How to Define Policies Across Cloud Providers

Managing multiple cloud providers like AWS, Azure, and Google Cloud can be tricky, especially for UK organisations juggling security, compliance, and cost control. Here's the bottom line: you need consistent, clear policies that work across all platforms.

Key takeaways:

• Compliance risks: UK GDPR and data residency laws require uniform encryption, access controls, and logging across providers.
• Security gaps: Inconsistent practices (e.g., MFA in one cloud, not another) lead to vulnerabilities.
• Cost challenges: Without standardised tagging and reporting, reconciling expenses in GBP becomes error-prone.
• Operational inefficiencies: Teams waste time learning platform-specific tools instead of focusing on delivery.

Solution? A provider-neutral policy framework. Define universal rules for security, compliance, and cost management, then map them to each provider’s tools (e.g., AWS Config, Azure Policy). Automate enforcement with policy-as-code tools like Open Policy Agent or HashiCorp Sentinel. Monitor compliance with centralised dashboards, refine policies regularly, and manage exceptions with clear processes.

AWS re:Inforce 2025 - Multicloud strategy and best practices (NTA124)

Step 1: Create a Common Policy Model

To streamline governance across multiple cloud platforms, start by building a provider-neutral policy framework. This approach allows you to map a single set of requirements to each cloud provider's unique controls, cutting down on duplicated work, avoiding conflicting rules, and ensuring all teams operate under the same governance structure. This framework becomes the foundation for defining governance domains and assigning roles.

Identify Governance Domains and Stakeholders

Begin by outlining the governance domains your organisation needs to manage. These might include:

• Identity and access management (IAM): Covering roles, least privilege, multi-factor authentication (MFA), and joiner/mover/leaver processes.
• Security and data protection: Addressing encryption, key management, network segmentation, and incident response.
• Cost management: Ensuring clear cost allocation and budget monitoring in GBP.
• Compliance and risk: Meeting regulatory requirements like GDPR.
• Operations: Encompassing backup, disaster recovery, logging, and monitoring.
• Resource management and tagging: Standardising resource organisation.

Each domain tackles specific risks and responsibilities. For example, IAM focuses on controlling access, while security ensures data protection and incident readiness.

Next, identify the key stakeholders for shaping and enforcing these policies. These groups may include:

• Cloud platform or DevOps teams: Responsible for technical implementation.
• Security and compliance officers: Including roles like the Data Protection Officer (especially relevant for GDPR compliance).
• IT operations teams: Overseeing infrastructure and day-to-day management.
• Finance or FinOps teams: Ensuring cost efficiency and budget adherence.
• Business or application owners: Balancing risk with delivery timelines.

Each group has its own priorities: security teams focus on robust controls and audit trails, finance teams need accurate cost tracking, DevOps teams look for automation-friendly rules, and business owners weigh risks against speed-to-market. Use targeted workshops and a RACI matrix to assign responsibilities clearly. For instance, the cloud platform team might implement encryption policies, while the security team defines encryption standards, and finance assesses cost impacts. Document all decisions, trade-offs, and exceptions in a central governance repository, which can later be automated into policy-as-code.

Adopt a Control Framework

Choose a recognised control framework to create a common governance language. Popular options include:

• Cloud Security Alliance Cloud Controls Matrix (CSA CCM): Designed for multi-cloud environments with mappings to ISO 27001, NIST, PCI DSS, and GDPR.
• CIS Benchmarks: Cloud-specific best practices for AWS, Azure, and Google Cloud.
• NIST Cybersecurity Framework (CSF) or ISO 27001/27017: Broad security standards adaptable to cloud environments.

Load your selected framework(s) into a spreadsheet or governance tool, rewriting each control in neutral terms. For example, instead of Enable S3 bucket encryption, use All storage services holding personal data must enforce server-side encryption using AES-256 or stronger. Add metadata for governance domains, risk levels, applicable regulations (e.g., GDPR, PCI DSS), and workload types (e.g., regulated vs. unregulated). Clearly mark mandatory and recommended controls, and specify the evidence needed - such as logs or configuration states - to demonstrate compliance.

