[{"data":1,"prerenderedAt":1209},["ShallowReactive",2],{"/de-de/blog/secure-ai-agent-deployment-to-gke":3,"navigation-de-de":426,"banner-de-de":858,"footer-de-de":866,"blog-post-authors-de-de-Regnard Raquedan":1106,"blog-related-posts-de-de-secure-ai-agent-deployment-to-gke":1121,"blog-promotions-de-de":1146,"next-steps-de-de":1199},{"id":4,"title":5,"authors":6,"body":8,"category":405,"date":406,"description":407,"extension":408,"externalUrl":409,"faq":409,"featured":410,"heroImage":411,"meta":412,"navigation":413,"path":414,"seo":415,"slug":417,"stem":418,"tags":419,"template":424,"updatedDate":409,"__hash__":425},"blogPosts/de-de/blog/secure-ai-agent-deployment-to-gke.md","KI-Agenten mittels GitLab in GKE bereitstellen",[7],"Regnard Raquedan",{"type":9,"value":10,"toc":390},"minimark",[11,22,27,30,36,42,48,54,57,61,64,77,80,91,95,98,137,140,145,148,155,158,172,175,179,182,209,212,218,229,232,236,239,265,273,282,286,289,292,295,306,313,317,320,326,339,343,346,351,354,367],[12,13,14,15,21],"p",{},"KI-Agents für Production-Umgebungen bereitzustellen erfordert systematische Absicherung. Dieser Leitfaden beschreibt, wie die native Integration zwischen GitLab und Google Kubernetes Engine (GKE) sichere AI-Agent-Deployments ermöglicht – mit integrierten Security-Scans und OIDC-Authentifizierung ohne Service-Account-Keys. Das Tutorial nutzt Googles Agent Development Kit (",[16,17,20],"a",{"href":18,"rel":19},"https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/",[],"ADK",") für die Anwendungsentwicklung.",[23,24,26],"h2",{"id":25},"warum-gke-für-ai-agent-deployment","Warum GKE für AI-Agent-Deployment?",[12,28,29],{},"GKE bietet unternehmenstaugliche Orchestrierung, die sich direkt mit GitLab CI/CD-Pipelines verbindet. Die Authentifizierung erfolgt über OIDC (OpenID Connect). Entwicklungsteams können AI-Agents deployen und behalten dabei vollständige Nachvollziehbarkeit, Compliance und Kontrolle über die Cloud-Infrastruktur.",[12,31,32],{},[33,34,35],"strong",{},"Drei Hauptvorteile:",[12,37,38,41],{},[33,39,40],{},"Vollständige Infrastruktur-Kontrolle"," – Daten, Regeln und Umgebung bleiben unter eigener Verwaltung. Teams bestimmen, wo AI-Agents ausgeführt werden und wie diese konfiguriert sind. Besonders für deutsche und europäische Unternehmen relevant: GKE-Cluster lassen sich in EU-Regionen wie Frankfurt oder Belgien betreiben. Dies ermöglicht Daten-Souveränität und erfüllt DSGVO-Anforderungen für Datenverarbeitung im EU-Raum.",[12,43,44,47],{},[33,45,46],{},"Native GitLab-Integration"," – Bestehende CI/CD-Pipelines funktionieren ohne komplexe Anpassungen. Die Integration zwischen GitLab und Google Cloud ist nativ implementiert. OIDC-Authentifizierung über Workload Identity Federation eliminiert das Risiko gestohlener Service-Account-Keys. Es werden keine Credentials im Code oder in Umgebungsvariablen gespeichert. Dies reduziert Angriffsflächen und vereinfacht Compliance-Audits, da keine Credential-Rotation erforderlich ist.",[12,49,50,53],{},[33,51,52],{},"Production-Grade Scaling"," – GKE übernimmt automatisch Scaling und Orchestrierung bei wachsenden AI-Workloads. Die Horizontal Pod Autoscaler-Konfiguration skaliert basierend auf CPU-Auslastung, beispielsweise von 2 bis 10 Replicas.",[12,55,56],{},"Der systematische Deployment-Ansatz mit integrierten Security-Scans erfüllt beispielsweise Anforderungen der NIS2-Richtlinie für Cybersicherheit. Die Pipeline erzeugt vollständige Audit-Trails vom Code-Commit bis zur Production-Bereitstellung. Jeder Deployment-Schritt ist nachvollziehbar und dokumentiert – eine Grundvoraussetzung für regulierte