{"id":1081,"date":"2026-02-23T20:02:54","date_gmt":"2026-02-23T17:02:54","guid":{"rendered":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/"},"modified":"2026-02-23T20:03:30","modified_gmt":"2026-02-23T17:03:30","slug":"llm-data-leakage-riski-2","status":"publish","type":"post","link":"https:\/\/neku.ai\/en\/llm-data-leakage-riski-2\/","title":{"rendered":"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131"},"content":{"rendered":"

LLM Kullanan Sistemlerde Data Leakage Riski<\/strong><\/h1>\n
\n

Giri\u015f<\/strong><\/h3>\n

LLM kullanan sistemlerde data leakage ai<\/em> riski, kurumsal verilerin istemsiz \u015fekilde a\u00e7\u0131\u011fa \u00e7\u0131kmas\u0131 ya da \u00fc\u00e7\u00fcnc\u00fc taraflara s\u0131zmas\u0131 anlam\u0131na gelir. Yapay zek\u00e2 platformlar\u0131 b\u00fcy\u00fck dil modelleri (LLM) \u00fczerinden \u00e7al\u0131\u015f\u0131rken, bu modellerin e\u011fitimi ve sorgulama a\u015famalar\u0131nda hassas veri g\u00fcvenli\u011fi \u00f6ncelikli hale gelir. G\u00fcvenlik perspektifinden bak\u0131ld\u0131\u011f\u0131nda, bu risk; gizlilik, eri\u015fim y\u00f6netimi ve model davran\u0131\u015f\u0131n\u0131n kontrol\u00fc gibi konular\u0131 do\u011frudan etkiler.<\/p>\n

\n

LLM Kullanan Sistemlerde Data Leakage Riski tan\u0131m\u0131<\/strong><\/h3>\n

LLM security \u00e7er\u00e7evesinde veri s\u0131z\u0131nt\u0131s\u0131, modelin e\u011fitim veya \u00e7\u0131kar\u0131m s\u00fcrecinde i\u015fletme i\u00e7i verilerin fark\u0131nda olmadan a\u00e7\u0131\u011fa \u00e7\u0131kmas\u0131d\u0131r. Bu durum, kullan\u0131c\u0131 girdilerinin veya kurumsal bilgi tabanlar\u0131n\u0131n model yan\u0131tlar\u0131nda d\u0131\u015fa vurulmas\u0131yla ger\u00e7ekle\u015febilir. Data leakage ai<\/em> kavram\u0131, hem teknik altyap\u0131 hem de operasyonel s\u00fcre\u00e7lerin b\u00fct\u00fcnsel g\u00fcvenlik politikalar\u0131yla y\u00f6netilmesini gerektirir.<\/p>\n

\n

data leakage ai nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h3>\n

Data leakage ai<\/em> tipik olarak modelin e\u011fitimi s\u0131ras\u0131nda, yanl\u0131\u015f yap\u0131land\u0131r\u0131lm\u0131\u015f veri eri\u015fim politikalar\u0131 veya izleme eksikliklerinden kaynaklan\u0131r. Model, e\u011fitim setine dahil edilmemesi gereken verileri \u00f6\u011frenebilir ve daha sonra bu bilgileri yan\u0131tlar\u0131nda ortaya \u00e7\u0131karabilir. Bunun nedeni, LLM\u2019lerin \u00f6rneklerden genelleme yaparken baz\u0131 hassas kal\u0131plar\u0131 da \u00f6\u011frenebilmesidir.<\/p>\n

Temel parametreler ve ayarlar<\/strong><\/h4>\n

Veri s\u0131z\u0131nt\u0131s\u0131 riskini azaltmak i\u00e7in giri\u015f do\u011frulama, \u00e7\u0131k\u0131\u015f filtreleme ve veri maskeleme mekanizmalar\u0131 uygulanmal\u0131d\u0131r. Modelin eri\u015fim d\u00fczeyleri, API \u00e7a\u011fr\u0131lar\u0131nda kimlik do\u011frulama kurallar\u0131 ve veri \u00f6n i\u015fleme katmanlar\u0131yla s\u0131n\u0131rland\u0131r\u0131lmal\u0131d\u0131r. Denetimli \u00f6\u011frenme senaryolar\u0131nda e\u011fitim veri setlerinin anonimle\u015ftirilmesi g\u00fcvenli\u011fin temel ad\u0131m\u0131d\u0131r.<\/p>\n