For UK organisations, ensure your framework addresses GDPR and the UK Data Protection Act 2018. Include requirements like data minimisation, storage limitations, integrity, confidentiality, and records of processing activities. If handling payment card data, incorporate PCI DSS standards for encryption, logging, and vulnerability management. This master control list becomes your policy catalogue, which can later be mapped to provider-specific tools like AWS Config, Azure Policy, and Google Cloud Organisation Policy.

Define Policy Objectives in Provider-Neutral Terms

Policy objectives should be clear, measurable, and testable across all platforms. Avoid referencing specific services like S3 or Azure Key Vault. Instead, focus on conditions, metrics, and ownership.

Here are some examples:

• Encryption: All production workloads classified as 'confidential' or higher must use encryption at rest with AES-256 or stronger, with keys managed in an approved key management service. This can be validated through configuration scans and metadata checks.
• Access control: All user access to management consoles must require MFA via a corporate identity provider. Administrator roles must be time-bound and just-in-time elevated. This can be audited across any cloud platform.
• Logging: All production accounts must forward audit logs for control plane and data access events to a central log platform within five minutes of creation. This applies regardless of whether logs come from AWS CloudTrail, Azure Activity Logs, or Google Cloud Audit Logs.
• Cost management: Monthly cloud spend per product line must stay within 10% of the approved budget in GBP. Alerts should trigger at 70%, 90%, and 100% of the budget. For non-production environments, add: Resources must shut down or scale down outside business hours (18:00–08:00 UK time) unless an exception is documented, aiming for 30% savings on non-production costs.

Ensure data residency and transfer policies are explicit, such as: Customer data for UK and EU customers must be stored in UK or EEA regions unless an approved transfer mechanism exists. Define deletion and retention processes to meet GDPR's storage limitation and right-to-erasure requirements.

For identity and access policies, focus on logical roles and groups rather than specific provider implementations. Define roles like Cloud-Admin, Platform-Engineer, and Security-Analyst, detailing their permissions. Apply global principles such as least privilege, separation of duties, mandatory MFA, and no shared accounts. For machine identities, use service principals or roles with key rotation and secret management practices. Set clear timelines for joiner/mover/leaver processes, such as disabling accounts within 24 hours of departure.

Step 2: Map Policies to Cloud-Specific Constructs

This step focuses on translating the abstract policy model into actionable configurations tailored to each cloud platform. Building on the provider-neutral policy model established earlier, the goal is to align each policy with the unique features and capabilities of AWS, Azure, and Google Cloud. Since each cloud provider operates differently, a straightforward one-to-one mapping isn't always possible. Instead, this process involves understanding each platform's enforcement mechanisms, creating a mapping matrix, and ensuring consistent identity controls across all environments.

Review Policy Mechanisms for Each Cloud

Each cloud provider has its own tools and methods for enforcing policies. Understanding these differences is key for organisations in the UK managing multi-cloud setups.

AWS relies on Service Control Policies (SCPs) within AWS Organisations as its primary enforcement tool. SCPs act as guardrails, setting the maximum permissions IAM principals can have. They follow an implicit deny model, meaning actions are only allowed if explicitly permitted by both SCPs and IAM policies. For example, a UK financial services firm might use SCPs to limit operations to eu-west-2 (London) and eu-west-1 (Ireland) for compliance with data residency requirements. Other tools include AWS Config rules to detect configuration drift, IAM policies for detailed access control, and permission boundaries to restrict user and role actions.

Azure centres its enforcement on Azure Policy, which operates at different levels, such as management groups, subscriptions, and resource groups. Azure Policy offers multiple effects, including Deny (blocking non-compliant resources), Audit (logging violations), and DeployIfNotExists (automatically deploying missing configurations). For instance, a UK public sector organisation might use DeployIfNotExists to ensure all storage accounts enable encryption with customer-managed keys via Azure Key Vault. Azure also provides Blueprints (now largely replaced by Template Specs) to bundle policies, roles, and templates into reusable governance packages.

Google Cloud uses Organisation Policies to define constraints at various levels, such as organisation, folder, or project. These constraints can enforce specific configurations, such as disabling external IP addresses for virtual machines or restricting resource locations to UK and EU regions. Additionally, Google Cloud offers IAM policies for access control and VPC Service Controls for creating security perimeters around sensitive resources. Organisation Policies are preventive, stopping non-compliant resources from being created, while detection and reporting are handled by the Security Command Center.

