Panelists
Punavi Khatkool
Data Governance Tool
Platform Architect, Arch Group
Subhashish Prasad
Director, Data Governance,
Kestra Holdings
Eleni Galanopoulos
Senior Data Governance
Manager, Kestra Holdings
Moderator
Venkatesh Perumal
Venky Perumal, CTO,
DQLabs
Reimagining the data governance framework for the age of AI
Data governance was built around a simple assumption: humans write the rules, and systems follow them. AI breaks that assumption. Models ingest data before it is classified. Pipelines move faster than policy reviews. Agentic workflows make decisions that no governance document anticipated. The result is a growing gap between what governance teams have documented and what is actually happening in production data environments.
This session brings together senior governance leaders to examine what that gap looks like when you are accountable for it, and what it takes to close it.
The conversation will cover how governance functions are rethinking the relationship between documented policy, data quality, and autonomous AI, and what enforcement looks like when the system is moving faster than any human team can review.
Hosted by DQLabs, recognized as a Gartner® Magic Quadrant™ Visionary for Augmented Data Quality.
What We’ll Cover
- Why the old policy-first governance model breaks down in an autonomous AI environment, and what can replace it.
- How data quality and observability became governance functions, not just engineering ones, and what that shift means for your team's remit.
- How regulated industries are redefining accountability when pipelines move faster than any audit cycle can follow.
- What modern enforcement looks like when static rules can no longer keep pace with production data volume and velocity.
- How leading data organizations are closing the gap between documented policy and ground truth in their environments.
Who Should Attend
Register Now
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Chief Data Officers and Chief Data & Analytics Officers
Senior leaders accountable for data trust, AI readiness, and the governance posture of the enterprise.
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Heads of Data Governance
Leaders defining policy, standards, and the operating model for governance in an AI-driven environment.
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Risk, Compliance, and Audit Leaders in Regulated Industries
Practitioners who carry the consequences when pipelines and models move faster than review cycles.
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Data Quality and Stewardship Leads
Owners of data health and domain-level trust, increasingly pulled into governance conversations.
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Data Architects and Platform Leaders
Teams building the systems that governance now has to operate inside, not alongside.