Why Responsible AI is a Hard Data Engineering Problem (Not a Legal Checklist)
About This Session
While enterprise leaders are eager to unlock the velocity of generative AI and autonomous agents, deployment frequently stalls at the governance layer due to concerns over data hallucinations, compliance risk, and unverified automated actions. The industry has spent significant cycles treating "Responsible AI" as an abstract ethical policy. In a production B2B environment, it is fundamentally an infrastructure architecture challenge.
This session bypasses the high-level policy hype to deliver a battle-tested, vendor-neutral blueprint for building deterministic validation and risk mitigation frameworks directly into the enterprise data layer.
This session bypasses the high-level policy hype to deliver a battle-tested, vendor-neutral blueprint for building deterministic validation and risk mitigation frameworks directly into the enterprise data layer.
Speaker
Shruti Anand
Product Lead Dev AI - Google
Shruti Anand is a veteran platform product leader and investor with over 11 years of experience specializing in highly scalable enterprise architectures, data engineering frameworks, and the deployment of production-grade AI. Her career tracks through managing major enterprise data portfolios at Snowflake—where she scaled core platform capabilities to multi-million dollar ARR—to leading advanced AI-assisted development tools natively at Google. Additionally, she evaluates deep-tech and enterprise software deal flows as a part-time VC at Mighty Capital. Shruti focuses on bridging the gap between heavy enterprise infrastructure data strategy and real-world, responsible AI automation.
