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The article presents an architectural mediation approach using the Model Context Protocol to enable controlled interactions between LLM agents and data spaces, ensuring compliance with governance and interoperability standards.
This article discusses the actual state of FDEs (Frontline Deployment Engineers) in China's AI sector, highlighting risks in project acquisition, data governance challenges, and business model issues. It stresses the importance of rationalizing industry identity and adopting a more practical approach.
The article argues that in enterprise AI, the AI model is often the easiest part, with greater challenges in data trust, governance, and workflow integration, especially in regulated industries like healthcare.
This paper proposes an additive profile for Croissant to evaluate data use conditions, enabling automated compliance checking for ML datasets with a decision procedure that records what was checked.
This article provides a comprehensive guide from concept to implementation for enterprise AI knowledge bases, emphasizing the importance of data governance, access control, testing, and validation, and includes an implementation checklist.
James Luan reflects on the evolution of vector databases from early similarity-search systems to critical infrastructure for production AI, emphasizing their expanding role in RAG and agent-based systems.
The paper introduces GROUND, a governed semantic-retrieval framework that constrains LLM-generated analytics to approved business definitions, effectively reducing hallucinations in enterprise data analysis while ensuring compliance with security and data policies.
UNESCO's Recommendation on the Ethics of Artificial Intelligence outlines core principles like transparency and fairness, with policy action areas to guide AI implementation in sectors such as data governance and health.
This paper formalizes the concept of spec-delta for data governance in lakehouse platforms and presents an empirical study comparing spec-delta-driven workflows to traditional code-based approaches.
Gregory Kurtzer, founder of Rocky Linux, announces OpenWALDO, a project aimed at opening up AI training data to foster true open-source AI development.
The article argues that AI agent safety focuses too much on instruction-following and not enough on data access governance, highlighting the Agentic Data Protocol as an early effort to put policy in infrastructure.
Enterprise AI often underestimates the work behind data operations, from labeling to governance, which is critical for scaling.
This thread explains why AI security requires infrastructure-layer controls (IAM, VPC, encryption, logging) beyond application-layer prompt filtering, using AWS services as an example.
A study graded 205 AI apps on their data governance practices, finding that over half received a D or F grade, with many apps not disclosing whether user input trains their models.
A report that Microsoft allegedly shared Dutch civil servants' emails with the U.S. House triggers renewed calls for digital sovereignty, highlighting the gap between data residency and actual legal control.
The article examines why internal enterprise AI projects often stall after the demo stage, highlighting operational challenges such as schema mapping, metric definitions, and maintaining trust, while noting that the AI model itself is the easiest part.
A discussion on the challenges consultants face when clients want to deploy LLMs despite having poor data governance, weighing the risks of fixing data first versus deploying quickly on messy data.
LSEG partners with OpenAI to scale trusted AI by grounding models in high-quality financial data, shrinking release cycles from months to two weeks, and expanding the analyst role from data processing to deeper insight.
OpenMetadata is a fast-growing open-source unified metadata platform offering data discovery, observability, and governance with 84+ connectors and no-code data quality tools.