Enterprises investing heavily in AI often discover that the constraint is not model capability but data quality, lineage, and access control. A model trained or grounded on inconsistent, duplicated, or improperly classified data produces outputs that look confident and are wrong in ways that are hard to detect until they cause real business harm.
Governance built for AI, not just reporting
Traditional data governance focused on reporting accuracy and regulatory reporting. AI-era governance must additionally address which data an AI system is permitted to access, how sensitive fields are masked or excluded from retrieval, and how lineage is tracked so that an incorrect output can be traced back to its source data.
Data catalogs and classification should be treated as prerequisites for any retrieval-augmented or agentic AI deployment, not optional documentation. Access controls must extend to embeddings and vector stores, which are frequently overlooked despite containing derivatives of sensitive source data.
JIG helps enterprises build the data governance foundation that makes AI outputs trustworthy enough to act on, not just impressive in a demo.