IAM Access Review Automation

The Problem and Why It Mattered

I built a quarterly IAM access review automation system — pulling all IAM bindings across a GCP organisation, risk-scoring access, routing certification tasks to managers, tracking responses, and producing SOC 2 / ISO 27001 audit evidence. 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 security design had to improve control coverage without exposing sensitive architecture, credentials, customer data, or internal findings in the public record.

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 Cloud Asset Inventory, Google Workspace API, Cloud Functions. 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 cybersecurity 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 Cloud Asset Inventory, Google Workspace API, Cloud Functions 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: IAM Inventory & Risk Scoring

Cloud Scheduler triggers the review at the start of each quarter. A Python Cloud Function queries Cloud Asset Inventory for all IAM policy bindings. Risk scoring: Owner/Editor on prod (HIGH), direct user bindings vs. groups (MEDIUM), service accounts with human-like names (MEDIUM), bindings older than 180 days with no resource access in 90 days (MEDIUM).

Implementation: Manager-Routed Certification

Cross-references the Google Workspace Directory API to identify each user's manager. Managers receive a formatted email listing all access their direct reports hold, with per-binding decisions: Certify, Revoke, Escalate. Responses collected through a Cloud Run form authenticated via IAP.

Implementation: Automatic Revocation & Evidence Package

When a manager submits Revoke, the system calls the GCP IAM API to remove the binding immediately, logs to BigQuery and Cloud Logging, and sends confirmation. Completed review produces full evidence: all bindings reviewed, each decision with timestamp and manager, before/after IAM policy comparison — archived to the locked compliance evidence bucket.

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 implementation is described with least privilege, traceability, controlled configuration, and evidence boundaries in mind. Where the project supports a regulated environment, these are technical safeguards and audit-readiness contributions, not a claim that an application or product is independently certified.

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 cybersecurity 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 security engineering case study addresses a defined technical problem through Cloud Asset Inventory, Google Workspace API, Cloud Functions. 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.