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DTSTAMP:20260421T223145Z
DESCRIPTION:Click for Latest Location Information: http://dgiq2024west.data
 versity.net/sessionPop.cfm?confid=160&proposalid=15249\nIn the era of growi
 ng unstructured data and advanced Large Language Models (LLMs), the risk of
  sensitive data exposure has become a paramount concern for enterprises. As
  reported in a study by Veritas Technologies, with an average of 17.5 petab
 ytes of unstructured data, of which approximately 55% is classified as dark
  data, organizations are increasingly challenged with safeguarding data in 
 AI interactions. In this talk, we will present a solution architecture that
  we have implemented that integrates AI-driven data classification, robust 
 access controls, and compliance mechanisms. We will describe how this appro
 ach enhances data security, ensures AI compliance, and streamlines sensitiv
 e data management while boosting operational efficiency and risk mitigation
 . We will also discuss why it is pivotal for organizations seeking to lever
 age AI capabilities responsibly in the LLM era to adopt this framework.\n\n
 Implementing a comprehensive data security solution is essential for modern
  enterprises coping with vast amounts of sensitive information. The success
  of our solution demonstrates that a hybrid approach combining deterministi
 c and probabilistic methods, such as pattern matching and advanced AI/ML mo
 dels, is effective in identifying and flagging sensitive data with overly p
 ermissive access controls. Our approach underlines the importance of a sens
 itivity rules database, categorized into Core, Common, and Unique Rules, to
  tailor data protection strategies to the specific needs of an organization
 . Join us in this session to learn more about this extensible framework tha
 t organizations can implement to secure their sensitive data in an increasi
 ngly complex digital environment.\n
DTSTART:20240605T114500
SUMMARY:Mastering the Dark Data Challenge: Harnessing AI for Enhanced Data 
 Governance and Quality
DTEND:20240605T122959
LOCATION: See Description
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