on what any identity within those accounts can do.

Where SCPs sit within AWS Organizations

AWS Organizations structures accounts in a tree. The root sits at the top, organizational units branch beneath it, and individual accounts sit at the leaves. SCPs can be attached to the root, to any organizational unit, or to individual accounts. An SCP attached to the root applies to every account in the organization. An SCP attached to an organizational unit applies only to accounts within that unit and any units nested beneath it.

SCPs do not grant permissions on their own. They exclusively define boundaries. The actual permissions that allow a user or role to take action still come from IAM policies. This distinction is fundamental to understanding what SCPs can and cannot do.

The relationship between SCPs, IAM policies, and permission boundaries

When AWS evaluates whether an action is allowed, it applies a logical AND across multiple policy layers. An identity needs explicit permission from an IAM policy AND that permission must not be blocked by an SCP. If an SCP does not allow an action, no IAM policy regardless of how permissive can enable it. Conversely, if an SCP allows an action but no IAM policy grants it, the action is still denied.

This layered model means SCPs and IAM policies serve different purposes. SCPs are the organizational guardrail. IAM policies are the operational permission layer. Both must align for an action to succeed.

Allow and Deny logic

SCPs support both Allow and Deny statements. The default behavior at the root of an AWS Organization is a fully permissive Allow all services and actions are permitted unless explicitly restricted. Organizations typically work in one of two modes: they either write Deny statements to block specific actions while leaving everything else open, or they replace the default Allow with an explicit allow list of approved services.

The allow list approach is stricter and more secure but requires significantly more maintenance. The deny list approach is easier to manage but relies on teams correctly identifying and blocking every action they want to prevent which creates risk as AWS releases new services that are not yet covered.

Inheritance and how policies stack

SCPs inherit downward through the organizational tree. An account receives the combined effect of all SCPs attached to every level above it plus any SCP attached directly to it. If an SCP at the root blocks access to a specific region, no account in the organization can operate in that region regardless of what SCPs are attached lower in the tree.

Deny statements at any level take precedence. A Deny in a parent organizational unit cannot be overridden by an Allow in a child unit or account. This makes inheritance predictable but also means that overly broad Deny statements at the root can have unintended consequences across the entire organization.

The boundaries of what SCPs control

SCPs apply only to principals within the AWS Organization IAM users, IAM roles, and the root user of member accounts. They do not apply to the management account of the organization itself, which means the management account sits outside SCP enforcement and requires separate controls. SCPs also do not apply to resource-based policies directly. A policy attached to an S3 bucket that grants access to an external account is not constrained by the SCPs of the organization that owns the bucket.

These boundaries are where SCP-based governance has real limits and where teams need to understand the edges of what the tool can reliably enforce.

4. Head-to-Head: Key Architectural Differences

Azure Policy and AWS SCPs solve the same governance problem but from opposite directions. One controls resources. The other controls identities. That single difference shapes everything how you design policies, where you place controls, and what you can realistically enforce.

Hierarchy model: Management Groups vs AWS Organizations

Azure organizes governance through Management Groups, Subscriptions, Resource Groups, and Resources. Policies flow downward from any point in that hierarchy. AWS organizes governance through a root, organizational units, and accounts. SCPs flow downward from the root or any organizational unit.

Both models support inheritance and both allow governance to be applied at different levels of the hierarchy. The difference is what sits at the bottom. In Azure, the lowest unit of governance is an individual resource a single VM, storage account, or database. In AWS, the lowest unit is an account. SCPs cannot target individual resources within an account. Everything inside an account is governed together.

This means Azure gives more granular control at the resource level while AWS governs at the account boundary. Organizations that separate workloads by account in AWS can apply different SCPs per account. Organizations that mix workloads within a single account have fewer options for differentiating governance rules between them.

Enforcement model: resource-level vs identity-level control

Azure Policy intercepts resource operations through Azure Resource Manager. When a resource is created or modified, Azure Resource Manager checks it against all applicable policies before the operation completes. The enforcement is resource-centric it does not matter who is deploying the resource or what permissions they have. If the resource violates a policy, the operation is blocked or flagged regardless.

AWS SCPs work at the identity layer. They cap what an IAM principal can do, but they do not evaluate the resource being created. If an SCP permits EC2 instance creation, any compliant IAM identity can create any EC2 instance type in any configuration unless other controls exist to restrict them. The resource itself is not directly evaluated by the SCP.

In practice this means Azure Policy can enforce rules like "all storage accounts must have encryption enabled" or "virtual machines cannot be deployed without a specific tag" directly at the resource level. Achieving the same outcome in AWS requires either IAM condition keys, AWS Config rules, or a combination of tools beyond SCPs alone.

