Data Governance
The management of data quality, access, privacy, and usage policies that underpin responsible AI system operation.
Full Definition
Data Governance in the context of AI encompasses the policies, processes, and technologies for managing the data that AI systems use for training, fine-tuning, retrieval (RAG), and decision-making. This includes data quality management (ensuring accuracy, completeness, and timeliness), access control (defining who and what systems can access which data), privacy compliance (GDPR, CCPA data handling requirements), lineage tracking (documenting where data comes from and how it's transformed), and retention policies (how long data is kept and when it's deleted). For autonomous AI agents, data governance is critical because the quality and provenance of data directly impacts the quality and fairness of agent decisions. Poor data governance leads to hallucinations based on stale data, bias from unrepresentative training sets, and compliance violations from improper data handling.
Related Terms
AI Governance
The framework of policies, processes, and technologies used to ensure AI systems operate ethically, transparently, and in compliance with regulations.
Compliance Framework
A structured set of regulations, standards, and guidelines that organizations must adhere to when deploying AI systems.
RAG
Retrieval-Augmented Generation — a technique that grounds AI responses in retrieved factual documents to reduce hallucinations.