[{"data":1,"prerenderedAt":53},["ShallowReactive",2],{"topic:ai-security":3,"topic-articles:ai-security":52},{"id":4,"title":5,"body":6,"description":39,"extension":40,"meta":41,"navigation":42,"order":43,"path":44,"repo":45,"seo":46,"slug":47,"status":48,"stem":49,"tags":50,"topic":45,"__hash__":51},"topics\u002Ftopics\u002Fai-security.md","AI Security",{"type":7,"value":8,"toc":32},"minimark",[9,14,18,22,25,29],[10,11,13],"h2",{"id":12},"définition","Définition",[15,16,17],"p",{},"La sécurité de l’IA couvre les systèmes qui utilisent des modèles, les données qui les alimentent et les décisions qu’ils influencent.",[10,19,21],{"id":20},"enjeux","Enjeux",[15,23,24],{},"Prompt injection, fuite de données, chaîne d’approvisionnement, contrôle des outils et évaluation continue nécessitent une approche expérimentale.",[10,26,28],{"id":27},"travaux-associés","Travaux associés",[15,30,31],{},"NIST AI RMF, OWASP LLM, MITRE ATLAS, sécurité des agents et évaluation des applications RAG forment le cadre principal.",{"title":33,"searchDepth":34,"depth":34,"links":35},"",2,[36,37,38],{"id":12,"depth":34,"text":13},{"id":20,"depth":34,"text":21},{"id":27,"depth":34,"text":28},"Évaluer les risques propres aux systèmes d’IA et aux applications LLM sans oublier leur architecture logicielle classique.","md",{},true,5,"\u002Ftopics\u002Fai-security",null,{"title":5,"description":39},"ai-security","published","topics\u002Fai-security",[],"qvoRf4g8vGUfylekTPr_vSWestoXP88auRnG953NrC8",[],1789036874531]