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Ab dem 24. April 2026 werden Daten aus Copilot Free,\nPro und Pro+ standardmäßig zum Training von KI-Modellen genutzt, sofern\nNutzende nicht aktiv widersprechen. Betroffen sind Eingaben, Ausgaben,\nCode-Snippets und zugehöriger Kontext. Copilot Business und Enterprise sind\naufgrund bestehender Vertragskonditionen ausgenommen.\n\nFür Unternehmen in regulierten Branchen wirft diese Änderung Fragen auf, die\nüber individuelle Entwicklerpräferenzen weit hinausgehen. Sie zwingt zu einer\ngrundlegenden Prüfung, die Führungskräfte aus Engineering und IT-Sicherheit\njedem KI-Anbieter in ihrem Stack stellen sollten: Werden unsere Daten für\nKI-Training verwendet?\n\nGitLabs Antwort lautet: Nein. GitLab trainiert KI-Modelle nicht mit\nKundendaten – auf keiner Preisstufe. KI-Anbieter sind vertraglich verpflichtet,\nKundeneingaben und -ausgaben nicht für eigene Zwecke zu verwenden. Das\n[GitLab AI Transparency Center](https://about.gitlab.com/de-de/ai-transparency-center/)\nmacht diese Zusage prüfbar. Eine zentrale Dokumentation zeigt, welche Modelle\nwelche Funktionen betreiben, wie Daten verarbeitet werden, welche\nUnterauftragsverarbeiter beteiligt sind und wie lange Daten gespeichert werden.\nDas AI Transparency Center dokumentiert außerdem den Compliance-Status jeder\nFunktion – einschließlich der Bestätigung, dass GitLabs aktuelle KI-Funktionen\nnicht als Hochrisikosysteme im Sinne des EU AI Act eingestuft werden. Diesen\nStandard hat GitLab-CEO Bill Staples\n[wiederholt bekräftigt](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf)\n– er spiegelt GitLabs Unternehmensmission und das\n[Trust Center](https://trust.gitlab.com/) wider.\n\n\n## Was die Richtlinienänderung tatsächlich bedeutet\n\nGitHub gibt zudem an, dass die Daten mit verbundenen Unternehmen – darunter\nMicrosoft – für KI-Entwicklungszwecke geteilt werden können.\n\nQuellcode zählt häufig zum sensibelsten geistigen Eigentum eines Unternehmens.\nEr kann proprietäre Geschäftslogik abbilden, interne Systemarchitekturen\noffenlegen oder Datenflüsse berühren, die strengen Aufbewahrungs- und\nZugriffsrichtlinien unterliegen. Wenn dieser Code einen KI-Assistenten\ndurchläuft und zum Training von Modellen verwendet wird, die auch Wettbewerbern\ndienen, werden Anbieterdatenpraktiken zu einem konkreten IP-Risiko. Regulierte\nBranchen weltweit – von Finanzdienstleistungen über Gesundheitswesen bis zum\nöffentlichen Sektor – operieren unter Compliance-Anforderungen, die genau\ndiesen Punkt adressieren: dokumentierte, prüfbare Kontrolle über den Umgang\nDritter mit sensiblen Daten.\n\nEine Anbieterrichtlinie, die Datenstandardeinstellungen ändert, ein aktives\nWiderspruchsrecht erfordert und je nach Vertragsstufe unterschiedliche\nSchutzstandards bietet, erzeugt genau die Art unkontrollierbarer Variablen,\ndie Compliance-Teams nicht akzeptieren können. Der\n[Digital Operational Resilience Act (DORA)](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng)\n– seit Januar 2025 für europäische Finanzinstitute verbindlich – macht dies\nexplizit: Wesentliche Änderungen an IT-Drittanbieterbeziehungen erfordern\ndokumentierte Bewertung und Nachverfolgung.\n\n\n## Was regulierte Unternehmen von KI-Anbietern tatsächlich benötigen\n\nRegulierte Unternehmen diskutieren nicht mehr grundsätzlich, ob KI in\nEntwicklungs-Workflows eingesetzt werden soll. Der Fokus liegt darauf, dies so\nzu tun, dass es gegenüber Aufsichtsbehörden, Vorständen und Kunden vertretbar\nist. Dabei sind branchenübergreifend konsistente Anforderungen sichtbar\ngeworden.\n\n**Vertragliche Klarheit.** Regulierte Unternehmen benötigen spezifische,\ndokumentierte und bedingungslose Zusagen darüber, was mit ihren Daten geschieht\n– nicht etwas, das je nach Vertragsstufe variiert oder eine Handlung vor einem\nStichtag erfordert.\n\n**Prüfbarkeit.