For businesses in the UK, these distinctions matter. AWS SCPs are particularly effective for enforcing regional restrictions and blocking risky services, which is critical for compliance with UK GDPR. Azure Policy's remediation capabilities make it easier to maintain compliance at scale, while Google Cloud's Organisation Policies offer precise control over resource configurations, ideal for managing complex folder hierarchies.

Create a Policy Mapping Matrix

A policy mapping matrix is an essential tool for multi-cloud governance. It connects provider-neutral policies to specific implementations across AWS, Azure, and Google Cloud, serving as a central reference point for tracking enforcement, identifying gaps, and documenting compensating controls.

To create the matrix, include columns such as Policy ID, Policy Name, Governance Domain (e.g., security, compliance, cost), Objective (a provider-neutral description), AWS Implementation, Azure Implementation, Google Cloud Implementation, Monitoring/Verification, Gaps/Exceptions, and Compensating Controls. UK organisations may also benefit from adding columns for Relevant UK Regulations (e.g., UK GDPR, NIS Regulations) and Internal Standards (e.g., data residency or cost allocation tagging).

For example, consider the policy: All storage buckets containing customer data must be encrypted at rest using customer-managed keys (CMKs) for production workloads.

• AWS: Implement SCPs to restrict the use of default AWS-managed keys, IAM policies to enforce AWS KMS CMKs, and AWS Config rules to ensure encryption for S3 buckets, EBS volumes, and RDS instances. S3 bucket policies can also block uploads lacking server-side encryption headers.
• Azure: Use an Azure Policy initiative to mandate CMKs for Storage Accounts, Azure SQL databases, and managed disks. Azure Key Vault would manage the keys, and the compliance dashboard in Azure Policy would provide centralised reporting.
• Google Cloud: Enforce the use of Cloud KMS CMKs for Cloud Storage buckets, Compute Engine disks, and BigQuery datasets using Organisation Policy constraints. The Security Command Center can then scan for non-compliance and issue alerts.

Logs from AWS CloudTrail, Azure Activity Logs, and Google Cloud Audit Logs can be centralised in a SIEM or log management platform to monitor and verify compliance across all providers.

Where gaps exist, compensating controls are crucial. For example, if a specific PaaS service doesn't support CMKs in a UK region, document the gap and implement additional controls like network segmentation or enhanced logging until the service capabilities improve. Similarly, since AWS SCPs don't directly enforce resource tagging, use AWS Config rules and IAM conditions to require tags during resource creation.

Here’s an example of how a row in the matrix might look:

Maintain this matrix in a central location, such as a Git repository, SharePoint, or governance platform, and update it regularly to reflect changes in cloud capabilities and policies.

Standardise Identity and Access Policies

Managing identity and access across multiple clouds is one of the most challenging aspects of governance. The aim is to enforce least privilege, require multi-factor authentication (MFA), and use consistent role naming conventions across AWS, Azure, and Google Cloud.

Start by creating a universal role catalogue with provider-neutral roles, such as:

• app-reader: Read-only access to application resources
• app-operator: Deploy and manage applications
• app-owner: Full control, including IAM administration
• network-admin: Manage networking
• security-auditor: Read-only access to security logs and configurations

Then, map these roles to specific implementations:

• AWS: Define IAM roles with scoped policies and permission boundaries. Use IAM Identity Center (formerly AWS SSO) for centralised role assignments and integration with your corporate identity provider. Avoid broad permissions like s3:*, and instead specify actions like s3:GetObject and s3:PutObject for designated buckets.
• Azure: Use built-in RBAC roles where applicable, and create custom roles for more granular access. Integrate with Entra ID to enforce MFA and simplify access management.
• Google Cloud: Leverage predefined IAM roles and create custom roles aligned with the universal role catalogue. Ensure authentication mechanisms enforce MFA consistently.

This unified approach reduces security risks and ensures compliance with UK regulations, such as UK GDPR. By standardising identity policies, organisations can achieve more consistent and secure access management across their multi-cloud environments.

