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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16757\nLineage is essent
 ial to modern data governance, but AI governance requires more than a map o
 f data movement. Catalogs and lineage tools help organizations understand w
 here data came from, what systems touched it, and which downstream assets d
 epend on it. But when datasets, models, prompts, APIs, and AI outputs move 
 across platforms, teams, vendors, or regulatory boundaries, lineage alone m
 ay not preserve the evidence needed to prove governance, accountability, or
  control.\n\nThis session explains the difference between lineage, provenan
 ce, chain of custody, documentation, and verifiable evidence. Attendees wil
 l learn where catalog visibility remains valuable, where it becomes insuffi
 cient for high-risk AI governance, and what evidence questions governance t
 eams should ask when AI assets move beyond their original environment.\n\nP
 articipants will learn how to:\n\n
 Distinguish lineage from provenance, chain of custody, documentation, and g
 overnance evidence\n
 Recognize where catalog-centric governance can break down for AI assets\n
 Identify why documentation may not be strong enough for audit, model review
 , or regulatory examination\n
 Understand the evidence categories needed for governed datasets, models, pr
 ompts, APIs, and outputs\n
 Extend the value of catalogs and lineage tools without positioning them as 
 the entire evidence layer\n
 Apply practical review questions to determine whether current governance re
 cords can support AI accountability\n
DTSTART:20261118T133000
SUMMARY:Lineage Is Not Evidence: What AI Governance Requires Beyond Catalog
  Visibility
DTEND:20261118T141459
LOCATION: See Description
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