Multi-Cloud Identity Federation (GCP + Azure)

The Problem and Why It Mattered

I designed and implemented a cross-cloud identity federation architecture allowing GCP workloads to authenticate to Azure resources using short-lived OIDC tokens — no stored credentials, no service account keys, no client secrets anywhere. The relevant technical and business risk is the cost of leaving this workflow, control, or product boundary unclear, manual, insecure, or difficult to operate. The case study focuses on the decisions used to make that risk manageable.

Objectives and Success Criteria

• Deliver the capability described in the project objective using a maintainable technical design.
• Make the main workflow, control boundary, and operational responsibilities explicit.
• Produce reviewable implementation evidence without publishing confidential values or customer data.

Requirements and Constraints

The implementation had to be reproducible, observable, and safe to describe publicly without exposing operational secrets or private infrastructure detail.

My Role and Ownership

I owned the technical design and implementation described in this portfolio record, including the technology choices, core system boundaries, and the documentation of the resulting approach. Related organisational decisions, vendor operations, and formal certification decisions remain outside the scope of this technical case study unless stated explicitly.

Solution Overview

The solution is built around Workload Identity Federation, Azure AD, OIDC. Its implementation is organised as the components below so that the architecture, control points, and operating model can be evaluated separately rather than treated as a list of tools.

Architecture and System Design

The public architecture description follows the path from the initiating user, workload, or operator action through the relevant cloud engineering components. Access boundaries, data or state handling, and operational controls are described at a safe level; secrets, private addresses, and client-sensitive implementation details are intentionally omitted.

Technology Decisions and Trade-Offs

I selected Workload Identity Federation, Azure AD, OIDC because they support the project objective and make the critical engineering boundaries explicit. The trade-off is operational and platform complexity: the design depends on documented configuration, controlled access, and ongoing review rather than assuming that a managed service or framework removes engineering responsibility.

Implementation Walkthrough

The following implementation notes describe the components that matter most to the project outcome. They are intentionally focused on technical decisions and operating behaviour instead of reproducing entire source files or sensitive configuration.

Implementation: GCP → Azure Federation

Azure AD configured to trust GCP's OIDC token endpoint. A GCP service account is a federated identity in Azure AD. When a GCP workload needs an Azure token, it calls the GCP metadata server for a service account OIDC token, then exchanges it at Azure's token endpoint. Tokens are short-lived (1 hour).

Implementation: GitHub Actions → Both Clouds

GitHub Actions jobs authenticate to both GCP and Azure using OIDC — zero secrets stored in GitHub. The job exchanges a GitHub OIDC token at GCP's Workload Identity Federation endpoint and at Azure AD. The entire chain is cryptographic — no passwords, no keys.

Implementation: Terraform Implementation

Full federation configuration codified in Terraform: GCP Workload Identity Pool and Provider resources, Azure AD application and federated credential, IAM bindings scoped to only required resources. Reproducible from scratch in one terraform apply.

Challenges, Failures and Corrections

This portfolio record does not invent deployment failures, client incidents, or corrective actions that are not supported by the published project evidence. Where operational details are confidential or were not captured for public release, the case study documents the design boundary and lesson instead of presenting an unsupported narrative.

Security, Compliance and Risk

The public record avoids secrets, customer data, and private infrastructure details. The relevant security controls, access boundaries, and operational risks are addressed in the implementation notes where they are material to the project.

Testing, Validation and Evidence

The evidence published for this project is the documented implementation, its stated architecture and controls, and the portfolio record. The published record documents the implemented architecture, the core controls, and the technology choices that can be reviewed without exposing confidential operational data. Claims are limited to the implementation details and outcomes documented in this case study.

Results and Evidence

The published record documents the implemented architecture, the core controls, and the technology choices that can be reviewed without exposing confidential operational data.

What I Learned

This project reinforced that cloud engineering work is not a technology checklist. The durable value comes from making boundaries, trade-offs, ownership, and evidence clear enough for another engineer or stakeholder to review and operate the system responsibly.

What I Would Improve Next

• Expand automated validation around the most important workflow and control boundaries.
• Add or deepen operational evidence, such as dashboards, test artefacts, or an architecture diagram where it is safe to publish one.
• Review the design against the next expected scale, reliability, and governance requirement before extending the system.

Final Takeaway

This cloud engineering case study addresses a defined technical problem through Workload Identity Federation, Azure AD, OIDC. It demonstrates my ability to turn requirements into an explainable design, implement the important controls, and document outcomes and limitations without overstating what the evidence proves.