S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h4>\n

Kurulu\u015flar genellikle modelin \u00e7\u0131kt\u0131s\u0131n\u0131 do\u011frudan kullan\u0131c\u0131ya sunarken g\u00fcvenlik katman\u0131n\u0131 ihmal eder. \u00d6zellikle test ortamlar\u0131nda ger\u00e7ek verilerin kullan\u0131lmas\u0131 yanl\u0131\u015f bir uygulamad\u0131r. Ka\u00e7\u0131nmak i\u00e7in model yan\u0131tlar\u0131n\u0131 s\u0131n\u0131fland\u0131rma tabanl\u0131 filtreleme, red-team testleri ve g\u00fcvenli prompt tasar\u0131m\u0131 kullan\u0131lmal\u0131d\u0131r.<\/p>\n

Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h4>\n

Bir e-ticaret platformu, m\u00fc\u015fteri destek botu i\u00e7in LLM kullan\u0131rken anonimle\u015ftirilmemi\u015f m\u00fc\u015fteri verilerini modelin haf\u0131zas\u0131nda tutabilir. Bu durum, benzer bir sorguda o m\u00fc\u015fterinin ki\u015fisel bilgilerinin yan\u0131t olarak d\u00f6nmesine neden olabilir. G\u00fcvenli uygulama i\u00e7in her sorgu \u00f6ncesinde veri do\u011frulama katman\u0131 ve izin kontrolleri eklenmelidir.<\/p>\n

\n

Teknik a\u00e7\u0131klama (derin seviye)<\/strong><\/h3>\n

LLM security, genellikle modelin parametrelerinin \u00f6\u011frenme e\u011frisinden ziyade veri ak\u0131\u015f mant\u0131\u011f\u0131yla ilgilidir. S\u0131zma riski, \u00f6zellikle embedding temelli semantik arama altyap\u0131lar\u0131nda ortaya \u00e7\u0131kar; \u00e7\u00fcnk\u00fc ilgili belgeler model haf\u0131zas\u0131na yak\u0131n ba\u011flamlarda tutulur. Data leakage ai<\/em> \u00f6nlemleri aras\u0131nda, sorgu bazl\u0131 izolasyon, bellek segmentasyonu ve \u00e7\u0131kt\u0131 denetimi yer al\u0131r. Bu mekanizmalar, hem model yan\u0131t\u0131n\u0131n hem de arka u\u00e7 veri kayna\u011f\u0131n\u0131n birbirinden g\u00fcvenli \u015fekilde ayr\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>\n

\n

\u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
    \n
  • Performans:<\/strong> S\u0131z\u0131nt\u0131lar, modelin do\u011fruluk seviyesini olumsuz etkileyebilir. <\/li>\n
  • G\u00fcvenilirlik:<\/strong> Sistem yan\u0131tlar\u0131n\u0131n tutarl\u0131l\u0131\u011f\u0131, bilgi gizlili\u011fiyle do\u011frudan ili\u015fkilidir. <\/li>\n
  • Maliyet:<\/strong> G\u00fcvenlik ihlalleri, veri d\u00fczeltme ve hukuki s\u00fcre\u00e7lerde b\u00fcy\u00fck kay\u0131plar do\u011furabilir. <\/li>\n
  • \u00d6l\u00e7ekleme:<\/strong> G\u00fcvensiz yap\u0131 b\u00fcy\u00fcd\u00fck\u00e7e risk zinciri geni\u015fler. <\/li>\n
  • Otomasyon:<\/strong> G\u00fcvenli otomasyon ak\u0131\u015flar\u0131 veri b\u00fct\u00fcnl\u00fc\u011f\u00fcne dayan\u0131r. <\/li>\n
  • Karar alma:<\/strong> Verinin do\u011frulu\u011fu, y\u00f6neticilerin stratejik kararlar\u0131nda merkezi rol oynar. <\/li>\n
  • Operasyonel verimlilik:<\/strong> G\u00fcvenlik kusurlar\u0131, s\u00fcre\u00e7lerin yava\u015flamas\u0131na ve ek ekip y\u00fcklerine neden olur.<\/li>\n<\/ul>\n
    \n

