AI governance

Policies, boundaries, proof obligations, change control, and machine-first publication.

Use this topic when interpretive doctrine must become organizational governance: measurement, visibility audits, baselines, and publication discipline.

Start here

This hub reduces collisions between close notes and clarifies where to go next when the problem changes layer.

1. Published baseline (phase 0): what observation shows, and what it does not prove 2026-02-26
2. Observation vs attestation: why Q-Ledger is intentionally weak 2026-02-26
3. Making governance measurable: Q-Metrics 2026-02-26
4. Runbook & ops: from logs to snapshots, without leakage or drift 2026-02-26
5. Measuring invisibilization: how to audit presence in AI answers without fooling yourself 2026-02-26

Adjacent topics

• Interpretive risk — Systemic risks: false certainty, plausible errors, economic and reputational damage.
• Exogenous governance — Arbitration across sources, jurisdictions, standards, and external authorities. Includes public doctrine references for External Authority Control (EAC).
• Agentic era — Agents, delegation, non-answers, safety, and proxy governance.

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