[{"data":1,"prerenderedAt":1046},["ShallowReactive",2],{"/fr-fr/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security":3,"footer-fr-fr":53,"the-source-banner-fr-fr":364,"the-source-navigation-fr-fr":376,"the-source-newsletter-fr-fr":403,"footer-source-/fr-fr/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security/":414,"article-site-categories-fr-fr":426,"4-ways-ai-can-help-devops-teams-improve-security-article-hero-category-fr-fr":428,"4-ways-ai-can-help-devops-teams-improve-security-the-source-source-cta-fr-fr":451,"4-ways-ai-can-help-devops-teams-improve-security-category-fr-fr":466,"4-ways-ai-can-help-devops-teams-improve-security-the-source-resources-fr-fr":479,"4-ways-ai-can-help-devops-teams-improve-security-article-hero-author-fr-fr":497},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":13,"content":19,"type":44,"category":5,"slug":45,"_id":46,"_type":47,"title":48,"_source":49,"_file":50,"_stem":51,"_extension":52},"/fr-fr/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security","ai",false,"",{"layout":9,"template":10,"author":11,"featured":6,"sourceCTA":12},"the-source","TheSourceArticle","kristina-weis","source-lp-how-to-get-started-using-ai-in-software-development",{"title":14,"description":15,"ogImage":16,"config":17},"L'IA au service des équipes DevOps pour une sécurité logicielle renforcée","Découvrez comment les équipes DevOps utilisent l'IA et le machine learning (ML) pour améliorer la sécurité, réduire les risques et livrer un code plus sécurisé.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463801/t2lucrovy8dadeimvk48.png",{"ignoreTitleCharLimit":18},true,{"title":14,"date":20,"description":21,"timeToRead":22,"heroImage":16,"keyTakeaways":23,"articleBody":27,"faq":28},"2023-12-05","Découvrez comment les équipes DevOps utilisent l'intelligence artificielle (IA) et le machine learning (ML) pour améliorer la sécurité, réduire les risques et livrer un code plus sécurisé.","Lecture : 4 min",[24,25,26],"Dans le développement logiciel, l'IA et le ML ne se limitent pas à la génération de code : ils renforcent la sécurité, corrigent rapidement les vulnérabilités, améliorent les revues de code et suggèrent des tests adaptés pour une couverture optimale.","Près d'un tiers des équipes DevSecOps utilisent déjà l'IA pour la génération automatisée de tests. Toutefois, 55 % des professionnels interrogés estiment que l'introduction de l'IA dans le cycle de développement logiciel comporte des risques.","Les entreprises doivent privilégier des outils d'IA qui n'entraînent pas leurs modèles de machine learning à l'aide de données propriétaires ou de code source, et qui adoptent une approche axée sur la protection de la confidentialité.","Aujourd'hui, l'intelligence artificielle (IA) et le machine learning (ML) dans le développement logiciel ne visent plus seulement à automatiser les tâches répétitives et à accélérer les livraisons de code. En devenant de véritables partenaires, ces technologies permettent aux équipes DevOps de livrer un code de meilleure qualité, plus fiable et de renforcer la sécurité des applications destinées à leur entreprise et leurs clients. \n\nVoici comment l'IA contribue à améliorer la sécurité de vos développements logiciels, de la détection des failles de sécurité à la protection des données sensibles :\n\n## Détection et corrections des vulnérabilités en un temps record\n\nFace à une faille de sécurité, chaque seconde compte. Comprendre précisément la nature du problème constitue la première étape vers une correction efficace. C'est là que l'IA révèle tout son potentiel. Traditionnellement, les équipes doivent examiner manuellement des centaines de lignes de code pour détecter les vulnérabilités : des revues de code qui s'avèrent chronophages, avec le risque d'erreurs humaines. Désormais, l'IA permet de générer instantanément des résumés clairs des vulnérabilités en identifiant la manière dont elles pourraient être exploitées, et mieux encore, en suggérant des mesures d'atténuation concrètes, accompagnées d'exemples de code pertinents pour chacune d'entre elles. En offrant des recommandations directement exploitables, l'IA aide ainsi les développeurs et les équipes de sécurité à réduire rapidement et efficacement les risques.\n\n## Des revues de code repensées et plus efficaces\n\nLorsqu'un développeur soumet son code à la relecture, l'IA peut considérablement accélérer le processus et identifier les problèmes potentiels en amont.