    Bu kavram NeKu.AI i\u00e7inde nas\u0131l uygulan\u0131r<\/strong><\/h3>\n

    NeKu.AI platform vizyonu, i\u015fletmelerin yapay zek\u00e2 s\u00fcre\u00e7lerinde veri gizlili\u011fini i\u015f mant\u0131\u011f\u0131na entegre etmeye dayan\u0131r. Sistem, model etkile\u015fimlerini denetleyen veri koruma katmanlar\u0131yla \u00e7al\u0131\u015f\u0131r. \u00d6rne\u011fin, kullan\u0131c\u0131 sorgular\u0131 \u00f6nce anonimle\u015ftirme ve eri\u015fim kontrol mod\u00fcllerinden ge\u00e7er, ard\u0131ndan LLM yan\u0131t\u0131 ayr\u0131 bir g\u00fcvenlik denetiminden ge\u00e7irilir. Bu yakla\u015f\u0131m, kurumsal AI mimarilerinde g\u00fcvenli prompt m\u00fchendisli\u011fi ve denetimli \u00e7\u0131kt\u0131 y\u00f6netimi i\u00e7in bir temel olu\u015fturur.<\/p>\n

    \n

    CTO, CIO, \u00fcr\u00fcn y\u00f6neticileri i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
      \n
    1. Sorun:<\/strong> Bir finans kurumunun LLM tabanl\u0131 raporlama asistan\u0131, dahili belgelerdeki gizli verileri \u00f6\u011frenmi\u015f. <\/li>\n
    2. Ba\u011flam:<\/strong> Model, belge \u00f6zetleme yaparken y\u00f6neticilerin \u00f6zel yaz\u0131\u015fmalar\u0131n\u0131 da analiz etmi\u015f. <\/li>\n
    3. Kavram\u0131n uygulanmas\u0131:<\/strong> Data leakage ai<\/em> riskini \u00f6nlemek i\u00e7in NeKu.AI benzeri \u00e7ok katmanl\u0131 g\u00fcvenlik yap\u0131s\u0131 kurulmu\u015f; veri kaynaklar\u0131 segmentlere ayr\u0131lm\u0131\u015f ve hassas alanlar maskelenmi\u015f. <\/li>\n
    4. Sonu\u00e7:<\/strong> Model yaln\u0131zca kamuya a\u00e7\u0131k verilerle \u00fcretim yapabilir hale gelmi\u015f. <\/li>\n
    5. \u0130\u015f etkisi:<\/strong> Veri g\u00fcvenli\u011fi artm\u0131\u015f, reg\u00fclasyon uyumu sa\u011flanm\u0131\u015f ve raporlama s\u00fcresi %30 k\u0131salm\u0131\u015f.<\/li>\n<\/ol>\n
      \n

      S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n
        \n
      • Model e\u011fitiminde ham \u00fcretim verilerini do\u011frudan kullanmak. <\/li>\n
      • API entegrasyonlar\u0131nda eri\u015fim kontrol\u00fc eksikli\u011fi. <\/li>\n
      • Prompt tasar\u0131m\u0131 s\u0131ras\u0131nda hassas bilgi kal\u0131plar\u0131n\u0131 d\u0131\u015fa vurmak.
        \nEn iyi uygulamalar:<\/strong> <\/li>\n
      • T\u00fcm veri ak\u0131\u015f\u0131n\u0131 denetleyen g\u00fcvenlik proxy katmanlar\u0131 kullanmak. <\/li>\n
      • S\u00fcrekli model izleme (monitoring) ve denetim raporlar\u0131 olu\u015fturmak. <\/li>\n
      • Kullan\u0131c\u0131 girdilerini dinamik maskeleme ve anonimle\u015ftirme mekanizmalar\u0131yla korumak.<\/li>\n<\/ul>\n
        \n

        Sonu\u00e7<\/strong><\/h3>\n

        LLM kullanan sistemlerde data leakage ai<\/em> riski, kurumsal yapay zek\u00e2 d\u00f6n\u00fc\u015f\u00fcm\u00fcn\u00fcn en kritik g\u00fcvenlik ba\u015fl\u0131\u011f\u0131d\u0131r. Bu risk; veri y\u00f6neti\u015fimi, mimari yap\u0131 ve model davran\u0131\u015f\u0131n\u0131n bir arada y\u00f6netilmesiyle azalt\u0131labilir. Kurulu\u015flar, sa\u011flam llm security yap\u0131lar\u0131na ve s\u00fcre\u00e7 tabanl\u0131 denetimlere yat\u0131r\u0131m yaparak hem teknik g\u00fcvenli\u011fi hem de i\u015f s\u00fcreklili\u011fini g\u00fc\u00e7lendirebilir. NeKu.AI yakla\u015f\u0131m\u0131, bu kapsamda g\u00fcvenli AI mimarilerini s\u00fcrd\u00fcr\u00fclebilir ve \u00f6l\u00e7\u00fclebilir hale getirmeyi ama\u00e7lar.<\/p>","protected":false},"excerpt":{"rendered":"

