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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16776\nEnterprises are d
 eploying AI that acts at machine speed—agents that read data, decide,
  and trigger actions in seconds. But the rules that govern that data (who c
 an use it, for what, and for how long) still move at human speed: committee
 s, spreadsheets, and quarterly reviews. That gap is where the real risk in 
 agentic AI now lives, and most governance programs can't see it until s
 omething breaks. This session introduces the "data lifecycle control p
 lane": a governance layer that travels with the data itself, from crea
 tion to retirement, so policy is enforced at the moment an agent touches da
 ta rather than reconstructed after the fact. Drawing on 18 years of buildin
 g enterprise data practices and current work designing cloud and AI foundat
 ions for large organizations, the talk is vendor-neutral and product-free. 
 Attendees leave with a maturity lens to assess their own programs and a pha
 sed roadmap to close the machine-speed/human-speed gap without stalling AI 
 delivery.\n\n
 Where AI velocity has outrun governance—and how to find it in your ow
 n organization\n
 The data lifecycle control plane: attaching context, lineage, and entitleme
 nt so agents inherit policy automatically\n
 A reference pattern for embedding governance into data and AI pipelines\n
 A maturity model for data lifecycle governance in the agentic era\n
 A phased roadmap to build the control plane without slowing AI delivery\n
 Why the agentic enterprise is won or lost at the data layer, not the model 
 layer\n
DTSTART:20261117T120000
SUMMARY:The Data Lifecycle Control Plane: Governing Agentic AI at Machine S
 peed
DTEND:20261117T124459
LOCATION: See Description
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