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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",[670,679,688,697,706,714,724,733,742],{"content":671,"config":677},{"title":672,"heroImage":673,"category":9,"description":674,"authors":675},"Leitfäden und Ressourcen zu agentischer KI","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658912/Blog/Hero%20Images/blog-image-template-1800x945__20_.png","Hier erfährst du alles, was du über agentische KI wissen musst, wie sie funktioniert, warum sie deine DevSecOps-Umgebung aufwertet und welche bewährten Methoden es für die Implementierung gibt.",[676],"GitLab",{"externalUrl":-1,"slug":678},"agentic-ai-guides-and-resources",{"content":680,"config":686},{"title":681,"heroImage":682,"category":9,"description":683,"authors":684},"GitLab Duo mit Amazon Q: In wenigen Minuten wird aus einer Idee der passende Code","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097127/Blog/Hero%20Images/Blog/Hero%20Images/Screenshot%202024-11-27%20at%204.55.28%E2%80%AFPM_4VVz6DgGBOvbGY8BUmd068_1750097126673.png","Die neue Integration von GitLab Duo mit Amazon Q analysiert deine Tickets und erstellt automatisch funktionierende Codelösungen, wodurch deine Workflows beschleunigt werden.",[685],"Cesar Saavedra",{"externalUrl":-1,"slug":687},"gitlab-duo-amazon-q-transform-ideas-into-code-in-minutes",{"content":689,"config":695},{"title":690,"heroImage":691,"category":9,"description":692,"authors":693},"GitLab Duo mit Amazon Q: Entdecke die neue KI-Lösung","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659604/Blog/Hero%20Images/Screenshot_2024-11-27_at_4.55.28_PM.png","Die umfassende KI-basierte DevSecOps-Plattform mit den umfangreichsten Cloud-Computing-Funktionen beschleunigt Entwicklungszyklen, erhöht die Automatisierung und verbessert die Codequalität.",[694],"Emilio Salvador",{"externalUrl":-1,"slug":696},"gitlab-duo-with-amazon-q-agentic-ai-optimized-for-aws",{"content":698,"config":704},{"title":699,"heroImage":700,"category":9,"description":701,"authors":702},"Was ist Generative KI?","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663009/Blog/Hero%20Images/ESA_case_study_image.jpg","Erfahre, was generative KI ist, wie sie sich von klassischer KI unterscheidet, welche Modelle es gibt und wie GitLab mit generativer KI arbeitet.",[703],"GitLab Germany Team",{"externalUrl":-1,"slug":705},"what-is-generative-ai",{"content":707,"config":712},{"title":708,"heroImage":709,"category":9,"description":710,"authors":711},"Verbesserte Qualitätssicherung von Anwendungen mit GitLab Duo Workflow","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097617/Blog/Hero%20Images/Blog/Hero%20Images/Workflow%201800x945_2gQoQIbY9NvjLFpXtsxtXy_1750097616649.png","Erfahre, wie du mithilfe von agentischer KI Unit-Tests zu einer Java-Anwendung hinzufügst (Video-Tutorial inklusive).",[685],{"externalUrl":-1,"slug":713},"use-gitlab-duo-workflow-to-improve-application-quality-assurance",{"content":715,"config":722},{"title":716,"heroImage":717,"category":9,"description":718,"authors":719},"GitLab Duo Self-Hosted: Enterprise-KI für mehr Datenschutz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097840/Blog/Hero%20Images/Blog/Hero%20Images/Self-Hosted%201800x945_1dL1II2ITh2PteObA9DBLD_1750097839679.png","Kund(inn)en in regulierten Branchen können GitLab Duo jetzt auf Self-Managed-Infrastruktur bereitstellen und so leistungsfähige generative KI nutzen, ohne dabei die Datenresidenz und Datenschutzbedenken über Bord werfen zu müssen.",[720,721],"Susie Bitters","Aathira Nair",{"externalUrl":-1,"slug":723},"gitlab-duo-self-hosted-enterprise-ai-built-for-data-privacy",{"content":725,"config":731},{"title":726,"heroImage":727,"category":9,"description":728,"authors":729},"GitLab Duo Workflow: Transparenz und Kontrolle für agentische KI auf Enterprise-Niveau","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749660174/Blog/Hero%20Images/Workflow_1800x945.png","Sichere, autonome, kontextbewusste KI-Tools übernehmen komplexe Aufgaben und geben Entwickler(inne)n die Möglichkeit, innovative Software schneller zu entwickeln. Die private Beta-Warteliste ist jetzt geöffnet.",[730],"Pini Wietchner",{"externalUrl":-1,"slug":732},"gitlab-duo-workflow-enterprise-visibility-and-control-for-agentic-ai",{"content":734,"config":740},{"title":735,"heroImage":736,"category":9,"description":737,"authors":738},"Automatisierung mit GitLab Duo, Teil 3: Validierung von Tests","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097447/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%284%29_3LZkiDjHLjhqEkvOvBsVKp_1750097447404.png","Hier erfährst du, welche Tests wir durchgeführt haben, um die Auswirkungen von GitLab Duo auf die automatisierten Tests unseres Teams zu überprüfen – und welche Ergebnisse wir erzielt haben.",[739],"Byron Boots",{"externalUrl":-1,"slug":741},"automating-with-gitlab-duo-part-3-validating-testing",{"content":743,"config":748},{"title":744,"heroImage":745,"category":9,"description":746,"authors":747},"Automatisierung mit GitLab Duo, Teil 2: Komplexes Testen","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099243/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%284%29_3LZkiDjHLjhqEkvOvBsVKp_1750099243011.png","Hier erfährst du, wie das GitLab-Team mit Hilfe der KI-Funktionen von GitLab Duo komplexere Testsituationen bewältigt und dabei auch sicherstellt, dass die Code-Tests den Standards entsprechen.",[739],{"externalUrl":-1,"slug":749},"automating-with-gitlab-duo-part-2-complex-testing",1777302688430]