Remediation

Azure Policy includes native remediation. Policies using the Deploy If Not Exists or Modify effects can automatically correct non-compliant resources without manual intervention. An organization can write a policy that automatically attaches a diagnostic setting to every new resource that requires it, or one that adds a missing tag to resources that were deployed without one. Remediation tasks can also be triggered manually for existing non-compliant resources.

AWS SCPs have no remediation capability. They are purely preventative they block or allow actions but do nothing about resources that are already in a non-compliant state. Remediation in AWS typically requires AWS Config rules combined with Systems Manager Automation runbooks, which adds complexity and requires separate configuration.

Comparison at a glance

5. Where Each Falls Short

Neither Azure Policy nor AWS SCPs are complete governance solutions on their own. Both have real limitations that become more visible as environments grow in size and complexity.

Azure Policy limitations

At scale, Azure Policy can introduce latency. Environments with a large number of policy assignments particularly those using Deploy If Not Exists effects can see noticeable delays in resource provisioning as evaluation and remediation tasks queue up. This is rarely a problem for smaller environments but becomes a practical operational issue in large enterprises with hundreds of subscriptions and thousands of deployments per day.

Complex policy logic is difficult to maintain. Azure Policy definitions are written in JSON and use a condition language that becomes hard to read as rules get more specific. Teams that build extensive custom policy libraries often find that maintaining, testing, and documenting those definitions becomes a significant overhead. There is no native testing environment validating a policy before assigning it to production requires careful use of Audit mode or separate test subscriptions.

Assignment conflicts create unexpected behavior. When multiple policies apply to the same resource and their effects contradict each other, the outcomes are not always intuitive. A resource might pass one policy and fail another, or a Deploy If Not Exists remediation might conflict with a Deny policy applied at a different scope. Debugging these conflicts requires a detailed understanding of how Azure resolves overlapping assignments.

AWS SCP limitations

SCPs cannot target individual resources. An SCP can block the ability to delete S3 buckets across an entire account but cannot distinguish between a production bucket and a test bucket within that same account. Any resource-level differentiation requires IAM conditions, S3 bucket policies, or AWS Config none of which are part of the SCP framework itself.

Visibility into SCP denials is limited. When an action is blocked by an SCP, the error message returned to the user often does not clearly indicate which SCP caused the denial. Debugging permission issues in environments with multiple SCPs stacked across organizational units can be time-consuming, particularly for teams who did not design the original SCP structure.

There is no remediation layer. SCPs are preventative only. Resources that were deployed before an SCP was applied, or resources that became non-compliant due to configuration drift, are not addressed by SCPs in any way. Organizations relying solely on SCPs for governance are effectively only governing what happens going forward, not what already exists.

The confidence gap

Both tools create a risk that is harder to quantify the illusion of complete coverage. A team that has deployed Azure Policy or AWS SCPs across their environment can reasonably believe their governance is solid. In practice, the gaps in both tools mean that coverage is rarely as complete as compliance dashboards suggest. Misconfigured policies, overlooked resource types, edge cases in inheritance logic, and the management account exclusion in AWS all create real exposure that standard governance tooling does not surface automatically.

6. How Teams Are Using Both in Multi-Cloud Environments

Running Azure and AWS simultaneously is common. Managing governance across both in a consistent way is not straightforward, because the tools do not map to each other cleanly and there is no shared control plane between them.

The gaps neither tool covers on its own

Azure Policy and AWS SCPs each leave gaps in single-cloud environments. In a multi-cloud setup those gaps compound. Azure Policy has no visibility into anything outside Azure. AWS SCPs have no visibility into anything outside AWS. An organization running workloads across both clouds effectively has two separate governance frameworks with no shared enforcement, no unified compliance view, and no way to write a rule once and apply it everywhere.

Beyond that, neither tool covers the network layer comprehensively, neither governs third-party integrations effectively, and neither provides a consolidated audit trail across both environments. Organizations with serious compliance requirements financial services, healthcare, government quickly find that native tools alone are not enough.

Why neither tool alone is sufficient in multi-cloud

The core issue is that Azure Policy and AWS SCPs operate on fundamentally different models. Translating an Azure Policy definition into an equivalent SCP is not a direct process. The enforcement layers are different, the scope definitions are different, and the effects available in each tool do not have clean equivalents in the other. A team trying to enforce the same rule across both clouds has to write it twice, maintain it twice, and debug it twice in two different languages with two different behaviors.

Mapping equivalent controls

Despite the differences, many governance requirements have approximate equivalents in both tools. Region restrictions can be enforced through Azure Policy location conditions and AWS SCPs using the aws: Requested Region condition key. Encryption requirements can be enforced through Azure Policy deny effects on storage resources and through AWS SCPs blocking the ability to create unencrypted resources in combination with IAM conditions. Tag enforcement is available in Azure Policy natively and approximated in AWS through a combination of SCPs and AWS Config.

