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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16718\nMove beyond princ
 iples to practice—learn how to validate AI trustworthiness with a pro
 ven framework that operationalizes governance principles into measurable te
 sts across security, fairness, reliability, and transparency.\n \nAs o
 rganizations rapidly deploy Generative AI and Agentic AI systems—from
  conversational assistants to autonomous agents—a critical governance
  question emerges: How do you validate these systems are trustworthy before
  exposing them to users or sensitive data? Unlike traditional software, Gen
  AI systems introduce unique risks: prompt injection attacks, hallucinated 
 outputs, jailbreaking attempts, context manipulation, bias amplification, a
 nd unpredictable emergent behaviors.\n\nThis session introduces a comprehen
 sive testing framework specifically designed for Generative AI governance t
 hat transforms abstract principles into concrete validation protocols&mdash
 ;providing the go/no-go evidence executives and risk teams require.\n\nThis
  isn't theoretical—it's a battle-tested framework now embedde
 d as a formal control gate in an enterprise Gen AI Governance program, with
  multiple production LLM-powered systems (conversational AI, document analy
 sis agents, and code, image, voice, and video generation tools) assessed an
 d certified for deployment. The framework addresses the urgent governance c
 hallenge: how to govern Gen AI systems where traditional testing falls shor
 t.\n
DTSTART:20261118T083000
SUMMARY:Comprehensive AI Testing for Generative and Agentic AI Systems
DTEND:20261118T091459
LOCATION: See Description
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