As organizations rapidly adopt AI and generative AI solutions, the importance of trusted, well-governed, and high-quality data has become more critical than ever. Poor data governance leads to unreliable AI models, compliance risks, and lack of transparency in decision-making.

Microsoft Azure provides a strong foundation for building modern data governance frameworks through services such as Microsoft Purview, Azure Data Lake, Synapse, and Microsoft Fabric, enabling organizations to manage metadata, lineage, and data policies at scale.

In this session, we will explore how to design and implement a responsible data platform on Azure that is AI-ready and governance-first.

Key areas covered include:

Evolution of data governance in the AI and GenAI era
Building a metadata-driven architecture using Microsoft Purview
End-to-end data lineage tracking across Azure data services
Implementing data classification, cataloging, and policy enforcement
Governance integration with modern data platforms like Microsoft Fabric and Azure Synapse
Designing responsible AI-ready data foundations (privacy, compliance, and trust)
Real-world architecture patterns for enterprise-scale governance

We will also discuss common challenges such as fragmented metadata systems, governance gaps in distributed data stacks, and strategies to unify governance across batch, streaming, and analytics workloads.

Attendees will leave with a practical understanding of how to build secure, governed, and AI-ready data platforms on Azure, enabling trustworthy analytics and responsible AI adoption.

🎯 Key Takeaways
Understand modern data governance in the AI/GenAI era
Learn Azure-native governance architecture using Microsoft Purview
Design metadata-driven and lineage-aware data platforms
Enable compliance, privacy, and responsible AI practices
Integrate governance across Azure + Fabric + Synapse ecosystems