Enterprise Digital Twin implementation: build around one real decision
An implementation guide for decision context: source ownership, temporal records, retrieval, permissions, MCP boundaries, failure tests and a staged rollout around one workflow.
From the archive
Trust Layer & Version Control
Version Drift is a hidden compliance risk associated with AI. When your AI retrieves outdated policies that appear “correct,” you are not only mistaken but also provably wrong. Air Canada experienced this costly issue firsthand.
This section encompasses Trust Layer architecture, which includes temporal knowledge graphs that track policy versions over time, ContentOps practices for managing knowledge lifecycles, dual-index governance systems, and audit lineage frameworks.
It’s essential to understand that relying solely on Retrieval-Augmented Generation (RAG) isn’t sufficient. We must architect for policy versioning and explore practical implementation strategies to prevent Version Drift in regulated environments. This is particularly critical for organizations where referencing an incorrect policy version can lead to regulatory penalties, financial liabilities, or risks to patient safety.
Articles
Version drift: when a well-sourced AI answer uses the wrong policy
Keep policy versions, effective dates, permissions and approvals aligned from retrieval to action. A worked example and test plan for enterprise AI version drift.
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