Queries · q317 · Chief Information Security Officer · week 2, night 3
“Is the missing 'who changed this' actor specific to order cancellations, or does order_status_history never record an actor for any status transition?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
The manager, at the start of week 2
Subject: Re: Account security snapshot — good start, three gaps worry me more now
Noah,
This is a strong first week — thank you. The sign-in and access-governance findings give me confidence I can defend in a questionnaire. Three things genuinely surprised me and I want to understand them better before Friday's memo, plus one follow-up.
If we can only chase a couple of these deeply this week, I'd prioritize the deletion-audit scope (#1) and the churn-deactivation scope (#3) — those are the two a due-diligence team is most likely to ask us to prove, one way or the other.
Thanks, Henrik
The analyst · week 2, night 3, request 1 of 3
Big reversal on #1 tonight's results refine, not confirm, last week's alarm: order_status_history shows 100% coverage of cancellations across every one of the 50 tenants printed — Tidewick DOES have a timestamped record of every cancellation, just not in generic audit_log. But matching_rows_with_actor is 0 for every tenant — the WHO is missing even though the WHEN is solid. That's a much narrower, more precise finding than "we can't reconstruct order history." Invoice voids turn out to not exist at all (0 ever), so that's a non-issue; but 85 "uncollectible" write-offs across 11 tenants have genuinely 0% audit trail and no purpose-built history table standing in — that's the real, confirmed financial-document gap. And the churn-session risk mostly evaporates: all never-revoked sessions on all 9 churned tenants are already naturally expired (0 still valid) — so the real residual churn risk is just account status (100% of users, all 9 tenants) plus the 2 tenants with a live API key/webhook found last week.
With #1 and #3 now reasonably well-characterized, tonight I'm clearing Henrik's remaining two questions: whether the "no actor" gap in order_status_history is specific to cancellations or systemic across all transitions (quick, sharpens the #1 story), what actually stands in for shopper MFA (oauth-linked login vs raw password), and who specifically holds the oldest active API keys so we can judge concentration/sensitivity for #4.
The engineer · 1 dry run on the empty schema, then the submission
select to_status, count(*) as total_transitions, count(*) filter (where changed_by_user_id is not null) as transitions_with_actor, round(100.0 * count(*) filter (where changed_by_user_id is not null) / nullif(count(*), 0), 1) as share_with_actor_pct, count(distinct tenant_id) as distinct_tenants from order_status_history where created_at >= date '2023-01-01' and created_at < date '2026-01-01' group by to_status order by total_transitions desc, to_status
“Query compiles cleanly. Submitting.”
“Submitted. The report groups order_status_history by to_status across the full 2023-01-01–2025-12-31 window, showing total transitions, transitions with a non-null changed_by_user_id, the actor-recorded share (%), and distinct tenant count — which will directly show whether the actor gap is isolated to cancelled or systemic across all 7 status values.”
As Noah Fischer saw it, from the small dataset the agents worked on
to_status | total_transitions | transitions_with_actor | share_with_actor_pct | distinct_tenants ----------+-------------------+------------------------+----------------------+----------------- pending | 51039 | 0 | 0.0 | 118 paid | 49906 | 0 | 0.0 | 118 fulfilled | 49257 | 0 | 0.0 | 118 cancelled | 1712 | 0 | 0.0 | 90 refunded | 1586 | 0 | 0.0 | 74 (5 rows)
The memo and the queries that followed; links found by title, so inferred
It builds on:
Changed only where the benchmark needs the same result on every system
select to_status, count(*) as total_transitions, count(*) filter (where changed_by_user_id is not null) as transitions_with_actor, round(100.0 * count(*) filter (where changed_by_user_id is not null) / nullif(count(*), 0), 1) as share_with_actor_pct, 100.0 * count(*) filter (where changed_by_user_id is not null) / nullif(count(*), 0) as share_with_actor_pct, count(distinct tenant_id) as distinct_tenantsfrom order_status_historywhere created_at >= date '2023-01-01' and created_at < date '2026-01-01'group by to_statusorder by total_transitions desc, to_statusorder by total_transitions desc NULLS FIRST, to_status NULLS LAST
select to_status, count(*) as total_transitions, count(*) filter (where changed_by_user_id is not null) as transitions_with_actor, 100.0 * count(*) filter (where changed_by_user_id is not null) / nullif(count(*), 0) as share_with_actor_pct, count(distinct tenant_id) as distinct_tenants from order_status_history where created_at >= date '2023-01-01' and created_at < date '2026-01-01' group by to_status order by total_transitions desc NULLS FIRST, to_status NULLS LAST
The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted
Report request from Noah Fischer (analyst for the Chief Information Security Officer): Title: Actor attribution in order_status_history, across all transition types Question: Is the missing 'who changed this' actor specific to order cancellations, or does order_status_history never record an actor for any status transition? Grain: one row per to_status value in order_status_history Columns: - to_status - total transitions (all-time, 2023-01-01 to 2025-12-31) - transitions with a non-null changed_by_user_id - share with an actor recorded (%) - distinct tenants represented Details: Source: order_status_history grouped by to_status (pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded). For each, count total rows, count rows where changed_by_user_id is not null, and the percentage. This tells us whether the actor gap found for cancellations is a systemic property of the table (same for every transition type) or specific to cancellations. Order by total transitions descending, to_status as tie-break (expect 7 rows). Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runselect to_status, count(*) as total_transitions, count(*) filter (where changed_by_user_id is not null) as transitions_with_actor, round(100.0 * count(*) filter (where changed_by_user_id is not null) / nullif(count(*), 0), 1) as share_with_actor_pct, count(distinct tenant_id) as distinct_tenants from order_status_history where created_at >= date '2023-01-01' and created_at < date '2026-01-01' group by to_status order by total_transitions desc, to_status
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: to_status, total_transitions, transitions_with_actor, share_with_actor_pct, distinct_tenants
submitselect to_status, count(*) as total_transitions, count(*) filter (where changed_by_user_id is not null) as transitions_with_actor, round(100.0 * count(*) filter (where changed_by_user_id is not null) / nullif(count(*), 0), 1) as share_with_actor_pct, count(distinct tenant_id) as distinct_tenants from order_status_history where created_at >= date '2023-01-01' and created_at < date '2026-01-01' group by to_status order by total_transitions desc, to_status
Submitted to tonight's batch. Your work on this request is done.