GCP IAM für medizinische Websites mit Claude Code prüfen

Eine medizinische Website kann einfach wirken, während die Zugriffskette dahinter unklar ist. Ein Formular schickt Namen, Telefonnummern, Beratungsnotizen und Terminwünsche durch Cloud Run, E-Mail, Logs, CSV und externe Dienstleister.

Dieser Artikel zeigt, wie Claude Code ein GCP-IAM-Inventar vorbereitet, ohne echte Patientendaten an den Agenten zu geben. Ziel ist eine Grenze zwischen Prüfung, Änderung, Freigabe und Daten, die nicht berührt werden dürfen.

Kernaussagen

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.

Typische Fehler

Die Rechteprüfung einer medizinischen Website beginnt nicht bei Rollennamen. Sie beginnt bei der Patientenseite: Formular, Buchungssystem, Benachrichtigung, Logs, CSV, Backup, Werbereport und Dienstleisterumfang. Claude Code kann Konfiguration und Code lesen, Tabellen, Diffs, Risiken und Checklisten erzeugen. Menschen entscheiden Datenschutz, Vertrag, Praxisregel, Patientenkommunikation und Veröffentlichung.

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

Vom Formular rückwärts arbeiten

| 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 |

Was Claude Code tut und was Menschen entscheiden

3 Use Cases

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.

Kopierbarer Prompt


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/.

Prüfcode


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: Der Agent erweitert Zugriff beim Reparieren

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.

Mit uns sprechen

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

Was ich geprüft habe

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.