        LLM Kullanan Sistemlerde Data Leakage Riski Giri\u015f LLM kullanan sistemlerde data leakage ai riski, kurumsal verilerin istemsiz \u015fekilde a\u00e7\u0131\u011fa \u00e7\u0131kmas\u0131 ya da \u00fc\u00e7\u00fcnc\u00fc taraflara s\u0131zmas\u0131 anlam\u0131na [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":1082,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[],"tags":[],"class_list":["post-1081","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\nLLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131 - NeKu.AI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/neku.ai\/en\/llm-data-leakage-riski-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131 - NeKu.AI\" \/>\n<meta property=\"og:description\" content=\"LLM Kullanan Sistemlerde Data Leakage Riski Giri\u015f LLM kullanan sistemlerde data leakage ai riski, kurumsal verilerin istemsiz \u015fekilde a\u00e7\u0131\u011fa \u00e7\u0131kmas\u0131 ya da \u00fc\u00e7\u00fcnc\u00fc taraflara s\u0131zmas\u0131 anlam\u0131na [\u2026]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/neku.ai\/en\/llm-data-leakage-riski-2\/\" \/>\n<meta property=\"og:site_name\" content=\"NeKu.AI\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-23T17:02:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-23T17:03:30+00:00\" \/>\n<meta name=\"author\" content=\"Serkan \u00d6zcan\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Serkan \u00d6zcan\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/\"},\"author\":{\"name\":\"Serkan \u00d6zcan\",\"@id\":\"https:\/\/neku.ai\/#\/schema\/person\/cf640cfda3e16635fb740662d943e96b\"},\"headline\":\"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131\",\"datePublished\":\"2026-02-23T17:02:54+00:00\",\"dateModified\":\"2026-02-23T17:03:30+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/\"},\"wordCount\":998,\"publisher\":{\"@id\":\"https:\/\/neku.ai\/#organization\"},\"image\":{\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/\",\"url\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/\",\"name\":\"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131 - NeKu.AI\",\"isPartOf\":{\"@id\":\"https:\/\/neku.ai\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png\",\"datePublished\":\"2026-02-23T17:02:54+00:00\",\"dateModified\":\"2026-02-23T17:03:30+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage\",\"url\":\"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png\",\"contentUrl\":\"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png\",\"width\":1024,\"height\":1024},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/neku.ai\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/neku.ai\/#website\",\"url\":\"https:\/\/neku.ai\/\",\"name\":\"NeKuAI\",\"description\":\"\u0130\u015fletmenizi daha "Ak\u0131ll\u0131" yap\u0131n\",\"publisher\":{\"@id\":\"https:\/\/neku.ai\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/neku.ai\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/neku.ai\/#organization\",\"name\":\"NeKuAI\",\"url\":\"https:\/\/neku.ai\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/neku.ai\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/neku.ai\/wp-content\/uploads\/2025\/02\/apple-icon-180x180-1.png\",\"contentUrl\":\"https:\/\/neku.ai\/wp-content\/uploads\/2025\/02\/apple-icon-180x180-1.png\",\"width\":180,\"height\":180,\"caption\":\"NeKuAI\"},\"image\":{\"@id\":\"https:\/\/neku.ai\/#\/schema\/logo\/image\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\/\/neku.ai\/#\/schema\/person\/cf640cfda3e16635fb740662d943e96b\",\"name\":\"Serkan \u00d6zcan\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/neku.ai\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/neku.ai\/wp-content\/plugins\/swiss-toolkit-for-wp\/\/admin\/img\/default-avatar.png\",\"contentUrl\":\"https:\/\/neku.ai\/wp-content\/plugins\/swiss-toolkit-for-wp\/\/admin\/img\/default-avatar.png\",\"caption\":\"Serkan \u00d6zcan\"},\"url\":\"https:\/\/neku.ai\/en\/author\/serkanozcan\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131 - NeKu.AI","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/neku.ai\/en\/llm-data-leakage-riski-2\/","og_locale":"en_US","og_type":"article","og_title":"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131 - NeKu.AI","og_description":"LLM Kullanan Sistemlerde Data Leakage Riski Giri\u015f LLM kullanan sistemlerde data leakage ai