Branchen und Enterprise-Compliance.",[23,58,60],{"id":59},"voraussetzungen","Voraussetzungen",[12,62,63],{},"Folgende APIs müssen aktiviert sein:",[65,66,67,71,74],"ul",{},[68,69,70],"li",{},"GKE API",[68,72,73],{},"Artifact Registry API",[68,75,76],{},"Vertex AI API",[12,78,79],{},"Zusätzlich erforderlich:",[65,81,82,85,88],{},[68,83,84],{},"GitLab-Projekt erstellt",[68,86,87],{},"GKE-Cluster bereitgestellt",[68,89,90],{},"Artifact Registry Repository erstellt",[23,92,94],{"id":93},"der-deployment-prozess","Der Deployment-Prozess",[12,96,97],{},"Der Workflow umfasst sechs Schritte vom Code-Commit bis zur laufenden Anwendung:",[99,100,101,107,113,119,125,131],"ol",{},[68,102,103,106],{},[33,104,105],{},"Code-Commit"," → GitLab Source Code Repository",[68,108,109,112],{},[33,110,111],{},"Automatische Security-Scans"," → Schwachstellen werden erkannt, bevor Code Production erreicht",[68,114,115,118],{},[33,116,117],{},"CI/CD-Pipeline"," → GitLab orchestriert Build und Deployment",[68,120,121,124],{},[33,122,123],{},"Push zu Artifact Registry"," → Container-Images werden in Google Artifact Registry gespeichert",[68,126,127,130],{},[33,128,129],{},"GKE Deployment"," → Kubernetes-Cluster pullt Images und deployt",[68,132,133,136],{},[33,134,135],{},"Anwendung läuft"," → Zugriff auf deployed AI-Agent",[12,138,139],{},"Das Ergebnis: Eine durchgängig nachvollziehbare, auditierbare und kontrollierte Pipeline vom Code-Commit bis Production – mit integrierter Sicherheit, Artifact-Management und unternehmenstauglicher Orchestrierung.",[141,142,144],"h3",{"id":143},"_1-iam-und-permissions-konfigurieren","1. IAM und Permissions konfigurieren",[12,146,147],{},"Navigieren zu GitLab Integrations, um Google Cloud-Authentifizierung zu konfigurieren.",[12,149,150,151,154],{},"Unter ",[33,152,153],{},"Settings > Integrations"," die Google Cloud Integration konfigurieren. Bei Nutzung einer Group-Level-Integration werden Default-Einstellungen automatisch von Projekten geerbt. Dies bedeutet: Einmalige Konfiguration auf Group-Level, alle Projekte profitieren.",[12,156,157],{},"Für Setup von Grund auf folgende Angaben erforderlich:",[65,159,160,163,166,169],{},[68,161,162],{},"Project ID",[68,164,165],{},"Project Number",[68,167,168],{},"Workload Identity Pool ID",[68,170,171],{},"Provider ID",[12,173,174],{},"GitLab stellt ein Script bereit, das in der Google Cloud Console über Cloud Shell ausgeführt wird. Das Ergebnis ist ein Workload Identity Federation Pool mit dem notwendigen Service Principal für korrekten Zugriff.",[141,176,178],{"id":177},"_2-artifact-registry-integration-konfigurieren","2. 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CI/CD-Pipeline erstellen",[12,237,238],{},"Die CI/CD-Pipeline für das Deployment definiert vier Stages:",[65,240,241,247,253,259],{},[68,242,243,246],{},[33,244,245],{},"Build"," – Docker erstellt das Container-Image",[68,248,249,252],{},[33,250,251],{},"Test"," – GitLab Auto DevOps stellt integrierte Security-Scans bereit (Dependency-Scanning, Container-Scanning, Secret-Detection)",[68,254,255,258],{},[33,256,257],{},"Upload"," – GitLab CI/CD Component pusht zu Google Artifact Registry",[68,260,261,264],{},[33,262,263],{},"Deploy"," – Kubernetes-Konfiguration deployt nach GKE",[12,266,267,268,272],{},"Die Pipeline nutzt OIDC-Authentifizierung (",[269,270,271],"code",{},"identity: google_cloud","), wodurch keine Service-Account-Keys gespeichert werden müssen. Die Kubernetes-Konfiguration umfasst Deployment, Service (LoadBalancer) und Horizontal Pod Autoscaler (2-10 Replicas basierend auf 70% CPU-Auslastung).",[12,274,275,276,281],{},"Die