Hello, Data Governance Circle! 👋 I'm the founder of ReconAI.net , where we're exploring an increasingly important question: What happens to governance when AI stops simply providing answers and starts taking actions? Traditional data governance has taught us the importance of data quality, lineage, ownership, access, and accountability. Those foundations remain essential. But agentic AI introduces another dimension. Imagine an AI agent that can access financial records, evaluate a transaction, and initiate a payment. The data might be accurate. The system might have legitimate access. The recommendation might even be correct. But does the agent actually have the authority to execute that payment? That's the distinction we're exploring at ReconAI.net. We're interested in how organizations can establish clear boundaries between what an AI system knows, what it recommends, and what it's authorized to do. And importantly, how we preserve evidence of those decisions so humans can understand, challenge, and audit them. One principle guides our thinking: Access is not authority. Intelligence is not permission. I'm here to learn from practitioners who have spent years building the foundations of data governance. I'd love to hear your perspectives: As organizations adopt autonomous AI agents, should governing their actions become an extension of data governance, or does it require a distinct governance discipline? Looking forward to learning and contributing alongside everyone here.

Hi Everyone, I'm working in the Data Governance in the Informtica tool.Is their any possibility to implement stakeholders users groups implementaion

From this article. As of late September 2026, a structural governance deficit has emerged across global enterprises. While 88% of organizations have integrated AI into at least one business function, only 8% maintain a comprehensive governance framework. Accountability remains diffuse: only 28% place direct AI risk ownership with the CEO, and a mere 17% with the board. The landscape is further complicated by shifting regulatory goalposts, notably the recent Regulation (EU) 2026/1744 deferring the EU AI Act's high-risk obligations to December 2027, while keeping Article 50 transparency rules active. Key Takeaways: - The Maturity Mirage: An alarming 74% of organizations believe they could pass an AI compliance audit today, yet only 27% describe their governance programs as fully mature, exposing a massive blind spot in risk assessment. - Undefined Risk Ownership: Decentralized AI adoption without clear executive accountability creates a vacuum where systemic harms—such as discriminatory decision-making and privacy violations—operate undetected until a breach occurs. - Patchwork Compliance: Operating across jurisdictions forces enterprises to navigate fragmented standards—such as the US state-by-state legislative approach versus the EU's centralized model—making unified, global data governance architectures highly complex to scale. Let's Discuss: -  The Audit Disconnect: If 74% of enterprises believe they are audit-ready but only 8% have comprehensive frameworks, how are you validating the actual maturity of a client's data fabric before signing off on their AI governance roadmap? - The Executive Vacuum: When advising boards or executing enterprise transformation, what deterministic mechanisms are you putting in place to enforce CEO-level accountability for shadow AI, rather than letting risk default to fragmented IT teams?