\n\nElle peut également aider l'auteur à choisir le meilleur relecteur, à savoir une personne connaissant bien le code base, plus à même de détecter les anomalies critiques, et moins susceptible d'ignorer la revue de code ou de la bâcler. Alors que sélectionner manuellement les meilleurs relecteurs peut s'avérer fastidieux et imprécis, les algorithmes de machine learning analysent les modifications apportées ainsi que le graphique de contributions du projet pour recommander les profils de relecteurs les plus adaptés.\n\nL'IA va même plus loin en générant automatiquement un résumé de la merge request afin d'aider les relecteurs à comprendre rapidement les enjeux des modifications à examiner et d'assurer un transfert fluide du processus de revue du code.\n\n## Génération de tests pour garantir une couverture optimale\n\nL'un des principaux moyens de s'assurer que le code fonctionne comme prévu et qu'il n'introduit pas de failles de sécurité consiste à tester minutieusement les modifications apportées au code. Toutefois, rédiger des scénarios de tests peut s'avérer fastidieux, au point que certaines modifications font souvent l'objet d'un push vers l'environnement de production sans la couverture de test nécessaire. \n\nL'IA est capable d'analyser les modifications de code et de suggérer des tests pertinents ainsi que les fichiers de test associés. Les développeurs peuvent ainsi consacrer moins de temps à concevoir des tests et [plus de temps à coder](https://about.gitlab.com/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity/). \n\nEn réalité, de nombreuses équipes DevOps utilisent déjà l'IA pour générer des tests. Selon notre [enquête 2024 menée auprès de plus de 5 000 professionnels DevSecOps dans le monde entier](https://about.gitlab.com/fr-fr/developer-survey/2024/ai/), près d'un tiers (32 %) des personnes interrogées dont les entreprises ont adopté l'IA l'utilisent déjà pour automatiser la génération de tests.\n\n## Protection des données sensibles à l'ère de l'IA\n\nPour de nombreuses entreprises, il est important que les gains de productivité liés à l'utilisation de l'IA et du ML ne se fassent pas au détriment de la confidentialité, de la sécurité ou de la conformité. Plus de la moitié des répondants à notre enquête (55 %) estiment que l'introduction de l'IA dans le processus de développement logiciel comporte des risques. Les préoccupations relatives à la protection de la vie privée et à la sécurité des données constituent le principal obstacle à son adoption.\n\nAvant d'intégrer des outils d'IA à vos processus de développement logiciel, assurez-vous de comprendre si et comment vos données propriétaires seront exploitées pour entraîner ses modèles de machine learning. Laisser vos équipes DevOps utiliser un outil d'IA inadapté vous expose à de graves et coûteuses conséquences : [fuites de code source, divulgation de données sensibles et stratégiques](https://www.techradar.com/news/samsung-workers-leaked-company-secrets-by-using-chatgpt), entre autres.\n\n> Découvrez comment votre équipe DevSecOps peut appréhender et mesurer [l'impact de l'IA générative](https://about.gitlab.com/fr-fr/the-source/ai/how-to-put-generative-ai-to-work-in-your-devsecops-environment/).\n\n### Renforcez la sécurité avec des workflows DevSecOps alimentés par l'IA \n\nLes solutions d'IA comme [GitLab Duo](https://about.gitlab.com/fr-fr/gitlab-duo/) offrent des [fonctionnalités](https://docs.gitlab.com/ee/user/ai_features.html) intelligentes, telles que des résumés de vulnérabilités, des suggestions de tests, des suggestions de relecteurs et des résumés de merge requests, qui permettent à vos équipes DevOps de tirer parti de l'IA pour renforcer la sécurité tout au long du cycle de développement logiciel.\n\nConçu avec une approche axée sur la confidentialité, GitLab Duo n'entraîne pas les modèles de ML avec les données propriétaires ou le code source des clients. Une promesse forte pour aider les entreprises et les organisations réglementées à adopter l'IA en toute sécurité.",[29,32,35,38,41],{"header":30,"content":31},"Comment l'IA peut-elle aider les équipes DevOps à détecter et atténuer les failles de sécurité ?","L'IA accélère la détection des vulnérabilités en générant des résumés clairs des risques de sécurité identifiés et en suggérant des corrections exploitables. Au lieu d'effectuer des revues de code manuelles à la recherche de vulnérabilités, les équipes DevOps peuvent s'appuyer sur des outils d'IA pour analyser le code, identifier les faiblesses et obtenir des suggestions de correction. Résultat : un gain de temps précieux et une réponse plus rapide aux menaces de sécurité.",{"header":33,"content":34},"Comment l'IA améliore-t-elle l'efficacité des revues de code ?","L'IA optimise les revues de code en suggérant les relecteurs les plus pertinents sur la base de l'historique de leurs contributions et leur expertise. Elle peut également générer des