** IT-Risikomanagement-Frameworks verlangen von Unternehmen, die\neingesetzten KI-Systeme zu verstehen und zu validieren: die Trainingsdaten\nhinter diesen Modellen und die beteiligten Drittparteien. Anbieter, die diese\nFragen nicht beantworten können, erzeugen Dokumentationsrisiken für die\nOrganisationen, die sich auf sie stützen.\n\n**Trennung von Anbieterinteressen.** Wenn ein KI-Anbieter Modelle auf Basis\nvon Kundennutzungsdaten trainiert, werden Code und Workflows zu Eingaben für\nein System, das auch Wettbewerbern dient. Für Institutionen mit proprietären\nHandelsalgorithmen, Underwriting-Modellen oder Betrugserkennungssystemen ist\ndas ein konkretes IP-Risiko.\n\n\n## GitLabs Position zur KI-Datenverwaltung\n\nGitLab verwendet Kundendaten nicht zum Training von KI-Modellen. Diese Zusage\ngilt auf jeder Preisstufe; KI-Anbieter sind vertraglich verpflichtet, Eingaben\nund Ausgaben, die mit GitLab-Kunden verbunden sind, nicht für eigene Zwecke zu\nverwenden.\n\nDies ist eine bewusste architektonische und richtlinienbezogene Entscheidung –\nkein Merkmal einer bestimmten Preisstufe. Wie GitLabs\n[Beitrag zur Enterprise-Unabhängigkeit](https://about.gitlab.com/de-de/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/)\nfesthält, ist Datenverwaltung zu einem \"zunehmend kritischen Faktor bei\nUnternehmensentscheidungen\" geworden – getrieben durch nationale und regionale\nDatenschutzgesetze und wachsende Bedenken hinsichtlich der Kontrolle über\nsensibles geistiges Eigentum.\n\nGitLab ist cloud-neutral und modell-neutral und unterstützt\nSelf-Hosted-Deployments ohne kommerzielle Bindung an einen einzelnen\nCloud-Anbieter oder ein Large Language Model. Diese Unabhängigkeit ist für\nregulierte Unternehmen relevant, die Risiken durch Anbieterkonzentration\nbewerten. Der\n[AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/)\ndokumentiert, wie Anbieterveränderungen gehandhabt werden – einschließlich\nwesentlicher Änderungen daran, wie KI-Anbieter Kundendaten behandeln. Er ist\neine direkte Antwort auf die Governance-Anforderungen unter Frameworks wie\n[DORA](https://handbook.gitlab.com/handbook/legal/dora/).\n\n\n## Die Governance-Lücke, die KI-Teams schließen müssen\n\nGitHubs Richtlinienaktualisierung macht deutlich: Für Unternehmen in\nregulierten Branchen ist das genaue Verständnis des Datenumgangs eines\nKI-Werkzeugs eine Voraussetzung für dessen Einsatz. Das bedeutet, Anbietern\nklare, dokumentierte Antworten auf folgende Fragen abzuverlangen:\n\n1. Werden unsere Daten zum Training von KI-Modellen verwendet?\n2. Wer sind Ihre KI-Modell-Unterauftragsverarbeiter?\n3. Was geschieht, wenn ein Anbieter seine Datenpraktiken ändert?\n4. Lässt sich ein Deployment realisieren, das alle KI-Verarbeitung innerhalb\n   der eigenen Infrastruktur hält?\n5. Welche Haftungsübernahme wird für KI-generierte Ausgaben angeboten?\n\nAnbieter, die diese Fragen klar beantworten und die Antworten in prüfbarer\nForm dokumentieren, sind Anbieter, auf die sich aufbauen lässt.\n**Wer das nicht kann, schafft Compliance-Risiken bei jedem Policy-Update.**\nWenn ein Anbieter Datenpraktiken mit 30 Tagen Ankündigungsfrist ändern kann,\nist das kein partnerschaftlicher Rahmen für regulierte Unternehmen – das ist\nein strukturelles Compliance-Risiko.\n\n> Mehr zu GitLabs Ansatz für KI-Governance im\n> [GitLab AI Transparency Center](https://about.gitlab.com/de-de/ai-transparency-center/).