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Data Governance Circle

351 members • Free

3 contributions to Data Governance Circle
Practical Lessons on AI Governance in Production Systems
One thing I’m seeing repeatedly with AI governance: Most governance frameworks fail because they live outside where decisions actually happen. Top learnings from recent work: - AI risk is rarely a model issue — it’s a context + data + ownership issue - Policies defined upfront don’t survive runtime without enforcement hooks - “Human in the loop” breaks down without clear decision rights and escalation paths - Agents amplify governance gaps faster than dashboards ever did Key challenge ahead: Governance must move from review-time controls to runtime guardrails — embedded in data access, memory, orchestration, and action execution. Curious how others here are handling governance inside live AI workflows, not just around them.
Data Catalog
Hello everyone! I'm starting to research Open Metadata to implement as a data catalog. Does anyone use this tool for data cataloging? Could you share your experiences, both good and bad? 👀 Thanks!!!
0 likes • 12d
@Marcos Alhanati That’s great timing — I’m also researching OpenMetadata in parallel. I’d love to learn from your hands-on tests and the challenges you’re running into. Happy to exchange notes as you go.
Building data & AI governance where decisions actually happen
Hi everyone 👋 Great to be here. I’m Rakesh. I’ve spent the last ~22 years building and scaling enterprise data platforms, security systems, and more recently AI and agentic systems—both as a startup CTO and now in large-scale environments. My current focus is where data governance, AI governance, and real production systems collide: - Making governance work inside data and AI workflows, not as documentation - Ownership, decision accountability, and trust in AI-driven outcomes - Why strong data foundations matter more than models for AI ROI I joined this community to: - Learn how others are operationalizing governance (what’s actually working vs. not) - Exchange practical patterns around data ownership, quality, lineage, and AI readiness - Share real-world lessons from scaling governance beyond pilots Looking forward to learning from this group and contributing where I can. Thanks for having me here.
0 likes • 12d
Absolutely agree — this is where it gets real. At scale, these domains don’t just overlap, they collide because they evolved for different risks, audiences, and enforcement models. Data Governance optimizes trust and usability, Information Governance focuses on lifecycle and defensibility, Privacy/Data Protection centers on rights and harm, IT Governance on control and resilience, and Corporate Governance on accountability at the top. The mistake many orgs make is trying to “merge” them structurally. What works better in practice is clear boundary-setting with shared primitives: - common definitions (data, record, decision, risk) - aligned ownership and escalation paths - a single decision forum for conflicts (not parallel committees) - Convergence isn’t about one framework to rule them all. It’s about making trade-offs explicit when priorities clash — speed vs safety, insight vs rights, innovation vs accountability. Curious from your experience: where do you see the most friction today — ownership, language, or decision rights?
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Rakesh Khanduja
1
1point to level up
@rakesh-khanduja-7152
Principal Engineer at Microsoft. Writing about enterprise AI, data foundations, governance, and security systems.

Active 5d ago
Joined Dec 30, 2025