Step 3: Implement Policies as Code

Once policies have been clearly mapped out in Step 2, the next step is translating them into machine-readable code. This enables automated, repeatable enforcement across your cloud environments, ensuring consistency whether you're working with AWS, Azure, or Google Cloud [1][7]. Unlike manual methods - like wikis, checklists, or ad-hoc reviews - policy-as-code provides a reliable way to enforce rules without the risk of human error.

For organisations managing complex multi-cloud setups, automation becomes essential as they scale. In the UK, where compliance with regulations like UK GDPR is a priority, automating policy enforcement helps maintain adherence while reducing the likelihood of mistakes.

Select Policy-as-Code Tools

With policies mapped out, the next step is to automate their enforcement. Several tools can help ensure consistency across cloud platforms:

• Open Policy Agent (OPA): A flexible, provider-neutral policy engine written in Rego. OPA works across various layers - CI pipelines, Kubernetes admission controllers, API gateways, and custom services. It allows organisations to reuse the same policies for infrastructure and applications, ensuring consistent governance [7].

• HashiCorp Sentinel: Ideal for teams using Terraform Enterprise or Terraform Cloud. Sentinel integrates directly into Terraform workflows, catching policy violations during the planning stage and providing developers with immediate feedback. For example, a UK public sector team managing AWS and Azure infrastructure could use Sentinel to enforce that all resources are deployed only in eu-west-2 (London) or uksouth regions, automatically blocking non-compliant deployments [7].

• Cloud-native policy engines: These tools offer high-level guardrails at the provider level. Azure Policy can automatically remediate non-compliant resources, AWS Service Control Policies set account-wide permissions, AWS Config detects configuration drift, and Google Cloud Organisation Policy prevents non-compliant resources from being created in the first place [1][7].

To ensure smooth governance, standardise your repository structure. A typical setup separates infrastructure repositories (e.g., Terraform, Bicep) from policy repositories (e.g., OPA, Sentinel, Azure Policy), all stored in Git [1][7]. Use clear folder structures, such as global, aws, azure, and gcp, with subfolders for policies, modules, and environments like prod and nonprod. Naming conventions, such as policy-aws-s3-encryption or baseline-azure-networking, make policies easy to locate and reuse. Standardising labels (e.g., cost_centre, owner, environment) further supports governance and cost tracking [1][7].

Build Reusable Baselines and Guardrails

After selecting your tools, create reusable baselines and guardrails to streamline deployments.

• Baselines: These are pre-approved templates or modules that define the minimum configuration requirements for core areas like identity, networking, and data protection. For instance, identity baselines might enforce role-based access, MFA for privileged accounts, and least-privilege roles. In the UK, where access controls are under regulatory scrutiny, these baselines help organisations comply with ICO data protection guidelines.

• Guardrails: These are policy checks that prevent deviations from baselines. For example, network guardrails might block the creation of security groups allowing unrestricted access (0.0.0.0/0) to sensitive ports like SSH (22) or RDP (3389). Similarly, data guardrails could enforce encryption at rest and in transit, ensuring that storage buckets or databases are protected from the outset [1][7].

For UK organisations, these measures not only enhance security but also align with UK GDPR requirements, reducing risks like accidental data exposure. Treat baselines and guardrails as versioned artefacts, with changes reviewed and tested before deployment to ensure they evolve in a controlled, auditable manner.

Integrate Policy Checks into Delivery Pipelines

Embedding policy checks into CI/CD pipelines ensures compliance at every stage of deployment. This approach catches issues early, making them easier and cheaper to fix.

• Pre-deployment checks: Use tools like tfsec, Checkov, or Terrascan to scan infrastructure-as-code (IaC) templates during pull request validation. Policy engines like OPA or Sentinel can evaluate Terraform plans or Kubernetes manifests against organisational rules, blocking non-compliant changes and providing developers with actionable feedback [1][7]. For example, a UK SaaS company might require all resources to include a cost_centre tag with a valid budget code in pounds sterling.

• Deployment-stage checks: Configure CI/CD systems to block deployments that violate critical policies, such as unencrypted storage or missing tags. Stricter rules can be applied in production environments while allowing more flexibility in development. Microsoft’s Azure governance guidance suggests starting with policies in audit mode to measure their impact before full enforcement [1].

