{"id":70237,"date":"2026-05-08T11:50:07","date_gmt":"2026-05-08T08:50:07","guid":{"rendered":"https:\/\/dtechcloud.com\/yapay-zeka-caginda-veri-guvenligi-kurumlar-golge-yapay-zeka-ve-ic-tehdit-riskine-nasil-hazirlanmali\/"},"modified":"2026-09-21T14:29:57","modified_gmt":"2026-09-21T11:29:57","slug":"yapay-zeka-caginda-veri-guvenligi-kurumlar-golge-yapay-zeka-ve-ic-tehdit-riskine-nasil-hazirlanmali","status":"publish","type":"post","link":"https:\/\/dtechcloud.com\/tr\/yapay-zeka-caginda-veri-guvenligi-kurumlar-golge-yapay-zeka-ve-ic-tehdit-riskine-nasil-hazirlanmali\/","title":{"rendered":"Yapay Zek\u00e2 \u00c7a\u011f\u0131nda Veri G\u00fcvenli\u011fi: Kurumlar G\u00f6lge Yapay Zek\u00e2 ve \u0130\u00e7 Tehdit Riskine Nas\u0131l Haz\u0131rlanmal\u0131"},"content":{"rendered":"\t\t
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Yeni Bir G\u00f6r\u00fcnmez Risk T\u00fcr\u00fc<\/strong><\/h4>

2023 y\u0131l\u0131nda bir Samsung m\u00fchendisi, bir sorunu ay\u0131klamak i\u00e7in tescilli kaynak kodunu ChatGPT’ye yap\u0131\u015ft\u0131rd\u0131. Kod ve ondan \u00e7\u0131kar\u0131labilecek her \u015fey, \u00fc\u00e7\u00fcnc\u00fc bir taraf\u00e7a i\u015fletilen ticari bir yapay zek\u00e2 sisteminin e\u011fitim verilerinin par\u00e7as\u0131 haline geldi. Samsung’un neyin payla\u015f\u0131ld\u0131\u011f\u0131na dair hi\u00e7bir g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc, m\u00fchendisin bilin\u00e7li olarak ihlal etti\u011fi bir politikas\u0131 ve olan\u0131 tespit etmeye ya da \u00f6nlemeye y\u00f6nelik bir mekanizmas\u0131 yoktu. <\/p>

Bu sofistike bir sald\u0131r\u0131 de\u011fildi. K\u00f6t\u00fc niyetli bir i\u00e7 tehdit de de\u011fildi. \u0130\u015fini daha verimli yapmaya \u00e7al\u0131\u015fan bir \u00e7al\u0131\u015fan\u0131n, yapt\u0131\u011f\u0131 \u015feyin ciddi risk ta\u015f\u0131d\u0131\u011f\u0131n\u0131n fark\u0131nda olmadan elindeki en iyi arac\u0131 kullanmas\u0131yd\u0131. <\/p>

Bu hik\u00e2ye, g\u00fcvenlik liderlerinin art\u0131k her sekt\u00f6rde bo\u011fu\u015ftu\u011fu daha geni\u015f bir olgunun simgesi haline geldi: G\u00f6lge Yapay Zek\u00e2<\/strong><\/em>. Bu olgu, i\u00e7inde bulundu\u011fumuz on y\u0131l\u0131n yap\u0131sal olarak en zor g\u00fcvenlik sorunlar\u0131ndan birini temsil ediyor; \u00e7\u00fcnk\u00fc onu tetikleyen davran\u0131\u015f yaln\u0131zca anla\u015f\u0131labilir de\u011fil, ayn\u0131 zamanda rasyonel. <\/p>

Yapay zek\u00e2 ara\u00e7lar\u0131n\u0131 kullanan \u00e7al\u0131\u015fanlar \u00e7o\u011fu durumda daha \u00fcretken. Daha iyi i\u015fleri daha h\u0131zl\u0131 \u00fcretiyor, daha \u00f6nce uzman deste\u011fi gerektiren sorunlar\u0131 \u00e7\u00f6z\u00fcyorlar. Yapay zek\u00e2y\u0131 benimsemeye y\u00f6nelik kurumsal bask\u0131 \u00e7ok b\u00fcy\u00fck ve yetkili olsun ya da olmas\u0131n onu kullanman\u0131n yolunu bulan \u00e7al\u0131\u015fanlar, y\u00f6ntemden ba\u011f\u0131ms\u0131z olarak \u00e7o\u011fu zaman sonu\u00e7lar i\u00e7in \u00f6d\u00fcllendiriliyor. <\/p>

Bu durum, tehdidin kurumun d\u0131\u015f\u0131ndan ya da k\u00f6t\u00fc niyetli, k\u00fcsk\u00fcn \u00e7al\u0131\u015fanlardan gelmedi\u011fi bir g\u00fcvenlik ortam\u0131 yarat\u0131yor. Tehdit; son derece motive, y\u00fcksek performansl\u0131 ve tam olarak \u00f6rt\u00fck bi\u00e7imde te\u015fvik edildikleri \u015feyi yapan \u00e7al\u0131\u015fanlardan geliyor. <\/p>

Buna kar\u015f\u0131 savunma, geleneksel g\u00fcvenlik d\u00fc\u015f\u00fcncesinin sundu\u011fundan temelden farkl\u0131 bir yakla\u015f\u0131m gerektiriyor.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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G\u00f6lge Yapay Zek\u00e2 Tehdit Y\u00fczeyini Tan\u0131mlamak<\/strong><\/span><\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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G\u00f6lge Yapay Zek\u00e2; b\u00fcy\u00fck dil modelleri, yapay zek\u00e2 destekli kodlama platformlar\u0131, g\u00f6rsel \u00fcreticiler, belge \u00f6zetleyiciler ve yapay zek\u00e2 destekli \u00fcretkenlik uygulamalar\u0131 gibi yapay zek\u00e2 ara\u00e7lar\u0131n\u0131n a\u00e7\u0131k bir kurumsal yetkilendirme veya y\u00f6neti\u015fim olmadan kullan\u0131lmas\u0131n\u0131 ifade eder.<\/p>

Bu, G\u00f6lge BT’nin kurumsal kar\u015f\u0131l\u0131\u011f\u0131d\u0131r: \u00e7al\u0131\u015fanlar\u0131n yava\u015f veya yetersiz resmi ara\u00e7lar\u0131 a\u015fmak i\u00e7in yetkisiz bulut hizmetleri ve uygulamalar\u0131 benimsemesi olgusu. G\u00f6lge BT erken bulut d\u00f6neminde ortaya \u00e7\u0131kt\u0131 ve \u00e7o\u011fu kurumun onu anlamas\u0131 ve y\u00f6netmesi y\u0131llar ald\u0131. G\u00f6lge Yapay Zek\u00e2 daha h\u0131zl\u0131 ilerliyor, ara\u00e7lar daha yetenekli ve verilerin a\u00e7\u0131\u011fa \u00e7\u0131kma riskleri kategorik olarak \u00e7ok daha ciddi. <\/p>

G\u00f6lge Yapay Zek\u00e2’n\u0131n yaratt\u0131\u011f\u0131 tehdit y\u00fczeyi birka\u00e7 boyutta i\u015fler.<\/p>

Yapay zek\u00e2 komutlar\u0131 (prompt) \u00fczerinden veri s\u0131zd\u0131rma<\/strong><\/h5>

Bir \u00e7al\u0131\u015fan hassas bir belgeyi \u00f6zetlenmesi, \u00e7evrilmesi veya analiz edilmesi i\u00e7in harici bir yapay zek\u00e2 arac\u0131na yap\u0131\u015ft\u0131rd\u0131\u011f\u0131nda, bu i\u00e7erik kurumsal s\u0131n\u0131r\u0131n d\u0131\u015f\u0131na \u00e7\u0131kar. Yapay zek\u00e2 sa\u011flay\u0131c\u0131s\u0131n\u0131n veri saklama ve e\u011fitim politikalar\u0131na ba\u011fl\u0131 olarak bu i\u00e7erik s\u00fcresiz saklanabilir, insanlar taraf\u0131ndan incelenebilir veya model e\u011fitimine dahil edilebilir. <\/p>

