Core Framework

Page	Last updated
Core Framework The six foundational components of AI data readiness, each covering the full framework, AI-specific failure modes, tooling landscape, and a practical readiness checklist.	Updated 2026-06-12
Data Quality Why data quality is the AI bottleneck — the six quality dimensions, four AI-specific failure modes, a five-step framework, tooling, and a readiness checklist.	Updated 2026-06-12
Data Governance Why governance is different for AI — ownership, policy infrastructure, bias monitoring, explainability, the NIST / EU AI Act / ISO 42001 landscape, agentic governance, and a readiness checklist.	Updated 2026-06-12
Access & Integration The bridge between data that exists and data AI can use — the silo problem, four integration patterns, lake/warehouse/lakehouse/fabric/mesh architectures, cataloging, and agentic access requirements.	Updated 2026-06-12
Lineage & Metadata The evidence layer that makes data quality, governance, and access provable — four lineage types, column-level lineage, active metadata, the business glossary, model lineage, and the AI context layer.	Updated 2026-06-12
Infrastructure Readiness The most-skipped step in enterprise AI — the MLOps/LLMOps/AgentOps stack, the seven infrastructure layers, a maturity ladder, tooling landscape, and a readiness checklist.	Updated 2026-06-12
Security & Compliance Security and compliance as a first-order AI problem — how AI changes the threat model, six AI security threat categories, the privacy compliance landscape, access control, audit trails, and privacy-preserving techniques.	Updated 2026-06-12