• Runtime monitoring and auto-remediation: Beyond deployment, tools like Azure Policy, AWS Config, and Google Cloud Config Validator can detect and correct configuration drift in real time. For example, policies can automatically remediate non-compliant resources or block public access to storage buckets, ensuring ongoing compliance [1][7].

Step 4: Monitor, Audit, and Refine Policies

After implementing automated policies, the work doesn’t stop there. Policies as code require ongoing monitoring, auditing, and fine-tuning to keep up with organisational changes, cloud updates, and evolving UK regulations. Regular reviews are key to ensuring your governance stays effective and aligned with your goals.

Set Up Cross-Cloud Monitoring and Reporting

Keeping an eye on your cloud environment across AWS, Azure, and Google Cloud is crucial. Automated monitoring offers near real-time insights into configuration drift, access changes, and compliance issues - helping you spot risks before they escalate [3][4].

To achieve this, combine native cloud tools with a centralised platform. For example, enable AWS CloudTrail and Config, Azure Monitor and Defender, and Google Cloud Logging and Security Command Centre to collect event and configuration data [9]. Then, feed this data into a centralised system like Splunk or Datadog to normalise findings and apply your policy rules [3][7]. This method provides a unified view of your environment without the hassle of switching between multiple cloud consoles. To comply with UK GDPR, ensure log aggregation is hosted within the UK or EU [8]. For more complex multi-cloud setups, consulting firms like Hokstad Consulting can help integrate policy-as-code, infrastructure-as-code, and observability tools into a seamless governance framework tailored to your needs.

Your monitoring should focus on metrics that cover compliance, security, and cost. Compliance metrics might include the percentage of resources meeting baseline policies (e.g., encryption, tagging, network controls), the number of critical violations each month, or the speed of issue detection and remediation [3][7]. Security metrics could track failed access attempts, overly permissive roles, or unencrypted data stores [7][9]. Meanwhile, cost metrics - reported in GBP - might include monthly spend per provider, variance against budget, or savings from shutting down idle resources [3][7]. Regulatory metrics tied to UK GDPR compliance are also essential [3][8].

Dashboards should be tailored to specific roles and pull data from a central source [3][8]. For instance:

• Security teams need near real-time views of high-risk issues, like public storage buckets containing personal data or missing MFA.
• Compliance teams benefit from reports aligned with frameworks like ISO 27001, showing control coverage and audit evidence.
• Finance teams should track costs and budget adherence using tags such as cost centre or environment.
• Engineering leaders need service-level insights, such as compliance status per application or squad, and open policy violations that block deployments.
• Executives prefer concise summaries on risk posture, major incidents, cost efficiency, and progress against governance KPIs.

This level of monitoring feeds directly into regular policy reviews, ensuring your governance framework evolves as needed.

Schedule Regular Policy Reviews

Policies must adapt to changes in your cloud environment, business priorities, and regulations. Many organisations conduct formal reviews quarterly or semi-annually, with highly regulated industries like finance or healthcare often opting for quarterly assessments [3][8].

In addition to scheduled reviews, certain triggers - such as new cloud services, major security incidents, or regulatory updates - should prompt immediate updates [3][6]. These reviews should involve key stakeholders from security, compliance, finance, and engineering to balance security, cost, and agility [2][4][5]. Metrics like violation trends and remediation times can help identify areas where policies need adjustment [3][8].

During reviews, examine past incidents, false positive rates, and the costs of policy implementation. This ensures controls are still effective and aligned with current risks and priorities [3][8]. For example:

• Tighten controls that reduce risk with minimal overhead, such as narrowing exceptions or shortening remediation timelines.
• Relax or redesign controls that are costly but offer limited risk reduction, like replacing broad manual approvals with automated checks.
• Retire outdated controls through a managed deprecation process, ensuring rollback plans and monitoring are in place [3].

This evidence-based process not only supports better decision-making but also creates a clear audit trail, which is invaluable during external audits or board reviews [8]. Any changes should be version-controlled, thoroughly tested, and clearly communicated to affected teams, with training provided if necessary. By incorporating feedback from engineers and users, you can refine policies without compromising their effectiveness [4].