\u00c7al\u0131\u015fan, i\u015flevsel olarak hassas verileri bir rakibe ya da k\u00f6t\u00fc niyetli bir akt\u00f6re de\u011fil, kurumun gizlilik y\u00fck\u00fcml\u00fcl\u00fckleri, mevzuat gereksinimleri veya s\u00f6zle\u015fmesel taahh\u00fctleriyle tamamen uyumsuz olabilecek kendi veri y\u00f6neti\u015fim politikalar\u0131na sahip \u00fc\u00e7\u00fcnc\u00fc taraf bir yapay zek\u00e2 sistemine s\u0131zd\u0131rm\u0131\u015ft\u0131r.<\/p>

Yapay zek\u00e2 \u00e7\u0131kt\u0131lar\u0131nda hassas veriler<\/strong><\/h5>

Geni\u015f internet verileriyle e\u011fitilen yapay zek\u00e2 ara\u00e7lar\u0131, zaman zaman e\u011fitim k\u00fcmelerine istemeden dahil olmu\u015f bilgileri ortaya \u00e7\u0131karabilir. \u00c7al\u0131\u015fanlar harici yapay zek\u00e2 ara\u00e7lar\u0131n\u0131 kulland\u0131\u011f\u0131nda; rakipler, i\u015f ortaklar\u0131 ve hatta kendi kurumlar\u0131 hakk\u0131nda herkese a\u00e7\u0131k e\u011fitim verilerine s\u0131zm\u0131\u015f bilgiler i\u00e7eren \u00e7\u0131kt\u0131lar alabilirler. <\/p>

Daha \u00f6nemlisi, \u00e7al\u0131\u015fanlar tescilli verileri yapay zek\u00e2 sistemleriyle payla\u015ft\u0131\u011f\u0131nda, bu sistemler s\u00f6z konusu verilerin par\u00e7alar\u0131n\u0131 daha sonra ba\u015fka kullan\u0131c\u0131lara (potansiyel olarak rakipler de dahil) yapay zek\u00e2n\u0131n kendi genel bilgisi gibi g\u00f6r\u00fcnen \u00e7\u0131kt\u0131lar arac\u0131l\u0131\u011f\u0131yla sunabilir.<\/p>

\u00dc\u00e7\u00fcnc\u00fc taraf yapay zek\u00e2 entegrasyon riskleri<\/strong><\/h5>

Do\u011frudan komut tabanl\u0131 etkile\u015fimlerin \u00f6tesinde, bir\u00e7ok \u00e7al\u0131\u015fan yapay zek\u00e2 ara\u00e7lar\u0131n\u0131 OAuth entegrasyonlar\u0131 ve \u00fc\u00e7\u00fcnc\u00fc taraf uygulama ba\u011flant\u0131lar\u0131 arac\u0131l\u0131\u011f\u0131yla e-posta hesaplar\u0131, takvim sistemleri, belge depolar\u0131 ve CRM platformlar\u0131 gibi kurumsal sistemlere do\u011frudan ba\u011fl\u0131yor. Bu entegrasyonlar, yapay zek\u00e2 ara\u00e7lar\u0131na tek bir komutun \u00e7ok \u00f6tesine ge\u00e7en, kurumsal verilere kal\u0131c\u0131 eri\u015fim sa\u011fl\u0131yor. <\/p>

Bir yapay zek\u00e2 \u00fcretkenlik arac\u0131n\u0131 kurumsal e-posta hesab\u0131na ba\u011flayan bir \u00e7al\u0131\u015fan, o araca hassas m\u00fczakereler, stratejik planlama tart\u0131\u015fmalar\u0131 ve gizli personel konular\u0131 da dahil olmak \u00fczere y\u0131llara yay\u0131lan kurumsal ileti\u015fimlere okuma eri\u015fimi vermi\u015f olabilir.<\/p>

Yapay zek\u00e2 destekli i\u00e7 tehdit g\u00fc\u00e7lendirmesi<\/strong><\/h5>

G\u00f6lge Yapay Zek\u00e2 yaln\u0131zca pasif veri a\u00e7\u0131\u011fa \u00e7\u0131kma riskleri yaratmaz. Ayn\u0131 zamanda k\u00f6t\u00fc niyetli veya ihmalk\u00e2r i\u00e7 akt\u00f6rlerin yeteneklerini de g\u00fc\u00e7lendirir. Kurumdan ayr\u0131lmadan \u00f6nce veri s\u0131zd\u0131rmay\u0131 planlayan bir \u00e7al\u0131\u015fan, belgeleri manuel incelemenin izin verece\u011finden \u00e7ok daha verimli bi\u00e7imde analiz etmek, \u00f6zetlemek ve onlardan de\u011fer \u00e7\u0131karmak i\u00e7in yapay zek\u00e2 ara\u00e7lar\u0131n\u0131 kullanabilir. \u0130zlerini gizlemek isteyen bir \u00e7al\u0131\u015fan ise makul g\u00f6r\u00fcnen bahaneler \u00fcretmek veya meta verileri manip\u00fcle etmek i\u00e7in yapay zek\u00e2dan yararlanabilir. <\/p>

Bu g\u00fc\u00e7lendirme etkisi, yapay zek\u00e2 \u00f6ncesi bir ortam i\u00e7in tasarlanm\u0131\u015f i\u00e7 tehdit y\u00f6netimi programlar\u0131n\u0131n, bug\u00fcn kurumlar\u0131nda ger\u00e7ekle\u015fen veri s\u0131zd\u0131rma faaliyetlerinin hem hacmini hem de karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 b\u00fcy\u00fck olas\u0131l\u0131kla hafife ald\u0131\u011f\u0131 anlam\u0131na gelir.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\"\"<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Geleneksel G\u00fcvenlik Yakla\u015f\u0131mlar\u0131 Bunu Neden G\u00f6remiyor<\/strong><\/h4>

G\u00f6lge Yapay Zek\u00e2’n\u0131n temel zorlu\u011fu, geleneksel g\u00fcvenlik kontrolleri i\u00e7in b\u00fcy\u00fck \u00f6l\u00e7\u00fcde g\u00f6r\u00fcnmez olmas\u0131d\u0131r.<\/p>

Veri Kayb\u0131 \u00d6nleme (DLP) sistemleri<\/strong>; kredi kart\u0131 numaralar\u0131, sosyal g\u00fcvenlik numaralar\u0131, IBAN kodlar\u0131 ve belirli belge s\u0131n\u0131fland\u0131rmalar\u0131 gibi bilinen hassas veri kal\u0131plar\u0131n\u0131n iletimini tespit edip engellemek i\u00e7in tasarland\u0131. \u0130zlenen kanallardan ge\u00e7en a\u00e7\u0131k, tan\u0131nabilir hassas i\u00e7eri\u011fi yakalamakta etkilidirler. <\/p>

Ancak bir \u00e7al\u0131\u015fan\u0131n s\u0131n\u0131fland\u0131r\u0131lmam\u0131\u015f fakat stratejik a\u00e7\u0131dan hassas bir metni taray\u0131c\u0131 tabanl\u0131 bir yapay zek\u00e2 arac\u0131na kopyalamas\u0131n\u0131 tespit etmek i\u00e7in tasarlanmam\u0131\u015flard\u0131r. Harici bir yapay zek\u00e2 hizmetine g\u00f6nderilen bir komutun, tan\u0131ml\u0131 bir hassas kal\u0131pla e\u015fle\u015fmese de de\u011ferli fikri m\u00fclkiyet i\u00e7erdi\u011fini de\u011ferlendiremezler. Tan\u0131nan s\u0131zd\u0131rma kanallar\u0131 yerine yetkili ba\u011flant\u0131lar \u00fczerinden verilere eri\u015fen OAuth entegrasyonlu uygulamalara ili\u015fkin g\u00f6r\u00fcn\u00fcrl\u00fckleri s\u0131n\u0131rl\u0131d\u0131r. <\/p>