vollständige Pipeline-Konfiguration im ",[16,277,280],{"href":278,"rel":279},"https://about.gitlab.com/blog/secure-ai-agent-deployment-to-gke/#3-create-the-cicd-pipeline",[],"englischen Original",".",[141,283,285],{"id":284},"_4-kritische-gke-konfiguration","4. Kritische GKE-Konfiguration",[12,287,288],{},"Für funktionierende AI-Agents ist ein Kubernetes Service Account erforderlich, der mit Vertex AI arbeiten kann. Dieser Service Account benötigt Permissions für den Zugriff auf Google Cloud AI-Capabilities.",[12,290,291],{},"Ohne diese Konfiguration lässt sich die Anwendung deployen, der AI-Agent funktioniert jedoch nicht. Der Kubernetes Service Account muss Zugriff auf Vertex AI erhalten.",[12,293,294],{},"Das einmalige Setup-Script konfiguriert:",[65,296,297,300,303],{},[68,298,299],{},"GCP Service Account mit Vertex AI-Permissions",[68,301,302],{},"Kubernetes Service Account im Cluster",[68,304,305],{},"Workload Identity Federation-Binding zwischen beiden Accounts",[12,307,308,309,281],{},"Das vollständige Setup-Script im ",[16,310,280],{"href":311,"rel":312},"https://about.gitlab.com/blog/secure-ai-agent-deployment-to-gke/#the-critical-configuration-for-gke",[],[141,314,316],{"id":315},"_5-deployment-nach-gke","5. Deployment nach GKE",[12,318,319],{},"Nach dem Push der Pipeline-Änderungen startet das Deployment automatisch.",[12,321,150,322,325],{},[33,323,324],{},"CI/CD > Pipelines"," sind die vier Stages sichtbar:",[65,327,328,330,333,336],{},[68,329,245],{},[68,331,332],{},"Test (mit allen definierten Security-Scans)",[68,334,335],{},"Upload zu Artifact Registry (erfolgreich)",[68,337,338],{},"Deploy zu Kubernetes in GKE (erfolgreich)",[23,340,342],{"id":341},"zusammenfassung","Zusammenfassung",[12,344,345],{},"GitLab und Google Cloud ermöglichen gemeinsam sichere AI-Agent-Deployments nach GKE mit Kontrolle und Nachvollziehbarkeit. Die native Integration zwischen beiden Plattformen vereinfacht den Prozess erheblich.",[12,347,348],{},[33,349,350],{},"Video-Demonstration:",[12,352,353],{},"Regnard Raquedan, Senior Ecosystem Solutions Architect bei GitLab, demonstriert den kompletten Workflow:",[355,356,359,360],"figure",{"className":357},[358],"video_container","\n  ",[361,362,366],"iframe",{"src":363,"frameBorder":364,"allowFullScreen":365},"https://www.youtube.com/embed/mc2pCL5Qjus?si=QoH02lvz5KH5Ku9O","0","true"," ",[368,369,370],"blockquote",{},[12,371,372,373,378,379,384,385,281],{},"Das komplette Code-Beispiel zum Testen: ",[16,374,377],{"href":375,"rel":376},"https://gitlab.com/gitlab-partners-public/google-cloud/demos/gke-ai-agent",[],"GitHub Repository",". Noch kein GitLab-Kunde? Die DevSecOps-Plattform lässt sich mit einer ",[16,380,383],{"href":381,"rel":382},"https://about.gitlab.com/de-de/free-trial/",[],"kostenlosen Testversion"," erkunden. Startups auf Google Cloud erhalten ",[16,386,389],{"href":387,"rel":388},"https://about.gitlab.com/solutions/startups/google-cloud/",[],"spezielle Konditionen für GitLab",{"title":391,"searchDepth":392,"depth":392,"links":393},"",2,[394,395,396,404],{"id":25,"depth":392,"text":26},{"id":59,"depth":392,"text":60},{"id":93,"depth":392,"text":94,"children":397},[398,400,401,402,403],{"id":143,"depth":399,"text":144},3,{"id":177,"depth":399,"text":178},{"id":234,"depth":399,"text":235},{"id":284,"depth":399,"text":285},{"id":315,"depth":399,"text":316},{"id":341,"depth":392,"text":342},"ai","2025-11-10","Systematisches Deployment von AI-Agents nach Google Kubernetes Engine mit GitLab – integrierte Security-Scans, OIDC-Authentifizierung und 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