What ensures traceability in AI-driven workflows?

Rashmi Shetty

Senior Director - Enterprise Generative AI Platform · Capital One

Replayable Execution Paths

Rashmi Shetty says agentic systems have to provide replayable execution paths to support investigation and evidence packages.

The answer starts at 14:29.

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Beth Hiatt

SVP|Product Management|Data Governance and Responsible AI @LPL Financial · Lpl Financial

Link Data Lineage to Model Management

Beth Hiatt says linking data lineage to model management is needed—connecting data governance lineage to AI model governance—to get full traceability of what data is fed into models or shared with third-party AI vendors.

The answer starts at 10:13.

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David Vellante

Co-founder, co-CEO SiliconANGLE, theCUBE, theCUBE Research (formerly Wikibon) · Siliconangle & Thecube

Immutable Record

David Vellante says the essential practice is to preserve an immutable record connecting original intent, identities, actions, and resulting changes, because a log of only technical events isn't enough to reconstruct intent and decisions.

Ameya Bhatawdekar

VP, Field CTO · Braintrust

Robust Eval Discipline

Ameya Bhatawdekar says a robust eval discipline, with the right tools, automation and systems, is paramount to manage generational changes and keep AI systems working well.

Ajay Swamy

Senior Executive Director - GenAI Products, AIML Platform Management and Governance · Jpmorganchase

Explainability, Traceability, Lineage

Ajay Swamy says rooting yourself in ground rules—explainability, traceability and lineage—lets you know where data comes from and how it's modified, creating a governed, traceable, explainable process that will remove some manual elements.