\u00c7\u0131k\u0131\u015f katman\u0131ndaki a\u011f izleme ve DLP<\/strong>, bilinen yapay zek\u00e2 hizmetlerine giden trafi\u011fi tan\u0131mlayabilir; ancak yapay zek\u00e2 sa\u011flay\u0131c\u0131lar\u0131na giden t\u00fcm trafi\u011fi engellemek giderek daha az uygulanabilir hale geliyor. Bir\u00e7ok yapay zek\u00e2 yetene\u011fi kurumlar\u0131n me\u015fru olarak kulland\u0131\u011f\u0131 ara\u00e7lara g\u00f6m\u00fcl\u00fc ve toptan engelleme, \u00e7al\u0131\u015fanlar\u0131 daha da az g\u00f6r\u00fcn\u00fcr ge\u00e7ici \u00e7\u00f6z\u00fcmlere iten t\u00fcrden bir s\u00fcrt\u00fcnme yarat\u0131yor.<\/p>

Geleneksel i\u00e7 tehdit programlar\u0131<\/strong>, veri s\u0131zd\u0131rman\u0131n g\u00f6rece yava\u015f ve manuel oldu\u011fu bir d\u00fcnya i\u00e7in kalibre edilmi\u015f davran\u0131\u015f modelleri etraf\u0131nda kuruldu. Dosyalar\u0131 manuel olarak bir USB belle\u011fe kopyalayan veya belgeleri ki\u015fisel e-posta hesab\u0131na ileten bir \u00e7al\u0131\u015fan, i\u00e7 tehdit ara\u00e7lar\u0131n\u0131n tespit etmek \u00fczere tasarland\u0131\u011f\u0131 bir davran\u0131\u015f imzas\u0131 \u00fcretir. Y\u00fczlerce belgeyi dakikalar i\u00e7inde analiz edip i\u00e7g\u00f6r\u00fc \u00e7\u0131karmak i\u00e7in yapay zek\u00e2 kullanan bir \u00e7al\u0131\u015fan ise temelden farkl\u0131, h\u0131rs\u0131zl\u0131ktan \u00e7ok \u00fcretken \u00e7al\u0131\u015fmaya benzeyen bir davran\u0131\u015f kal\u0131b\u0131 \u00fcretir. <\/p>

Sonu\u00e7, \u00f6nemli bir tespit bo\u015flu\u011fudur. G\u00fcvenlik programlar\u0131 geleneksel bir i\u00e7 tehdidi veya veri s\u0131zd\u0131rma olay\u0131n\u0131 tespit edebileceklerine dair g\u00fcven \u00fcretirken, G\u00f6lge Yapay Zek\u00e2 faaliyeti neredeyse tamamen g\u00f6r\u00fcn\u00fcrl\u00fcklerinin d\u0131\u015f\u0131nda kalan bir risk kategorisi yarat\u0131r. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Davran\u0131\u015fsal Sinyal: \u0130\u00e7 Tehdit Risk Y\u00f6netimi Ne G\u00f6r\u00fcr<\/strong><\/h4>

Bu bo\u015flu\u011fa yan\u0131t, i\u00e7erik merkezli tespitten davran\u0131\u015f merkezli tespite ge\u00e7i\u015f gerektirir ve \u0130\u00e7 Tehdit Risk Y\u00f6netimi (Insider Risk Management) tam da bu noktada G\u00f6lge Yapay Zek\u00e2 g\u00fcvenlik duru\u015funun merkezine yerle\u015fir.<\/p>

Microsoft Purview Insider Risk Management, veri g\u00fcvenli\u011fi tehditlerini tespit etmeye temelden farkl\u0131 bir yakla\u015f\u0131m getirir. \u0130letilen belirli hassas i\u00e7eri\u011fi tan\u0131mlamaya \u00e7al\u0131\u015fmak yerine, kullan\u0131c\u0131lar\u0131n davran\u0131\u015f kal\u0131plar\u0131n\u0131 zaman i\u00e7inde modeller ve bu kal\u0131plardan y\u00fcksek risk i\u015fareti olabilecek sapmalar\u0131 belirler. <\/p>

Bu yakla\u015f\u0131m, hangi i\u00e7eri\u011fin hassas oldu\u011funu veya hangi kanallar\u0131n kullan\u0131laca\u011f\u0131n\u0131 \u00f6nceden bilmeyi gerektirmedi\u011fi i\u00e7in G\u00f6lge Yapay Zek\u00e2 tehdit y\u00fczeyine \u00f6zellikle uygundur. Davran\u0131\u015f\u0131 g\u00f6zlemler; \u00e7al\u0131\u015fanlar ola\u011fand\u0131\u015f\u0131 bir \u015fey yapt\u0131\u011f\u0131nda davran\u0131\u015f de\u011fi\u015fir \u2014 bu ola\u011fand\u0131\u015f\u0131 faaliyet k\u00f6t\u00fc niyetli, ihmalk\u00e2r ya da yaln\u0131zca risklerin fark\u0131nda olmayan bir davran\u0131\u015f olsun. <\/p>

IRM’nin ili\u015fkilendirdi\u011fi sinyaller<\/strong><\/h5>

IRM, her kullan\u0131c\u0131 i\u00e7in bir davran\u0131\u015f taban \u00e7izgisi olu\u015fturmak ve bu taban \u00e7izgisine g\u00f6re anomalileri belirlemek i\u00e7in Microsoft 365 ortam\u0131n\u0131n ve ba\u011fl\u0131 \u00fc\u00e7\u00fcnc\u00fc taraf sistemlerin tamam\u0131ndan gelen sinyalleri sentezler.<\/p>

Dosya etkinli\u011fi sinyalleri:<\/strong> Ola\u011fand\u0131\u015f\u0131 hacimde dosya indirme, harici konumlara dosya kopyalama, kullan\u0131c\u0131n\u0131n normalde eri\u015fmedi\u011fi depolara eri\u015fim, kullan\u0131c\u0131n\u0131n ge\u00e7mi\u015f kal\u0131plar\u0131ndan sapan toplu dosya i\u015flemleri.<\/p>

\u0130leti\u015fim sinyalleri: <\/strong>Ola\u011fand\u0131\u015f\u0131 giden e-posta kal\u0131plar\u0131, ki\u015fisel e-posta adresleriyle ileti\u015fim, standart d\u0131\u015f\u0131 ileti\u015fim kanallar\u0131n\u0131n kullan\u0131m\u0131.<\/p>

Taray\u0131c\u0131 ve uygulama etkinli\u011fi:<\/strong> Harici dosya payla\u015f\u0131m hizmetlerine eri\u015fim, kurum taraf\u0131ndan onaylanmam\u0131\u015f bulut depolama kullan\u0131m\u0131, taray\u0131c\u0131 tabanl\u0131 aray\u00fczler \u00fczerinden yapay zek\u00e2 platformlar\u0131yla etkile\u015fim.<\/p>

\u0130K ba\u011flay\u0131c\u0131 sinyalleri:<\/strong> \u0130stifa bildirimleri, i\u015ften \u00e7\u0131k\u0131\u015f tarihleri, performans iyile\u015ftirme planlar\u0131, disiplin i\u015flemleri. Bu ba\u011flamsal sinyaller, davran\u0131\u015fsal anomalilerin anlam\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde netle\u015ftirir. Ola\u011fand\u0131\u015f\u0131 hacimde dosya indiren bir kullan\u0131c\u0131 endi\u015fe vericidir. \u0130stifa bildirimini verdikten iki hafta sonra ola\u011fand\u0131\u015f\u0131 hacimde dosya indiren bir kullan\u0131c\u0131 ise bamba\u015fka bir risk seviyesidir. <\/p>

