Editorial collection
AI governance in practice.
Six evidence-led articles on the operational work behind responsible AI: authoritative sources, decision records, human oversight, transparency, production handover and proportionate review.
Governance becomes credible when people can see how a system is authorised, challenged and changed. This collection is arranged as a practical reading path. Start with evidence, then follow the decisions and controls that carry an AI use into day-to-day operation.
01 · Evidence
Policy Search Is Not Policy Evidence
Why retrieval is only the beginning—and why authority, version status and inspectable evidence determine whether an answer is safe to use.
02 · Decisions
AI Governance Needs Decision Logs
Policies state intent. Decision records preserve who approved an AI use, the evidence they relied on and the conditions for revisiting it.
03 · Oversight
Human Oversight Is a Workflow
A reviewer needs evidence, authority, timing and an escalation route. A name on a register is not enough.
04 · Transparency
Transparency Is an Operating Workflow
Labels matter, but disclosure also needs ownership, provenance, evidence and a process for keeping information current.
05 · Production
From AI Pilot to Production
The move into production needs an evidence-backed handover, accountable ownership and explicit review triggers.
06 · Review
AI-Assisted Coding Needs a Spectrum of Review
Review should rise with consequence. A low-risk draft and a production security change should not pass through the same control.
Governance is easier to trust when its evidence is visible.
PolicyOps is developing practical ways to connect policy authority, evidence, review and accountable decisions. Product material is clearly separated from this publication’s independent editorial analysis.