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DTSTAMP:20250420T113545Z
UID:D8FB7406-FFA9-44FF-B9DB-5901095C6F88
DTSTART;TZID=America/New_York:20250416T120000
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DESCRIPTION:Large Language Models (LLMs) are transforming business operatio
 ns through AI-driven insights and efficiency. However\, leveraging pretrai
 ned LLMs like GPT and Claude raises critical data privacy concerns\, parti
 cularly when handling Personally Identifiable Information (PII) or Protect
 ed Health Information (PHI). This webinar panel will explore the intersect
 ion of AI and data privacy\, providing actionable best practices for busin
 esses to maximize LLM efficiency while ensuring robust data protection. We
  will address the challenges of using LLM API tools with sensitive corpora
 te data and offer strategies to mitigate risks.\n\n- Participants will be 
 able to:\n\n- Understand the data privacy challenges associated with using
  LLM APIs.\n- Identify potential risks of exposing PII and PHI on LLM plat
 forms.\n- Learn best practices for safeguarding sensitive data in LLM appl
 ications.\n- Explore strategies for balancing AI efficiency with data prot
 ection.\n- Understand how to properly use LLMs in a corporate setting.\n\n
 Speaker(s): Mohamed Naiel\, \n\nAgenda: \nIntroduction: The Impact of LLMs
  - Case scenarios\n\nData Privacy Challenges in LLM Applications\n\nIdenti
 fying and Mitigating Risks: PII and PHI\n\nBest Practices for Secure LLM I
 mplementation\n\nQuestion and Answers\n\nVirtual: https://events.vtools.ie
 ee.org/m/476833
LOCATION:Virtual: https://events.vtools.ieee.org/m/476833
ORGANIZER:nihad@ieee.org
SEQUENCE:36
SUMMARY:Discussion at the intersection of AI and data privacy and recommend
  best practices to achieve higher efficiency while safeguarding sensitive 
 data
URL;VALUE=URI:https://events.vtools.ieee.org/m/476833
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot;&gt;Large Language Models (L
 LMs) are transforming business operations through AI-driven insights and e
 fficiency. However\, leveraging pretrained LLMs like GPT and Claude raises
  critical data privacy concerns\, particularly when handling Personally Id
 entifiable Information (PII) or Protected Health Information (PHI). This w
 ebinar panel will explore the intersection of AI and data privacy\, provid
 ing actionable best practices for businesses to maximize LLM efficiency wh
 ile ensuring robust data protection. We will address the challenges of usi
 ng LLM API tools with sensitive corporate data and offer strategies to mit
 igate risks.&lt;/p&gt;\n&lt;ul style=&quot;margin-top: 0in\;&quot; type=&quot;disc&quot;&gt;\n&lt;li class=&quot;M
 soNormal&quot; style=&quot;mso-list: l0 level1 lfo1\; tab-stops: list .5in\;&quot;&gt;&lt;stron
 g&gt;Participants will be able to:&lt;br&gt;&lt;br&gt;&lt;/strong&gt;\n&lt;ul style=&quot;margin-top: 0
 in\;&quot; type=&quot;circle&quot;&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l0 level2 lfo
 1\; tab-stops: list 1.0in\;&quot;&gt;Understand the data privacy challenges associ
 ated with using LLM APIs.&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l0 
 level2 lfo1\; tab-stops: list 1.0in\;&quot;&gt;Identify potential risks of exposin
 g PII and PHI on LLM platforms.&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-lis
 t: l0 level2 lfo1\; tab-stops: list 1.0in\;&quot;&gt;Learn best practices for safe
 guarding sensitive data in LLM applications.&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; s
 tyle=&quot;mso-list: l0 level2 lfo1\; tab-stops: list 1.0in\;&quot;&gt;Explore strategi
 es for balancing AI efficiency with data protection.&lt;/li&gt;\n&lt;li class=&quot;MsoN
 ormal&quot; style=&quot;mso-list: l0 level2 lfo1\; tab-stops: list 1.0in\;&quot;&gt;Understa
 nd how to properly use LLMs in a corporate setting.&lt;/li&gt;\n&lt;/ul&gt;\n&lt;/li&gt;\n&lt;/
 ul&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p&gt;Introduction: The Impact of LLMs - Case sc
 enarios&lt;/p&gt;\n&lt;p&gt;Data Privacy Challenges in LLM Applications&lt;/p&gt;\n&lt;p&gt;Identi
 fying and Mitigating Risks: PII and PHI&lt;/p&gt;\n&lt;p&gt;Best Practices for Secure 
 LLM Implementation&lt;/p&gt;\n&lt;p&gt;Question and Answers&lt;/p&gt;
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