The mapping is never perfect but it is workable with careful design. The challenge is keeping the two sets of controls in sync as policies evolve and as both cloud providers release new services and resource types.

Tools teams are using to bridge the gap

Several platforms have emerged to address multi-cloud governance directly. Tools like Prisma Cloud, Wiz, and Fugue provide a single control plane that sits above both Azure and AWS and allows teams to write governance rules once and enforce them across both environments. Open Policy Agent has also gained traction as a policy engine that can run independently of any cloud provider and integrate with both Azure and AWS workflows.

HashiCorp Sentinel takes a different approach enforcing governance at the infrastructure-as-code layer before resources are ever deployed, regardless of which cloud they are targeting. For teams using Terraform across both clouds, this provides a consistent governance layer that operates upstream of both Azure Policy and AWS SCPs.

What a practical multi-cloud governance architecture looks like

Most teams that have successfully solved this problem use a layered model. Native tools Azure Policy and AWS SCPs handle the platform-level enforcement within each cloud. A third-party governance platform sits above both and provides unified visibility, cross-cloud policy management, and consolidated compliance reporting. Infrastructure-as-code pipelines include policy checks before deployment so issues are caught before they reach production.

No single tool handles everything. The architecture works because each layer covers what the others cannot, and the combination produces governance coverage that no individual tool provides alone.

7. Choosing the Right Approach for Your Environment

Governance decisions are not one-size-fits-all. The right approach depends on where your workloads live, how your teams are structured, and what your compliance requirements actually demand not what sounds most comprehensive on paper.

Key questions to ask before designing a governance model

Before choosing between Azure Policy, AWS SCPs, or a combination of both, there are a few questions worth answering honestly.

How are your accounts or subscriptions structured? If your AWS environment mixes multiple workload types within the same account, SCPs alone will not give you the resource-level control you need. If your Azure environment has subscriptions that span multiple business units with different compliance requirements, your policy assignment structure needs to reflect that before you write a single rule.

Who is responsible for governance in your organization? A centralized platform team managing governance across the entire organization has different tooling needs than a federated model where individual teams own their own accounts. SCPs work well for centralized enforcement at the account boundary. Azure Policy scales well for centralized teams managing large subscription estates.

What does your compliance framework actually require? Not every organization needs the same coverage. A startup running non-regulated workloads has different governance needs than a financial institution operating in multiple jurisdictions. Scoping your requirements clearly before building a governance framework prevents overengineering and the operational overhead that comes with it.

When Azure Policy alone is sufficient

Azure Policy covers most governance needs for organizations running entirely within Azure. If your requirements center on resource configuration enforcement, tagging standards, region restrictions, and automated remediation, Azure Policy handles all of that natively without additional tooling. Organizations with mature Azure Policy implementations and well-structured Management Group hierarchies can achieve strong governance coverage using built-in policies alone for the majority of their controls.

Azure Policy becomes insufficient when workloads extend beyond Azure or when identity-level controls are needed that resource-level policy enforcement cannot address. It also requires a dedicated investment in policy management as environments scale the tool is capable but not maintenance-free.

When AWS SCPs alone are sufficient

AWS SCPs are sufficient when the primary governance requirement is setting hard boundaries on what accounts within an organization can do. Restricting access to specific regions, preventing the disabling of CloudTrail, or blocking the use of unapproved services are all scenarios where SCPs work cleanly and reliably.

SCPs become insufficient when resource-level differentiation is needed within an account, when existing non-compliant resources need to be remediated, or when compliance reporting needs to reflect the actual configuration state of resources rather than just the permission boundaries applied to identities.

Decision framework

For single-cloud Azure environments, start with built-in Azure Policy initiatives aligned to your compliance framework and expand with custom definitions only where gaps exist. For single-cloud AWS environments, design your organizational unit structure first since SCP inheritance depends on it, then layer SCPs from the root downward with the least permissive controls at the top. For multi-cloud environments, treat native tools as the enforcement layer within each cloud and invest in a third-party governance platform for unified visibility and cross-cloud policy management.

Avoid the common mistake of treating governance as a deployment checkbox. Policies need to be reviewed as environments change, as cloud providers release new services, and as compliance requirements evolve. A governance framework that was accurate at deployment will drift without active maintenance.

What good governance hygiene looks like regardless of cloud

The fundamentals hold across both platforms. Tag everything from day one retroactive tagging at scale is one of the most painful infrastructure tasks that exists and it is entirely avoidable. Restrict by default and open up deliberately rather than allowing everything and trying to lock down later. Keep policy definitions in version control the same way you would treat application code. Test changes in non-production environments before applying them to scopes with real workloads. And audit compliance state on a schedule rather than waiting for an incident to prompt a review.

Governance is not a feature you enable once. It is an ongoing operational discipline that reflects how seriously an organization treats the infrastructure it depends on