riski, kurumsal verilerin istemsiz \u015fekilde a\u00e7\u0131\u011fa \u00e7\u0131kmas\u0131 ya da \u00fc\u00e7\u00fcnc\u00fc taraflara s\u0131zmas\u0131 anlam\u0131na [\u2026]","og_url":"https:\/\/neku.ai\/en\/llm-data-leakage-riski-2\/","og_site_name":"NeKu.AI","article_published_time":"2026-02-23T17:02:54+00:00","article_modified_time":"2026-02-23T17:03:30+00:00","author":"Serkan \u00d6zcan","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Serkan \u00d6zcan","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#article","isPartOf":{"@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/"},"author":{"name":"Serkan \u00d6zcan","@id":"https:\/\/neku.ai\/#\/schema\/person\/cf640cfda3e16635fb740662d943e96b"},"headline":"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131","datePublished":"2026-02-23T17:02:54+00:00","dateModified":"2026-02-23T17:03:30+00:00","mainEntityOfPage":{"@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/"},"wordCount":998,"publisher":{"@id":"https:\/\/neku.ai\/#organization"},"image":{"@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage"},"thumbnailUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png","inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/","url":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/","name":"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131 - NeKu.AI","isPartOf":{"@id":"https:\/\/neku.ai\/#website"},"primaryImageOfPage":{"@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage"},"image":{"@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage"},"thumbnailUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png","datePublished":"2026-02-23T17:02:54+00:00","dateModified":"2026-02-23T17:03:30+00:00","breadcrumb":{"@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/neku.ai\/llm-data-leakage-riski-2\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#primaryimage","url":"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png","contentUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2026\/02\/cover-image-1081.png","width":1024,"height":1024},{"@type":"BreadcrumbList","@id":"https:\/\/neku.ai\/llm-data-leakage-riski-2\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/neku.ai\/"},{"@type":"ListItem","position":2,"name":"LLM Sistemlerinde Data Leakage Riskine Kar\u015f\u0131 G\u00fcvenli Yapay Zek\u00e2 Uygulamalar\u0131"}]},{"@type":"WebSite","@id":"https:\/\/neku.ai\/#website","url":"https:\/\/neku.ai\/","name":"NeKuAI","description":"\u0130\u015fletmenizi daha "Ak\u0131ll\u0131" yap\u0131n","publisher":{"@id":"https:\/\/neku.ai\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/neku.ai\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/neku.ai\/#organization","name":"NeKuAI","url":"https:\/\/neku.ai\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/neku.ai\/#\/schema\/logo\/image\/","url":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/02\/apple-icon-180x180-1.png","contentUrl":"https:\/\/neku.ai\/wp-content\/uploads\/2025\/02\/apple-icon-180x180-1.png","width":180,"height":180,"caption":"NeKuAI"},"image":{"@id":"https:\/\/neku.ai\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/neku.ai\/#\/schema\/person\/cf640cfda3e16635fb740662d943e96b","name":"Serkan \u00d6zcan","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/neku.ai\/#\/schema\/person\/image\/","url":"https:\/\/neku.ai\/wp-content\/plugins\/swiss-toolkit-for-wp\/\/admin\/img\/default-avatar.png","contentUrl":"https:\/\/neku.ai\/wp-content\/plugins\/swiss-toolkit-for-wp\/\/admin\/img\/default-avatar.png","caption":"Serkan \u00d6zcan"},"url":"https:\/\/neku.ai\/en\/author\/serkanozcan\/"}]}},"_links":{"self":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts\/1081","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/comments?post=1081"}],"version-history":[{"count":1,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts\/1081\/revisions"}],"predecessor-version":[{"id":1083,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/posts\/1081\/revisions\/1083"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/media\/1082"}],"wp:attachment":[{"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/media?parent=1081"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/categories?post=1081"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neku.ai\/en\/wp-json\/wp\/v2\/tags?post=1081"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}