Dizi tespiti:<\/strong> IRM, sinyalleri tek tek ve yal\u0131t\u0131lm\u0131\u015f bi\u00e7imde de\u011ferlendirmez. Tek ba\u015f\u0131na uyar\u0131 tetiklemeyecek ancak bir arada tutarl\u0131 bir risk senaryosuna i\u015faret eden birbiriyle ili\u015fkili faaliyet dizilerini belirler; \u00f6rne\u011fin dosyalar\u0131 indirip ki\u015fisel OneDrive’a kopyalamak ve ard\u0131ndan harici bir yapay zek\u00e2 hizmetine eri\u015fmek gibi. <\/p>

Gizlili\u011fi koruyan tasar\u0131m<\/strong><\/h5>

\u0130\u00e7 tehdit risk y\u00f6netimi yetkinliklerini devreye alan kurumlar i\u00e7in kritik bir husus, g\u00fcvenlik izleme ile \u00e7al\u0131\u015fan gizlili\u011fi aras\u0131ndaki gerilimdir. Bu gerilim ger\u00e7ektir ve g\u00f6z ard\u0131 edilmesi, me\u015fru hukuki ve etik kayg\u0131lar do\u011furan, bu da operasyonel etkinliklerini zay\u0131flatan programlara yol a\u00e7ar. <\/p>

IRM, gizlili\u011fin korunmas\u0131n\u0131 sonradan eklenen bir \u00f6zellik de\u011fil, yap\u0131sal bir \u00f6zellik olarak tasarlanm\u0131\u015ft\u0131r. Kullan\u0131c\u0131 kimlikleri varsay\u0131lan olarak takma adland\u0131r\u0131l\u0131r; uyar\u0131lar\u0131 inceleyen analistler, risk kan\u0131tlar\u0131 anonimli\u011fin kald\u0131r\u0131lmas\u0131n\u0131 gerektirecek bir e\u015fi\u011fe ula\u015fana kadar \u00e7al\u0131\u015fan adlar\u0131 yerine anonim tan\u0131mlay\u0131c\u0131lar g\u00f6r\u00fcr. Bu, davran\u0131\u015fsal anomalileri ger\u00e7ek riskten \u00e7ok masum a\u00e7\u0131klamalar\u0131 yans\u0131tan kullan\u0131c\u0131lar\u0131n b\u00fcy\u00fck \u00e7o\u011funlu\u011fu i\u00e7in anlaml\u0131 bir gizlilik korumas\u0131 sa\u011flar. <\/p>

Takma adland\u0131rma mimarisi, g\u00fc\u00e7l\u00fc \u00e7al\u0131\u015fan gizlili\u011fi yasalar\u0131na sahip \u00fclkelerdeki kurumlar i\u00e7in hukuki ve d\u00fczenleyici koruma da sa\u011flar; k\u0131s\u0131tlamas\u0131z g\u00f6zetimin yarataca\u011f\u0131 hukuki riske yol a\u00e7madan anlaml\u0131 bir i\u00e7 tehdit izlemesine imk\u00e2n tan\u0131r.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Yapay Zek\u00e2 i\u00e7in DSPM: G\u00f6remedi\u011finizi Y\u00f6netmek<\/strong><\/h4>

Davran\u0131\u015fsal izleme, \u00e7al\u0131\u015fanlar\u0131n yapay zek\u00e2 ara\u00e7lar\u0131yla ne yapt\u0131\u011f\u0131n\u0131 ele al\u0131r. Ancak yapay zek\u00e2 ara\u00e7lar\u0131n\u0131n kurumsal verilerle ne yapt\u0131\u011f\u0131 sorusunu, \u00f6zellikle bu ara\u00e7lar yetkili kurumsal sistemlere g\u00f6m\u00fcl\u00fc oldu\u011funda, ele almaz. <\/p>

Kurumlar Microsoft 365 Copilot, Azure OpenAI Service ve do\u011frudan teknoloji y\u0131\u011f\u0131nlar\u0131na entegre edilen di\u011fer yapay zek\u00e2 yeteneklerini benimsedik\u00e7e yeni bir y\u00f6neti\u015fim zorlu\u011fu ortaya \u00e7\u0131k\u0131yor. Bunlar G\u00f6lge Yapay Zek\u00e2 ara\u00e7lar\u0131 de\u011fil; yetkili, onayl\u0131 ve BT taraf\u0131ndan y\u00f6netilen sistemler. Ancak yaratt\u0131klar\u0131 veri ak\u0131\u015flar\u0131 karma\u015f\u0131k, \u00e7o\u011fu zaman opak ve hassas bilgileri geleneksel veri y\u00f6neti\u015fimi \u00e7er\u00e7evelerinin ele almak \u00fczere tasarlanmad\u0131\u011f\u0131 bi\u00e7imlerde a\u00e7\u0131\u011fa \u00e7\u0131karma potansiyeline sahip. <\/p>

Microsoft Purview Yapay Zek\u00e2 i\u00e7in Veri G\u00fcvenli\u011fi Duru\u015f Y\u00f6netimi (DSPM for AI), bu zorlu\u011fu ele almak \u00fczere tasarlanm\u0131\u015ft\u0131r. Kurum i\u00e7indeki yapay zek\u00e2 sistemlerinin verilerle nas\u0131l etkile\u015fti\u011fine, yapay zek\u00e2 arac\u0131l\u0131 s\u00fcre\u00e7lerden hangi verilerin ge\u00e7ti\u011fine ve yapay zek\u00e2 y\u0131\u011f\u0131n\u0131nda veri g\u00fcvenli\u011fi risklerinin nerede bulundu\u011funa ili\u015fkin g\u00f6r\u00fcn\u00fcrl\u00fck sa\u011flar. <\/p>

DSPM for AI neleri ortaya \u00e7\u0131kar\u0131r<\/strong><\/h5>

Yapay zek\u00e2 kullan\u0131m kal\u0131plar\u0131:<\/strong> Kurum genelinde hangi yapay zek\u00e2 ara\u00e7lar\u0131n\u0131n, hangi ekipler ve ki\u015filer taraf\u0131ndan, hangi s\u0131kl\u0131kla kullan\u0131ld\u0131\u011f\u0131. Bu g\u00f6r\u00fcn\u00fcrl\u00fck temeldir; kurumlar g\u00f6remedikleri yapay zek\u00e2 kullan\u0131m\u0131n\u0131 y\u00f6netemez. <\/p>

Yapay zek\u00e2 sistemlerine veri girdileri:<\/strong> Yapay zek\u00e2 ara\u00e7lar\u0131na komut olarak ne t\u00fcr verilerin g\u00f6nderildi\u011fi, bu girdilerde hangi hassas bilgilerin bulundu\u011fu ve yapay zek\u00e2 etkile\u015fimlerinde veri y\u00f6neti\u015fimi politikalar\u0131na uyulup uyulmad\u0131\u011f\u0131.<\/p>

Risk alt\u0131ndaki korumas\u0131z veriler:<\/strong> DSPM for AI, uygun hassasiyet etiketleri veya koruma politikalar\u0131 bulunmayan ve bu nedenle yeterli kontroller olmadan yapay zek\u00e2 sistemleri taraf\u0131ndan eri\u015filebilir olma potansiyeli ta\u015f\u0131yan kurumsal verileri otomatik olarak tarar. Yapay zek\u00e2 ara\u00e7lar\u0131 etiketlenmemi\u015f, korumas\u0131z verilere eri\u015febildi\u011finde, yapay zek\u00e2 \u00e7\u0131kt\u0131lar\u0131 yoluyla istenmeyen a\u00e7\u0131\u011fa \u00e7\u0131kma riski \u00f6nemli \u00f6l\u00e7\u00fcde artar. <\/p>

