Revisar IAM de GCP para sitios médicos con Claude Code

Un sitio médico puede parecer sencillo, pero la cadena de permisos suele estar desordenada. Un formulario mueve nombres, teléfonos, notas de consulta y fechas preferidas por Cloud Run, correo, logs, CSV y proveedores externos.

Este artículo muestra cómo usar Claude Code para inventariar IAM de GCP sin entregar datos reales de pacientes al agente. El objetivo es trazar límites claros entre inspección, edición, aprobación clínica y datos que no se deben tocar.

Puntos clave

Map forms, logs, secrets, CSV exports, and vendors before changing IAM.
Let Claude Code prepare inventory tables, not legal or medical conclusions.
Split read-only review, staging work, approval-gated changes, and production deploy.
Use official references for IAM, service accounts, audit logs, Data Access logs, Sensitive Data Protection, and medical privacy guidance.
Send readers to training because this needs clinic-specific review.

Fallas frecuentes

La revisión de permisos de un sitio médico no empieza por nombres de roles. Empieza por la página que toca el paciente: formulario, sistema de reserva, correo de aviso, logs, CSV, copia de seguridad, reporte de anuncios y alcance del proveedor web. Claude Code puede leer configuración y código para preparar tablas, diferencias, riesgos y listas. Una persona decide privacidad, contrato, política clínica, respuesta al paciente y publicación.

Official references: Use IAM securely, service account best practices, Cloud Audit Logs, Data Access audit logs, Sensitive Data Protection, and medical care privacy guidance.

Trabajar hacia atrás desde el formulario

| Area | Inventory | Human review |

| --- | --- | --- |

| Form | fields, destination, confirmation | does it ask too much |

| Runtime | service account, env vars | does patient data reach logs |

| Logs | viewers, retention, data access | who needs read evidence |

| Secrets | API keys, mail credentials | are secrets shared safely |

| CSV | fields, viewers, exports | can vendors see patient data |

Qué hace Claude Code y qué decide una persona

3 casos de uso

Use case 1: Retired users and vendors

Input: IAM export, service accounts, vendor scope, retired staff list.
Output: member, role, purpose, patient-data touch point, reviewer.
Human review: confirm contract, clinic owner, and whether the access is still needed.

Use case 2: Patient data in logs

Input: form handler code, logger calls, sample log names, email templates.
Output: logged fields, mask candidates, remove candidates, debug-only fields.
Human review: decide whether names, phone numbers, consultation text, symptoms, and dates belong in logs.

Use case 3: Read-only path for AI agents

Input: repository, project ID, resources to inspect, and prohibited data.
Output: read-only tasks, staging-only tasks, approval-gated tasks, prohibited tasks.
Human review: check credentials, commands, output files, deletion targets, and deployments.

Prompt para copiar


Review GCP IAM for a medical website. Keep patient data away from unnecessary staff, vendors, and AI agents. Build tables for IAM roles, service accounts, logs, secrets, forms, emails, and CSV exports. Do not run change commands. Do not paste real patient values. Mark human approvals. Main CTA: /training/.

Código de revisión


const iamRows = [{ member: '[email protected]', role: 'roles/editor', purpose: 'LP edit', touchesPatientData: true, reviewer: '' }];

const findings = iamRows.filter((row) => row.role === 'roles/editor' || (row.touchesPatientData && !row.reviewer));

console.log(findings.length ? findings : 'Medical IAM draft passed.');

if (findings.length) process.exitCode = 1;

Pitfall: el agente amplía acceso al arreglar permisos

The fix is to name prohibited data first: patient names, phone numbers, consultation text, appointment details, inboxes, raw logs, and downloaded service account keys. Use masked samples, column names, or counts when possible.

FAQ

Q. Can the agent read the repository? A. Code and configuration are a good start; real patient messages are not.

Q. Is removing editor enough? A. No. Logs, secrets, CSV exports, emails, datasets, and CI/CD can still expose data.

Q. Why training? A. Permission boundaries depend on the clinic, vendors, and publishing flow.

Hablar con nosotros

ClaudeCodeLab training can help map forms, GCP IAM, logs, secrets, service accounts, and vendor boundaries for a medical website.

Lo que comprobé

This article checks frontmatter, official links, three use cases, input-output-human review labels, code fences, and the /training/ CTA. The code sample is intentionally small and checks broad roles plus missing reviewers.