\n",[657,658],"AI/ML","product",{},"/de-de/blog/github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call",{"config":662,"title":663,"description":664},{"noIndex":10},"GitHubs Copilot-Richtlinie: KI-Governance für Unternehmen","GitHubs Copilot-Richtlinie ändert die Datennutzung ab 24. April. Was das für regulierte Branchen bedeutet – und wie GitLabs Ansatz strukturell anders ist.","de-de/blog/github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call",[667,658],"aiml","DR-0oKvJZZlxEXADZYkeVcpiUGIvjaCcIiI_zBBXSDQ",[670,679,686,693,700,707,714,721,728],{"content":671,"config":677},{"title":672,"heroImage":673,"category":9,"description":674,"authors":675},"Flows verstehen: Multi-Agent-Workflows","https://res.cloudinary.com/about-gitlab-com/image/upload/v1765809212/noh0mdfn9o94ry9ykura.png","Vertiefe dich in GitLab Duo Agent Platform Flows. Lerne Foundational Flows kennen, erstelle Custom User-defined Workflows und lerne Flow-Orchestrierungsmuster.",[676],"Itzik Gan Baruch",{"externalUrl":-1,"slug":678},"understanding-flows-multi-agent-workflows",{"content":680,"config":684},{"title":681,"heroImage":673,"category":9,"description":682,"authors":683},"Agenten verstehen: Foundational, Custom und External","Lerne, wann du welchen Agententyp nutzt: sofort verfügbare Foundational Agents, teamspezifische Custom Agents oder externe KI-Integrationen.",[676],{"externalUrl":-1,"slug":685},"understanding-agents-foundational-custom-external",{"content":687,"config":691},{"title":688,"heroImage":673,"category":9,"description":689,"authors":690},"KI-Workflows überwachen, verwalten und automatisieren","Automate-Funktionen der GitLab Duo Agent Platform: Aktivität über Sessions überwachen, Workflows mit Triggern automatisieren, KI-Prozesse verwalten.",[676],{"externalUrl":-1,"slug":692},"monitor-manage-automate-ai-workflows",{"content":694,"config":698},{"title":695,"heroImage":673,"category":9,"description":696,"authors":697},"Einführung in GitLab Duo Agent Platform","Grundlagen der GitLab Duo Agent Platform kennenlernen und erste Agent-Interaktion abschließen.",[676],{"externalUrl":-1,"slug":699},"introduction-to-gitlab-duo-agent-platform",{"content":701,"config":705},{"title":702,"heroImage":673,"category":9,"description":703,"authors":704},"GitLab Duo Agent Platform: Der vollständige Einstiegsleitfaden","Lerne GitLab Duo Agent Platform mit dieser umfassenden Serie kennen – von ersten Interaktionen bis zu fortgeschrittener MCP-Integration, Anpassung und Produktions-Workflows.",[676],{"externalUrl":-1,"slug":706},"gitlab-duo-agent-platform-complete-getting-started-guide",{"content":708,"config":712},{"title":709,"heroImage":673,"category":9,"description":710,"authors":711},"GitLab Duo Agentic Chat – Erste Schritte","Chat-Interface kennenlernen, Model- und Agent-Auswahl verstehen sowie Best Practices für effektive KI-Kollaboration über Web UI und IDEs hinweg.",[676],{"externalUrl":-1,"slug":713},"getting-started-with-gitlab-duo-agentic-chat",{"content":715,"config":719},{"title":716,"heroImage":673,"category":9,"description":717,"authors":718},"GitLab Duo Agent Platform anpassen: Chat-Regeln, Prompts und Workflows","GitLab Duo Agent Platform an Team-Workflows anpassen. Chat-Regeln konfigurieren, System-Prompts erstellen, Agent-Tools einrichten und Flows anpassen.",[676],{"externalUrl":-1,"slug":720},"customizing-gitlab-duo-chat-rules-prompts-workflows",{"content":722,"config":726},{"title":723,"heroImage":673,"category":9,"description":724,"authors":725},"AI Catalog: Agenten und Flows entdecken, erstellen und teilen","Nutze den AI Catalog als zentrale Stelle für vorgefertigte und teamspezifische Agenten und Flows – durchsuchbar, aktivierbar und teilbar im gesamten Team.",[676],{"externalUrl":-1,"slug":727},"ai-catalog-discover-and-share-agents",{"content":729,"config":736},{"title":730,"heroImage":731,"category":9,"description":732,"authors":733},"KI-gestützte Incident-Reports – strukturierte Dokumentation für NIS2- und ISO-Compliance","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098339/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%285%29_1iy516k40hwBDChKcUJ2zb_1750098339103.png","KI-gestützte Incident-Dokumentation für NIS2/ISO-Compliance: Strukturierte Reports, Root-Cause-Analysen und Audit-Trails. Mit Use-Case-Beispielen.",[734,735],"Valentine Mairet","David O'Regan",{"externalUrl":-1,"slug":737},"how-to-leverage-gitlab-duo-for-enhanced-security-reporting",1777302688264]