Yapay zek\u00e2 ba\u011flamlar\u0131nda mevzuata uyum:<\/strong> Yapay zek\u00e2ya \u00f6zg\u00fc d\u00fczenleyici gereksinimler k\u00fcresel olarak ortaya \u00e7\u0131kt\u0131k\u00e7a DSPM for AI, kurumsal yapay zek\u00e2 veri uygulamalar\u0131n\u0131 ge\u00e7erli \u00e7er\u00e7evelerle e\u015fle\u015ftirir; uyum ekiplerinin risklerini anlamas\u0131n\u0131 ve uygun kontrolleri uygulamas\u0131n\u0131 sa\u011flar.<\/p>

Hassasiyet etiketi devralma sorunu<\/strong><\/h5>

Yapay zek\u00e2 ba\u011flam\u0131nda pratik a\u00e7\u0131dan en \u00f6nemli veri g\u00fcvenli\u011fi zorluklar\u0131ndan biri hassasiyet etiketi devralmad\u0131r. Microsoft Purview, kurumlar\u0131n belgelere ve verilere \u201cGizli\u201d<\/strong><\/em> veya \u201c\u00c7ok Gizli\u201d<\/em><\/strong> gibi, uygun koruma politikalar\u0131n\u0131 tetikleyen hassasiyet etiketleri uygulamas\u0131n\u0131 sa\u011flar. <\/p>

Microsoft 365 Copilot etiketli kaynak materyallere dayanarak i\u00e7erik \u00fcretti\u011finde, bu kaynak materyallerin hassasiyet s\u0131n\u0131fland\u0131rmas\u0131n\u0131 devral\u0131r. Birka\u00e7 \u00e7ok gizli strateji belgesini \u00f6zetleyen bir belge de \u00e7ok gizli olarak s\u0131n\u0131fland\u0131r\u0131lmal\u0131d\u0131r ve Copilot’un davran\u0131\u015f\u0131 bunu yans\u0131t\u0131r. <\/p>

Ancak \u00e7al\u0131\u015fanlar harici yapay zek\u00e2 ara\u00e7lar\u0131yla etkile\u015fime girdi\u011finde bu devralma zinciri k\u0131r\u0131l\u0131r. Harici yapay zek\u00e2 sistemleri taraf\u0131ndan \u00fcretilen i\u00e7erik, \u00fcretime kaynakl\u0131k eden materyallerin hassasiyet etiketlerini ta\u015f\u0131maz. \u00c7\u0131kt\u0131 s\u0131n\u0131fland\u0131r\u0131lmam\u0131\u015f i\u00e7erik olarak g\u00f6r\u00fcn\u00fcr ve onu olu\u015fturan hassas girdilerle ba\u011flant\u0131s\u0131 veri y\u00f6neti\u015fimi sistemleri i\u00e7in g\u00f6r\u00fcnmezdir. <\/p>

DSPM for AI bu sorunu, yapay zek\u00e2 sistemlerine hangi verilerin girdi\u011fine ili\u015fkin g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc koruyarak ve hassas kaynaklardan t\u00fcretilen yapay zek\u00e2 \u00fcretimi i\u00e7eri\u011fin uygun \u015fekilde ele al\u0131nmas\u0131n\u0131 sa\u011flayan y\u00f6neti\u015fim kontrolleri olu\u015fturarak ele al\u0131r.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Uyarlanabilir Koruma: Risk \u0130stihbarat\u0131n\u0131n Politika Uygulamas\u0131yla Bulu\u015ftu\u011fu Yer<\/strong><\/h4>

Davran\u0131\u015fsal risk tespitinin karma\u015f\u0131kl\u0131\u011f\u0131, yaln\u0131zca tespit edilen riske yan\u0131t olarak ger\u00e7ekten de\u011fi\u015fen g\u00fcvenlik kontrollerine d\u00f6n\u00fc\u015ft\u00fc\u011f\u00fcnde de\u011ferlidir. Uyarlanabilir Koruma’n\u0131n i\u015flevi budur ve mevcut ara\u00e7 neslindeki veri g\u00fcvenli\u011fi mimarisinin en \u00f6nemli evrimini temsil eder.

<\/p>

Geleneksel DLP statiktir. Bir politika ya bir kullan\u0131c\u0131ya uygulan\u0131r ya da uygulanmaz. Hi\u00e7bir davran\u0131\u015fsal anomalisi olmayan g\u00fcvenilir, k\u0131demli bir \u00e7al\u0131\u015fan\u0131n davran\u0131\u015f\u0131yla, istifas\u0131n\u0131 yeni vermi\u015f ve \u00fc\u00e7 g\u00fcnd\u00fcr ola\u011fand\u0131\u015f\u0131 bir h\u0131zda dosya indiren bir \u00e7al\u0131\u015fan\u0131n davran\u0131\u015f\u0131n\u0131 ayn\u0131 kontroller y\u00f6netir. Kurumlarda risk ger\u00e7ekte b\u00f6yle da\u011f\u0131lmaz ve bu durum g\u00fcvenlik ile \u00fcretkenlik aras\u0131nda ka\u00e7\u0131n\u0131lmaz bir gerilim yarat\u0131r.

<\/p>

Herkese agresif DLP kontrolleri uygulamak y\u00fcksek riskli kullan\u0131c\u0131lardan kaynaklanan riski azalt\u0131r; ancak y\u00fcksek risk ta\u015f\u0131mayan b\u00fcy\u00fck \u00e7o\u011funluk i\u00e7in ciddi s\u00fcrt\u00fcnme yarat\u0131r. \u00dcretkenli\u011fi korumak i\u00e7in gev\u015fek kontroller uygulamak ise risk seviyeleri ger\u00e7ekten y\u00fckseldi\u011finde kurumu korumas\u0131z b\u0131rak\u0131r. <\/p>

Uyarlanabilir Koruma bu gerilimi, DLP politika uygulamas\u0131n\u0131 dinamik hale getirerek \u00e7\u00f6zer: belirli kullan\u0131c\u0131lara uygulanan kontrolleri, mevcut i\u00e7 tehdit risk seviyelerine g\u00f6re otomatik olarak ayarlar.<\/p>

Uyarlanabilir Koruma pratikte nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h5>

IRM, davran\u0131\u015fsal sinyalleri s\u00fcrekli olarak de\u011ferlendirir ve g\u00f6zlemlenen faaliyetlerin b\u00fct\u00fcn\u00fcne dayanarak her kullan\u0131c\u0131ya d\u00fc\u015f\u00fck, orta veya y\u00fcksek bir risk seviyesi atar. Uyarlanabilir Koruma bu risk seviyelerini DLP politika yap\u0131land\u0131rmalar\u0131yla e\u015fle\u015ftirir:

<\/p>

D\u00fc\u015f\u00fck risk seviyesi:<\/strong> Kullan\u0131c\u0131n\u0131n davran\u0131\u015f\u0131 baz\u0131 anomaliler g\u00f6sterir ancak aktif bir veri g\u00fcvenli\u011fi endi\u015fesine i\u015faret eden bir \u015fey yoktur. Kullan\u0131c\u0131 hassas i\u00e7eri\u011fi harici olarak payla\u015fmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131nda bir politika ipucu g\u00f6r\u00fcnt\u00fclenir; me\u015fru i\u015fi aksatmas\u0131 olas\u0131 olmayan ancak fark\u0131ndal\u0131k yaratan nazik bir hat\u0131rlatma.

<\/p>

Orta risk seviyesi:<\/strong> Kullan\u0131c\u0131n\u0131n davran\u0131\u015f kal\u0131b\u0131 daha \u00f6nemli bir anomaliye i\u015faret eder. DLP kontrolleri s\u0131k\u0131la\u015f\u0131r; harici payla\u015f\u0131m a\u00e7\u0131k gerek\u00e7e gerektirebilir, ge\u00e7ersiz k\u0131lma se\u00e7enekli engelleme politikalar\u0131 devreye girer ve izleme yo\u011funlu\u011fu artar.

