AI That Complies White Paper

AI is moving fast. Regulations are not. And when speed outpaces structure, risk compounds. 

The promise of AI is real, but so is the risk. In regulated sectors such as life sciences, financial services, and energy, AI systems must do more than generate answers. They must be able to show
their work. Compliance requires more than performance. It demands transparency, traceability,
and control.

AI initiatives rarely fail due to model performance. They fail because the content beneath them is
fragmented, unlabeled, and ungoverned. Without structured metadata, consistent taxonomy, and
auditable content models, Retrieval-Augmented Generation (RAG) and other AI approaches introduce unacceptable risk. The result is untrustworthy outputs, broken audit trails, and systems that
cannot be validated.

This white paper is a practical guide for IT and data leaders in regulated industries. Whether
you are deploying large language models, building RAG systems, or laying the groundwork for
AI-based knowledge access, the goal is the same: build a foundation that supports compliance,
not just capability.

Topics include: 

• Why content structure and traceability determine AI risk
• The role of metadata governance, taxonomy, and content modeling in enabling safe AI
• Design patterns for building compliant RAG systems
• A practical framework to benchmark your AI readiness using the Knowledge Quotient

Download the white paper to ensure your enterprise can deploy AI with the transparency, traceability, and compliance that regulated industries demand.