When gaps are identified, a controlled exception process can help maintain governance without disrupting operations.

Manage Policy Exceptions

Even the most well-crafted policies will occasionally need exceptions. A structured exception process ensures flexibility without undermining governance. Start with a standard request template that includes the policy being exempted, the business justification, the scope of the exception, a risk assessment, proposed compensating controls, and the duration of the exception [3][8]. Approval should involve the relevant technical owner, security or risk teams, and - if regulatory impact is possible - a compliance officer [8]. For high-risk exceptions, senior management or a governance board may need to sign off.

Every exception should have a clear expiry (e.g., 30–180 days) and a remediation plan [8]. Keep documentation centralised and linked to monitoring systems, so dashboards can highlight active exceptions, their risks, and expiry dates. Monthly reviews can flag exceptions nearing expiry or those becoming long-term, allowing you to adjust policies or architecture as needed [3][8].

Treat exceptions as integral to your governance model. Tag and track them in the same systems used for compliance monitoring [3][7]. For instance, compliance tools can flag resources under approved exceptions and alert teams when an exception is about to expire. Policy-as-code systems can also incorporate exception IDs, ensuring deviations are only allowed when valid exceptions are in place. If an ID is missing or expired, the system should treat the resource as non-compliant and trigger alerts or block deployments [3][7].

Clearly communicating the exception process encourages teams to plan ahead rather than resort to workarounds. This demonstrates to auditors and regulators that your organisation is serious about governance, even when flexibility is required.

Conclusion

Setting clear policies across multiple cloud providers is crucial for UK organisations aiming to manage risks, control costs, and meet regulatory requirements. This guide breaks the process into four key steps: creating a provider-neutral policy model, aligning it with individual cloud platforms, implementing policies as code, and ensuring continuous monitoring and refinement.

This structured approach helps minimise risks, avoids inconsistent configurations, and ensures multi-cloud operations remain cost-effective and compliant. By grounding policies in established frameworks, organisations can communicate more effectively with auditors and stakeholders while streamlining the policy definition process. Mapping controls directly to UK GDPR, the Data Protection Act 2018, and sector-specific regulations ensures cloud strategies align with goals like building customer trust, maintaining service availability, and achieving predictable costs.

Automation plays a pivotal role in scaling governance without slowing down delivery. Tools like Open Policy Agent or Sentinel can enforce rules such as keeping customer data within approved regions or mandating storage encryption, preventing non-compliant deployments. In complex multi-cloud environments, where manual reviews are impractical, automation reduces the risk of misconfigurations, security vulnerabilities, and uncontrolled expenses. Starting with a monitor-first approach - observing configurations before enforcing strict rules - helps identify potential impacts and avoids disrupting legitimate operations [1].

To keep pace with evolving cloud services, emerging risks, and changing UK regulations, organisations must focus on continuous improvement. Tracking metrics like policy violations, average remediation time, and the percentage of resources under policy management helps prioritise updates and demonstrate progress to leadership and auditors. Cross-functional governance teams, involving security, engineering, finance, and compliance, should oversee this process. These teams can approve policy updates, learn from incidents, and ensure training evolves alongside policy changes.

For immediate action, organisations can begin by assessing their current state. Inventory all cloud accounts and providers, identify existing policies and gaps, and focus on high-risk areas like identity and access management, data protection, and logging. Define a minimal set of common policies - for example, multi-factor authentication (MFA), encryption standards, region restrictions, and tagging and logging baselines - and implement these as code for critical workflows. Integrating these checks into at least one CI/CD pipeline ensures enforcement. Once the initial setup is operational and measurable, expand coverage incrementally, using metrics and audit findings to guide the rollout.

For organisations further along in their governance journey, external expertise can help refine and accelerate progress. Firms like Hokstad Consulting specialise in DevOps transformation, cloud cost management, and AI-driven automation. They can assist in designing a provider-neutral policy model, translating it into actionable controls across platforms like AWS, Azure, Google Cloud, and private hosting, and ensuring compliance across hybrid environments. Their tailored approach supports UK-specific needs, such as data residency and regulatory compliance, while integrating seamlessly into existing DevOps workflows.

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