<\/p>

Y\u00fcksek risk seviyesi:<\/strong> Do\u011frulanm\u0131\u015f bir risk olay\u0131 belirlenmi\u015ftir. DLP kontrolleri en \u00fcst d\u00fczeyde s\u0131k\u0131la\u015f\u0131r; hassas i\u00e7eri\u011fin harici payla\u015f\u0131m\u0131 tamamen engellenir ve g\u00fcvenlik ekibi aktif inceleme i\u00e7in uyar\u0131l\u0131r.

<\/p>

Bir kullan\u0131c\u0131n\u0131n risk seviyesi normale d\u00f6nd\u00fc\u011f\u00fcnde, y\u00fckseltilmi\u015f kontroller otomatik olarak kald\u0131r\u0131l\u0131r. Bu, g\u00fcvenlik ekibinin m\u00fcdahalesini gerektiren manuel bir s\u00fcre\u00e7 de\u011fildir; otomatik, s\u00fcrekli ve orant\u0131l\u0131d\u0131r. <\/p>

Uyarlanabilir Koruma’y\u0131 G\u00f6lge Yapay Zek\u00e2 senaryolar\u0131na uygulamak<\/strong><\/h5>

Uyarlanabilir Koruma’n\u0131n g\u00fcc\u00fc, G\u00f6lge Yapay Zek\u00e2 senaryolar\u0131na uyguland\u0131\u011f\u0131nda \u00f6zellikle netle\u015fir.

<\/p>

Bir \u00e7al\u0131\u015fan, ayr\u0131l\u0131k \u00f6ncesi veri toplamayla uyumlu davran\u0131\u015fsal anomaliler g\u00f6stermeye ba\u015flar: nadiren ziyaret etti\u011fi depolara eri\u015fir, ola\u011fand\u0131\u015f\u0131 hacimlerde dosya indirir, i\u00e7erikleri ki\u015fisel bulut depolamaya kopyalar. IRM ona y\u00fcksek bir risk seviyesi atar. Uyarlanabilir Koruma, DLP kontrollerini otomatik olarak s\u0131k\u0131la\u015ft\u0131r\u0131r; harici veri aktar\u0131mlar\u0131n\u0131 engeller, taray\u0131c\u0131 etkinli\u011finin izlenmesini art\u0131r\u0131r ve g\u00fcvenlik ekibini uyar\u0131r.

<\/p>

Bu y\u00fcksek risk seviyesinde, \u00e7al\u0131\u015fan izlenen bir taray\u0131c\u0131 kanal\u0131 \u00fczerinden kurumsal i\u00e7eri\u011fi harici bir yapay zek\u00e2 arac\u0131na yap\u0131\u015ft\u0131rmaya \u00e7al\u0131\u015f\u0131rsa giri\u015fim engellenir. \u00c7al\u0131\u015fan, eylemin mevcut kurumsal politika kapsam\u0131nda izin verilmedi\u011fini a\u00e7\u0131klayan bir politika bildirimi al\u0131r. G\u00fcvenlik ekibine bir uyar\u0131 gider.

<\/p>

Hi\u00e7bir zaman ola\u011fand\u0131\u015f\u0131 davran\u0131\u015f kal\u0131plar\u0131 sergilememi\u015f \u00e7al\u0131\u015fan ise, risk seviyesi hi\u00e7 ek kontrol tetiklemedi\u011fi i\u00e7in ayn\u0131 eri\u015fim ve her zaman kulland\u0131\u011f\u0131 ayn\u0131 \u00fcretkenlik ara\u00e7lar\u0131yla normal \u015fekilde \u00e7al\u0131\u015fmaya devam eder.

<\/p>

Bu orant\u0131l\u0131l\u0131k yaln\u0131zca operasyonel olarak tercih edilir olman\u0131n \u00f6tesindedir; tart\u0131\u015fmas\u0131z tek s\u00fcrd\u00fcr\u00fclebilir yakla\u015f\u0131md\u0131r. K\u00fc\u00e7\u00fck bir az\u0131nl\u0131\u011f\u0131n olu\u015fturdu\u011fu riske yan\u0131t olarak t\u00fcm \u00e7al\u0131\u015fanlara azami s\u00fcrt\u00fcnme uygulayan g\u00fcvenlik programlar\u0131, muazzam kurumsal diren\u00e7 yarat\u0131r ve sonunda ge\u00e7ersiz k\u0131l\u0131n\u0131r ya da atlat\u0131l\u0131r. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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G\u00f6lge Yapay Zek\u00e2 Y\u00f6neti\u015fim \u00c7er\u00e7evesini Kurmak<\/strong><\/h4>

Teknoloji kontrolleri gereklidir ancak yeterli de\u011fildir. G\u00f6lge Yapay Zek\u00e2’y\u0131 ele almak; politika, fark\u0131ndal\u0131k, ara\u00e7lar ve s\u00fcreci birle\u015ftiren bir y\u00f6neti\u015fim \u00e7er\u00e7evesi gerektirir. <\/p>

Yetkili yapay zek\u00e2y\u0131 tan\u0131mlay\u0131n ve net bir politika olu\u015fturun<\/strong><\/h5>

Ba\u015flang\u0131\u00e7 noktas\u0131 netliktir. Bir\u00e7ok kurumun fiili yapay zek\u00e2 politikalar\u0131 vard\u0131r \u2014 \u00e7al\u0131\u015fanlar neyin kabul edilebilir olup olmad\u0131\u011f\u0131n\u0131 ba\u011flamdan \u00e7\u0131kar\u0131r \u2014 ancak a\u00e7\u0131k, iletilmi\u015f ve uygulanabilir y\u00f6nergeler yoktur. Bu belirsizlik kendi ba\u015f\u0131na bir risktir; hem hukuki ve d\u00fczenleyici risk yaratt\u0131\u011f\u0131 i\u00e7in hem de \u00e7al\u0131\u015fanlar kurumun ger\u00e7ek tutumunu bilmediklerinde yapay zek\u00e2 kullan\u0131m\u0131 hakk\u0131nda bilin\u00e7li kararlar veremedi\u011fi i\u00e7in.

<\/p>

Etkili bir yapay zek\u00e2 kullan\u0131m politikas\u0131 \u015funlar\u0131 ele al\u0131r:<\/strong> Hangi yapay zek\u00e2 ara\u00e7lar\u0131n\u0131n hangi kullan\u0131m senaryolar\u0131 i\u00e7in yetkili oldu\u011fu, hangi bilgi kategorilerinin yapay zek\u00e2 ara\u00e7lar\u0131na (yetkili olsun ya da olmas\u0131n) g\u00f6nderilebilece\u011fi ve g\u00f6nderilemeyece\u011fi, yapay zek\u00e2 ara\u00e7lar\u0131n\u0131 (yetkili olanlar da dahil) kurumsal verilerle kullan\u0131rken \u00e7al\u0131\u015fanlar\u0131n sorumluluklar\u0131n\u0131n ne oldu\u011fu ve ihlallerin nas\u0131l tespit edilip ele al\u0131naca\u011f\u0131.<\/p>

Yapay zek\u00e2 benimsenmesi \u00f6l\u00e7eklenmeden \u00f6nce s\u0131n\u0131fland\u0131r\u0131n ve etiketleyin<\/strong><\/h5>

Hassasiyet etiketleme, yapay zek\u00e2 y\u00f6neti\u015fimini anlaml\u0131 k\u0131lan temel kontrold\u00fcr. Kurumsal veriler uygun hassasiyet s\u0131n\u0131fland\u0131rmalar\u0131yla tutarl\u0131 bi\u00e7imde etiketlendi\u011finde, yapay zek\u00e2 sistemleri etiketli i\u00e7eri\u011fi uygun \u015fekilde ele alacak bi\u00e7imde yap\u0131land\u0131r\u0131labilir: etiketli verilerin yapay zek\u00e2 ara\u00e7lar\u0131na g\u00f6nderilmesi k\u0131s\u0131tlan\u0131r, hassas girdilerden t\u00fcretilen yapay zek\u00e2 \u00fcretimi i\u00e7eri\u011fin uygun s\u0131n\u0131fland\u0131rmalar\u0131 devralmas\u0131 sa\u011flan\u0131r ve DLP politikalar\u0131n\u0131n yapay zek\u00e2 ba\u011flamlar\u0131nda veri i\u015fleme kurallar\u0131n\u0131 uygulamas\u0131 m\u00fcmk\u00fcn olur.

