Governance visual guide
From source drift to a trusted release
The human-gated workflow for detecting changed AI guidance and releasing evidence-linked updates safely.
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What this shows
Automation can detect changed provider documentation and propose updates, but it should not silently rewrite learning content. A proposal links changed sources to affected claims and assessments, runs factuality and regression checks, presents a preview to a human approver, and preserves release and rollback evidence. This is the target-state design. In Project 42 today the human approval gate, release and rollback evidence, brief generation, and proposal ingest are built and tested; scheduled drift detection is not yet running in production, so the freshness checker reports staleness and nothing consumes its output.
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Key takeaways
- Automate change detection and impact mapping, not unreviewed publication.
- Link every proposed claim and assessment change to current source evidence.
- Keep preview, approval, release manifest, monitoring, and rollback evidence together.