résumés des merge requests, permettant aux relecteurs de comprendre rapidement les modifications apportées au code et de se concentrer sur les risques de sécurité majeurs. En réduisant les goulots d'étranglement dans le processus, ces revues de code garantissent des évaluations de sécurité de meilleure qualité.",{"header":36,"content":37},"L'IA peut-elle contribuer à la génération de tests pour renforcer la sécurité ?","Tout à fait. L'IA peut générer automatiquement des tests pour garantir une couverture optimale du code et réduire la possibilité que des failles de sécurité passent à travers les mailles du filet. En analysant les modifications apportées au code, elle suggère des tests unitaires, des tests d'intégration et des tests de sécurité pertinents, qui permettent aux équipes DevOps de valider efficacement leurs logiciels sans avoir à rédiger manuellement chaque scénario de test.",{"header":39,"content":40},"Quels sont les risques de sécurité associés à l'utilisation de l'IA dans le développement logiciel ?","Les principaux risques concernent la confidentialité des données, la conformité et les fuites de données potentielles. Pour limiter ces menaces, il est essentiel d'évaluer rigoureusement les outils d'IA que vous utilisez et de vérifier qu'ils n'entraînent pas leurs modèles sur du code propriétaire. Les solutions d'IA telles que GitLab Duo privilégient une approche axée sur la protection de la vie privée, garantissant que les données sensibles et stratégiques restent protégées.",{"header":42,"content":43},"Comment les workflows DevSecOps alimentés par l'IA peuvent-ils renforcer la sécurité des logiciels ?","Les workflows DevSecOps alimentés par l'IA intègrent la sécurité à chaque étape du développement logiciel en fournissant différents leviers, tels qu'une détection des vulnérabilités, une analyse des risques, des tests automatisés et des recommandations de code sécurisé. En tirant parti de l'IA pour révéler des informations clés liées à la sécurité, les équipes DevSecOps peuvent livrer plus rapidement des logiciels plus sécurisés, réduire la charge de travail manuelle et limiter les erreurs humaines.","article","4-ways-ai-can-help-devops-teams-improve-security","content:fr-fr:the-source:ai:4-ways-ai-can-help-devops-teams-improve-security.yml","yaml","4 Ways Ai Can Help Devops Teams Improve Security","content","fr-fr/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security.yml","fr-fr/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security","yml",{"_path":54,"_dir":55,"_draft":6,"_partial":6,"_locale":7,"data":56,"_id":360,"_type":47,"title":361,"_source":49,"_file":362,"_stem":363,"_extension":52},"/shared/fr-fr/main-footer","fr-fr",{"text":57,"source":58,"edit":64,"contribute":69,"config":74,"items":79,"minimal":351},"Git est une marque déposée de Software Freedom Conservancy et notre utilisation de « GitLab » est sous 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Il est souvent sollicité par le secteur public comme privé pour fournir des commentaires et des réponses dans le cadre de leurs politiques.",{"altText":831,"config":841},{"src":842},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463423/mkmdhuxsjggfvokdmdv7.jpg",{"componentName":515},"joel-krooswyk","content:fr-fr:the-source:authors:joel-krooswyk.yml","fr-fr/the-source/authors/joel-krooswyk.yml","fr-fr/the-source/authors/joel-krooswyk",{"_path":849,"_dir":500,"_draft":6,"_partial":6,"_locale":7,"config":850,"seo":851,"content":853,"type":516,"slug":865,"_id":866,"_type":47,"title":852,"_source":49,"_file":867,"_stem":868,"_extension":52},"/fr-fr/the-source/authors/josh-lemos",{"layout":9},{"title":852},"Josh Lemos",[854,864],{"componentName":506,"componentContent":855},{"config":856,"name":852,"role":859,"bio":860,"headshot":861},{"gitlabHandle":857,"linkedInProfileUrl":858},"joshlemos","https://www.linkedin.com/in/joshlemos/","Chief Information Security Officer","Josh Lemos, Chief Information Security Officer chez GitLab Inc., bénéficie de 20 ans d'expérience dans la direction d'équipes de sécurité de l'information. 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Il définit la vision et oriente les processus afin d'intégrer la science des données au programme DevOps de GitLab. Avant de rejoindre GitLab, Taylor a forgé son expérience chez Indeed, Duo Security et WP Engine.",{"altText":1028,"config":1038},{"src":1039},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463427/trfasilaeasosxfcxmsm.jpg",{"componentName":515},"taylor-mccaslin","content:fr-fr:the-source:authors:taylor-mccaslin.yml","Taylor Mccaslin","fr-fr/the-source/authors/taylor-mccaslin.yml","fr-fr/the-source/authors/taylor-mccaslin",1761814447852]