<\/p>

Kapsaml\u0131 hassasiyet etiketlemesini uygulamadan \u00f6nce yapay zek\u00e2 benimsenmesinin \u00f6l\u00e7eklenmesine izin veren kurumlar, yapay zek\u00e2 veri g\u00fcvenli\u011fini m\u00fcmk\u00fcn k\u0131lan temel y\u00f6neti\u015fim altyap\u0131s\u0131 olmadan \u00e7al\u0131\u015f\u0131yor demektir. S\u0131ralama \u00f6nemlidir: \u00f6nce s\u0131n\u0131fland\u0131r\u0131n, sonra etkinle\u015ftirin. <\/p>

Erken a\u015fama G\u00f6lge Yapay Zek\u00e2 i\u00e7in yapt\u0131r\u0131m yerine fark\u0131ndal\u0131\u011fa \u00f6ncelik verin<\/strong><\/h5>

Kaynak kodunu ChatGPT’ye yap\u0131\u015ft\u0131ran Samsung m\u00fchendisi, bunun ciddi bir kurumsal risk ta\u015f\u0131d\u0131\u011f\u0131n\u0131 neredeyse kesinlikle anlamam\u0131\u015ft\u0131. G\u00f6lge Yapay Zek\u00e2 davran\u0131\u015flar\u0131n\u0131n \u00e7o\u011fu k\u00f6t\u00fc niyetten de\u011fil bilgisizlikten kaynaklan\u0131r ve bilgisizli\u011fe verilecek uygun ilk yan\u0131t yapt\u0131r\u0131m de\u011fil, e\u011fitimdir.

<\/p>

G\u00fcvenlik fark\u0131ndal\u0131k programlar\u0131n\u0131n yapay zek\u00e2 \u00e7a\u011f\u0131 i\u00e7in g\u00fcncellenmesi gerekir. \u00c7al\u0131\u015fanlar\u0131n, harici yapay zek\u00e2 ara\u00e7lar\u0131na g\u00f6nderdikleri verilere ne oldu\u011funu, kurumun veri y\u00f6neti\u015fimi politikalar\u0131 kapsam\u0131ndaki y\u00fck\u00fcml\u00fcl\u00fcklerinin ne oldu\u011funu ve veri g\u00fcvenli\u011fi riski yaratmadan yapay zek\u00e2n\u0131n \u00fcretkenlik faydalar\u0131ndan yararlanmalar\u0131n\u0131 sa\u011flayan hangi yetkili alternatiflerin bulundu\u011funu ger\u00e7ek\u00e7i \u00f6rneklerle somut olarak anlamas\u0131 gerekir.

<\/p>

DLP sistemlerinden gelen politika ipu\u00e7lar\u0131, bir \u00e7al\u0131\u015fan politikan\u0131n k\u0131s\u0131tlayaca\u011f\u0131 bir eylemi denedi\u011finde ger\u00e7ek zamanl\u0131 fark\u0131ndal\u0131k m\u00fcdahaleleri i\u015flevi g\u00f6rebilir; iyi tasarlanm\u0131\u015f bir ipucu eylemin neden sorunlu oldu\u011funu a\u00e7\u0131klar ve yetkili alternatifler \u00f6nerir. Bu yakla\u015f\u0131m, olas\u0131 ihlalleri disiplin olaylar\u0131na de\u011fil \u00f6\u011frenme anlar\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. <\/p>

Teknik kontrol y\u0131\u011f\u0131n\u0131n\u0131 kademeli olarak kurun<\/strong><\/h5>

G\u00f6lge Yapay Zek\u00e2 y\u00f6neti\u015fimi i\u00e7in tam teknik kontrol y\u0131\u011f\u0131n\u0131n\u0131n ayn\u0131 anda devreye al\u0131nmas\u0131 gerekmez. Pragmatik bir s\u0131ralama:

<\/p>

Birinci:<\/strong> IRM analiti\u011fini etkinle\u015ftirin. Bu, yap\u0131land\u0131r\u0131lm\u0131\u015f politika gerektirmez, son kullan\u0131c\u0131lara etkisi yoktur ve davran\u0131\u015f kal\u0131plar\u0131 ile olas\u0131 risk g\u00f6stergelerine ili\u015fkin i\u00e7g\u00f6r\u00fcler \u00fcretmeye hemen ba\u015flar.

<\/p>

\u0130kinci:<\/strong> Fikri m\u00fclkiyet, finansal kay\u0131tlar, personel verileri ve stratejik planlama belgeleri gibi en hassas veri kategorilerinde hassasiyet etiketlemesini uygulay\u0131n. Bu, sonraki yapay zek\u00e2 y\u00f6neti\u015fim kontrollerini m\u00fcmk\u00fcn k\u0131lan s\u0131n\u0131fland\u0131rma temelini olu\u015fturur.

<\/p>

\u00dc\u00e7\u00fcnc\u00fc:<\/strong> Microsoft 365 ortam\u0131ndaki yetkili yapay zek\u00e2 ara\u00e7lar\u0131nda kullan\u0131m kal\u0131plar\u0131na g\u00f6r\u00fcn\u00fcrl\u00fck kazanmak i\u00e7in DSPM for AI’\u0131 devreye al\u0131n.

<\/p>

D\u00f6rd\u00fcnc\u00fc:<\/strong> En y\u00fcksek riskli G\u00f6lge Yapay Zek\u00e2 senaryolar\u0131n\u0131 hedefleyen IRM politikalar\u0131n\u0131 yap\u0131land\u0131r\u0131n; \u00f6zellikle en zarar verici G\u00f6lge Yapay Zek\u00e2 olaylar\u0131n\u0131 karakterize eden ayr\u0131l\u0131k \u00f6ncesi sinyaller ile ola\u011fand\u0131\u015f\u0131 veri etkinli\u011fi kombinasyonunu yakalayan \u201cayr\u0131lan kullan\u0131c\u0131lar taraf\u0131ndan veri h\u0131rs\u0131zl\u0131\u011f\u0131\u201d \u015fablonunu.

<\/p>

Be\u015finci:<\/strong> IRM risk sinyallerini DLP politika uygulamas\u0131na ba\u011flamak i\u00e7in Uyarlanabilir Koruma’y\u0131 etkinle\u015ftirin; b\u00f6ylece de\u011fi\u015fen risk seviyelerine otomatik olarak uyum sa\u011flayan dinamik kontrol katman\u0131 olu\u015fur.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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D\u00fczenleyici Ufuk<\/strong><\/h4>

G\u00f6lge Yapay Zek\u00e2’n\u0131n y\u00f6neti\u015fim zorlu\u011fu yaln\u0131zca bir i\u00e7 g\u00fcvenlik sorunu de\u011fildir. H\u0131zla bir mevzuata uyum sorununa da d\u00f6n\u00fc\u015fmektedir.

<\/p>

Ba\u015fl\u0131ca \u00fclke ve b\u00f6lgelerde yapay zek\u00e2ya \u00f6zg\u00fc d\u00fczenleyici \u00e7er\u00e7eveler geli\u015ftiriliyor ve uygulan\u0131yor. Avrupa Birli\u011fi’nin Yapay Zek\u00e2 Yasas\u0131<\/strong><\/em>, y\u00fcksek riskli ba\u011flamlarda yapay zek\u00e2 sistemleri kullanan kurumlara y\u00fck\u00fcml\u00fcl\u00fckler getiriyor. AB Genel Veri Koruma T\u00fcz\u00fc\u011f\u00fc (GDPR)<\/strong><\/em>, ki\u015fisel verilerin yapay zek\u00e2 e\u011fitimi ve i\u015flenmesinde nas\u0131l kullan\u0131labilece\u011fine ili\u015fkin y\u00fck\u00fcml\u00fcl\u00fckleri zaten getirmi\u015f durumda. Finansal hizmetler, sa\u011fl\u0131k ve kritik altyap\u0131daki sekt\u00f6re \u00f6zg\u00fc d\u00fczenlemeler, yapay zek\u00e2 veri y\u00f6neti\u015fimini a\u00e7\u0131k\u00e7a ele almaya ba\u015fl\u0131yor.

<\/p>

Reg\u00fcle sekt\u00f6rlerdeki kurumlar i\u00e7in, yapay zek\u00e2 ara\u00e7lar\u0131na g\u00f6nderilen verilerin bildirilmesi gereken bir veri if\u015fas\u0131 olu\u015fturup olu\u015fturmad\u0131\u011f\u0131 ve ki\u015fisel veri i\u00e7eren yapay zek\u00e2 \u00fcretimi \u00e7\u0131kt\u0131lar\u0131n bildirim y\u00fck\u00fcml\u00fcl\u00fcklerini tetikleyip tetiklemedi\u011fi sorusu g\u00fcncel ve hen\u00fcz \u00e7\u00f6z\u00fclmemi\u015f bir sorudur. D\u00fczenleyici rehberlik geli\u015fmekte olsa da y\u00f6n a\u00e7\u0131kt\u0131r: kurumlardan yapay zek\u00e2 veri kullan\u0131m\u0131na ili\u015fkin yaln\u0131zca er ge\u00e7 ele alma niyeti de\u011fil, anlaml\u0131 bir y\u00f6neti\u015fim g\u00f6stermeleri beklenecek.

<\/p>

Kapsaml\u0131 G\u00f6lge Yapay Zek\u00e2 y\u00f6neti\u015fimini \u015fimdi uygulayan kurumlar yaln\u0131zca mevcut g\u00fcvenlik risklerini azaltm\u0131yor. Ayn\u0131 zamanda d\u00fczenleyici geli\u015fmelerin giderek daha fazla gerektirece\u011fi uyum altyap\u0131s\u0131n\u0131, yapt\u0131r\u0131m bask\u0131s\u0131 reaktif uyumu \u00e7ok daha maliyetli hale getirmeden \u00f6nce kuruyorlar. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Sonu\u00e7: Tehdit Zaten \u0130\u00e7eride<\/strong><\/h4>

\u0130\u00e7 tehdit riskini zarar verme niyeti ta\u015f\u0131yan k\u00f6t\u00fc niyetli akt\u00f6rlerin yol a\u00e7t\u0131\u011f\u0131 bir sorun olarak g\u00f6ren geleneksel \u00e7er\u00e7eve, G\u00f6lge Yapay Zek\u00e2 \u00e7a\u011f\u0131 i\u00e7in yetersizdir. Yapay zek\u00e2 ara\u00e7lar\u0131n\u0131 i\u00e7eren veri g\u00fcvenli\u011fi olaylar\u0131n\u0131n \u00e7o\u011funa k\u00f6t\u00fc niyetli i\u00e7 akt\u00f6rler neden olmaz. Bunlara; yeterli rehberlik, y\u00f6neti\u015fim veya yaratt\u0131klar\u0131 risklere dair fark\u0131ndal\u0131k olmadan, ellerindeki en etkili ara\u00e7larla en iyi i\u015flerini yapmaya \u00e7al\u0131\u015fan motive ve yetenekli \u00e7al\u0131\u015fanlar neden olur. <\/p>

Bu, baz\u0131 a\u00e7\u0131lardan k\u00f6t\u00fc niyetli i\u00e7 tehdit senaryosundan daha zor bir sorundur. K\u00f6t\u00fc niyetli akt\u00f6rler davran\u0131\u015fsal anomali analiziyle tespit edilebilir ve yapt\u0131r\u0131mla kar\u015f\u0131l\u0131k verilebilir. Riskleri anlamayan iyi niyetli \u00e7al\u0131\u015fanlar ise e\u011fitim, y\u00f6neti\u015fim altyap\u0131s\u0131 ve arad\u0131klar\u0131 me\u015fru \u00fcretkenlik faydalar\u0131n\u0131 engellemeyen orant\u0131l\u0131 kontrollerin bir bile\u015fimini gerektirir. <\/p>

G\u00f6lge Yapay Zek\u00e2 zorlu\u011funu en ba\u015far\u0131l\u0131 \u015fekilde y\u00f6netecek kurumlar, bunun bir teknoloji sorunu oldu\u011fu kadar bir insan ve s\u00fcre\u00e7 sorunu da oldu\u011funu kabul eden ve yan\u0131tlar\u0131n\u0131 buna g\u00f6re tasarlayan kurumlar olacakt\u0131r. Microsoft Purview gibi platformlardan gelen teknoloji kontrolleri, sorunun gerektirdi\u011fi g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc, davran\u0131\u015fsal istihbarat\u0131 ve dinamik uygulama yetene\u011fini sa\u011flar. Ancak bu kontroller en iyi, \u00e7al\u0131\u015fanlar\u0131n anlad\u0131\u011f\u0131 ve kabul etti\u011fi bir y\u00f6neti\u015fim \u00e7er\u00e7evesi i\u00e7inde \u00e7al\u0131\u015ft\u0131klar\u0131nda i\u015fe yarar. <\/p>

Yapay zek\u00e2 \u00e7a\u011f\u0131nda veri g\u00fcvenli\u011fi, \u00f6ncelikle \u00e7al\u0131\u015fanlar\u0131n yapay zek\u00e2 kullanmas\u0131n\u0131 engellemekle ilgili de\u011fildir. O sava\u015f zaten kaybedildi ve onu s\u00fcrd\u00fcrmek, yapay zek\u00e2 ara\u00e7lar\u0131n\u0131n ger\u00e7ekten sa\u011flad\u0131\u011f\u0131 muazzam \u00fcretkenlik faydalar\u0131ndan vazge\u00e7mek anlam\u0131na gelir. Mesele; \u00e7al\u0131\u015fanlar yapay zek\u00e2 kulland\u0131\u011f\u0131nda \u2014 yetkili ara\u00e7lar, g\u00f6lge ara\u00e7lar, g\u00f6m\u00fcl\u00fc ara\u00e7lar, t\u00fcketici ara\u00e7lar\u0131 \u2014 kurumun hangi verinin nereye akt\u0131\u011f\u0131, kimin neyi g\u00f6rebildi\u011fi ve risk sinyalleri bir \u015feylerin ters gitti\u011fine i\u015faret etti\u011finde ne olaca\u011f\u0131 \u00fczerinde anlaml\u0131 bir y\u00f6neti\u015fimi s\u00fcrd\u00fcrmesini sa\u011flamakt\u0131r. <\/p>

Tehdit zaten i\u00e7eride. Soru, g\u00fcvenlik programlar\u0131n\u0131n onu yeterince net g\u00f6rebilmesi ve onu y\u00f6netmek i\u00e7in yeterince dinamik yan\u0131t verebilmesidir. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t

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\r\n\t\t\tD Tech Cloud, g\u00fcvenilir teknoloji i\u015f orta\u011f\u0131n\u0131z!<\/span>\r\n\t\t<\/span><\/h5>\r\n\t\t<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

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