Queries · q312 · Chief Information Security Officer · week 2, night 1

Audit traceability of order cancellations and invoice voids

“For the two 'deletion-like' events on our financial documents that don't use soft-delete — a cancelled order, a voided invoice — is there any audit_log record at all tying back to that specific row?”

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

1. Henrik Aalto writes to Noah Fischer

   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.

   1. The audit trail gap is bigger than I initially feared. If we can't show who deleted something and when for one of our most common objects, I have to assume the same is true for the things a security reviewer or a merchant's own auditor would actually ask about — customer records, orders, discounts, financial documents. Can you check whether this is a product-wide blind spot or specific to a few areas? I need to know how wide the hole is before I can tell leadership whether this is "fix one thing" or "fix how deletion logging works everywhere."
   2. Shopper MFA at zero is either a non-issue or a real liability — I need to know which. Before this goes anywhere near a customer-facing security questionnaire, I want to understand what actually protects a shopper's account today if MFA genuinely isn't offered to them — is there anything else standing in for it (unusual-device checks, step-up verification at checkout, that sort of thing)? I don't want to either overstate a gap that doesn't matter or understate one that does.
   3. The churn-deactivation problem — how far does it go? You found it through privileged roles, but I suspect it's broader: if accounts aren't deprovisioned when a tenant churns, that likely applies to their sessions, their API access, everything tied to that tenant, not just role holders. I'd like a sense of the overall scale of "access that should have been turned off but wasn't" so I can size the fix properly.
   4. On the old API keys — are any of them doing something sensitive? Age alone isn't the risk; age combined with broad access is. Before I recommend a rotation mandate, I'd like to know whether those oldest keys are concentrated among a few tenants or integrations, and whether any of them look like they carry elevated access rather than routine, narrow use.

   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

   Show the whole note

2. Noah Fischer plans the night

   The analyst · week 2, night 1, request 2 of 3

   Henrik asked us to prioritize #1 (how wide is the deletion-audit gap) and #3 (how far does the churn-deactivation problem go) this week, so all 3 slots go there tonight. For #1, last week's 6-table sample (products at 0% coverage, users at 69%, three others with zero soft-deletes ever recorded) isn't enough to tell him whether this is "fix one thing" or "fix deletion logging everywhere" — so tonight I'm running a full census across every table that actually supports soft-deletion, not a hand-picked sample. I'm also addressing a gap in how I framed this last week: orders, invoices and other financial documents don't use deleted_at at all — they're "deleted" via a status change (cancelled/void), so testing audit coverage for the specific things Henrik named (orders, financial documents) needs a different query than soft-delete matching, which is report 2. For #3, last week I only checked role holders; tonight's report sizes the exposure across every access surface on churned tenants (user accounts, sessions, API keys, webhooks) so Henrik can judge whether this is a one-line fix (deactivate Owner/Admin) or a broader deprovisioning gap. I'm deliberately not touching #2 (shopper MFA compensating controls) or #4 (old-key sensitivity) tonight per his stated priority, and will pick those up once #1 and #3 are sized.

   Read the whole plan
   Audit traceability of order cancellations and invoice voids

   Question

   For the two 'deletion-like' events on our financial documents that don't use soft-delete — a cancelled order, a voided invoice — is there any audit_log record at all tying back to that specific row?

   One row per

   entity type: 'order cancellations' and 'invoice voids'

   Columns

   • entity type
   • total events (all-time, 2023-01-01 to 2025-12-31)
   • events with at least one matching audit_log row (any action, entity_table + entity_id match)
   • coverage rate (%)
   • distinct tenants represented

3. Viktor writes the SQL

   The engineer · 1 dry run on the empty schema, then the submission

   Dry run 1 · STATUS: COMPILES (dry run on the empty schema, no data read)

   SELECT
     'order cancellations' AS entity_type,
     COUNT(*) AS total_events,
     COUNT(*) FILTER (
       WHERE EXISTS (
         SELECT 1 FROM audit_log al
         WHERE al.entity_table = 'orders'
           AND al.entity_id = o.id
           AND al.tenant_id = o.tenant_id
       )
     ) AS events_with_match,
     ROUND(
       100.0 * COUNT(*) FILTER (
         WHERE EXISTS (
           SELECT 1 FROM audit_log al
           WHERE al.entity_table = 'orders'
             AND al.entity_id = o.id
             AND al.tenant_id = o.tenant_id
         )
       ) / NULLIF(COUNT(*), 0), 1
     ) AS coverage_rate_pct,
     COUNT(DISTINCT o.tenant_id) AS distinct_tenants
   FROM orders o
   WHERE o.status = 'cancelled'
     AND o.cancelled_at >= DATE '2023-01-01'
     AND o.cancelled_at < DATE '2026-01-01'
   
   UNION ALL
   
   SELECT
     'invoice voids' AS entity_type,
     COUNT(*) AS total_events,
     COUNT(*) FILTER (
       WHERE EXISTS (
         SELECT 1 FROM audit_log al
         WHERE al.entity_table = 'invoices'
           AND al.entity_id = i.id
           AND al.tenant_id = i.tenant_id
       )
     ) AS events_with_match,
     ROUND(
       100.0 * COUNT(*) FILTER (
         WHERE EXISTS (
           SELECT 1 FROM audit_log al
           WHERE al.entity_table = 'invoices'
             AND al.entity_id = i.id
             AND al.tenant_id = i.tenant_id
         )
       ) / NULLIF(COUNT(*), 0), 1
     ) AS coverage_rate_pct,
     COUNT(DISTINCT i.tenant_id) AS distinct_tenants
   FROM invoices i
   WHERE i.status = 'void'
     AND i.voided_at >= DATE '2023-01-01'
     AND i.voided_at < DATE '2026-01-01'
   
   ORDER BY entity_type

   “Query compiles cleanly. Submitting.”

   Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

   “Submitted. Summary for the record: - Grain: one row per entity type (order cancellations, invoice voids). - Logic: for each cancelled order / voided invoice, an EXISTS check against audit_log on (entity_table, entity_id, tenant_id), any action — matching the "looser than soft-delete" test requested. - Date scoping: used orders.cancelled_at and invoices.voided_at bounded to 2023-01-01–2025-12-31, since those are the natural event timestamps for each deletion-like state. - Caveat flagged in the note: a match only proves some audit trail exists for that row — not that the cancellation/void action itself was captured.”

4. The report comes back overnight

   As Noah Fischer saw it, from the small dataset the agents worked on

   entity_type         | total_events | events_with_match | coverage_rate_pct | distinct_tenants
   --------------------+--------------+-------------------+-------------------+-----------------
   invoice voids       |            0 |                 0 |              NULL |                0
   order cancellations |         1712 |                 3 |               0.2 |               90
   (2 rows)

5. What came of it

   The memo and the queries that followed; links found by title, so inferred

   Later queries that build on this report:

   • q314Order status history coverage for cancelled orders
   • q315Invoice void and uncollectible traceability, with timestamp completeness check

   It builds on:

   • q109Audit-trail coverage of actual soft-deletions, by entity table

6. It becomes benchmark query q312

   Changed only where the benchmark needs the same result on every system

   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results
   The benchmark text, against the submitted SQL

   SELECT  'order cancellations' AS entity_type,  COUNT(*) AS total_events,  COUNT(*) FILTER (    WHERE EXISTS (      SELECT 1 FROM audit_log al      WHERE al.entity_table = 'orders'        AND al.entity_id = o.id        AND al.tenant_id = o.tenant_id    )  ) AS events_with_match,  ROUND(    100.0 * COUNT(*) FILTER (      WHERE EXISTS (        SELECT 1 FROM audit_log al        WHERE al.entity_table = 'orders'          AND al.entity_id = o.id          AND al.tenant_id = o.tenant_id      )    ) / NULLIF(COUNT(*), 0), 1  ) AS coverage_rate_pct,  100.0 * COUNT(*) FILTER (    WHERE EXISTS (      SELECT 1 FROM audit_log al      WHERE al.entity_table = 'orders'        AND al.entity_id = o.id        AND al.tenant_id = o.tenant_id    )  ) / NULLIF(COUNT(*), 0) AS coverage_rate_pct,  COUNT(DISTINCT o.tenant_id) AS distinct_tenantsFROM orders oWHERE o.status = 'cancelled'  AND o.cancelled_at >= DATE '2023-01-01'  AND o.cancelled_at < DATE '2026-01-01'UNION ALLSELECT  'invoice voids' AS entity_type,  COUNT(*) AS total_events,  COUNT(*) FILTER (    WHERE EXISTS (      SELECT 1 FROM audit_log al      WHERE al.entity_table = 'invoices'        AND al.entity_id = i.id        AND al.tenant_id = i.tenant_id    )  ) AS events_with_match,  ROUND(    100.0 * COUNT(*) FILTER (      WHERE EXISTS (        SELECT 1 FROM audit_log al        WHERE al.entity_table = 'invoices'          AND al.entity_id = i.id          AND al.tenant_id = i.tenant_id      )    ) / NULLIF(COUNT(*), 0), 1  ) AS coverage_rate_pct,  100.0 * COUNT(*) FILTER (    WHERE EXISTS (      SELECT 1 FROM audit_log al      WHERE al.entity_table = 'invoices'        AND al.entity_id = i.id        AND al.tenant_id = i.tenant_id    )  ) / NULLIF(COUNT(*), 0) AS coverage_rate_pct,  COUNT(DISTINCT i.tenant_id) AS distinct_tenantsFROM invoices iWHERE i.status = 'void'  AND i.voided_at >= DATE '2023-01-01'  AND i.voided_at < DATE '2026-01-01'ORDER BY entity_typeORDER BY entity_type NULLS LAST

   As each system received it; highlighted, the parts spelled for that system

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · ClickHouse · chDB · clickhouse_brew

   SELECT
     'order cancellations' AS entity_type,
     COUNT(*) AS total_events,
     COUNT(*) FILTER (
       WHERE EXISTS (
         SELECT 1 FROM audit_log al
         WHERE al.entity_table = 'orders'
           AND al.entity_id = o.id
           AND al.tenant_id = o.tenant_id
       )
     ) AS events_with_match,
     100.0 * COUNT(*) FILTER (
       WHERE EXISTS (
         SELECT 1 FROM audit_log al
         WHERE al.entity_table = 'orders'
           AND al.entity_id = o.id
           AND al.tenant_id = o.tenant_id
       )
     ) / NULLIF(COUNT(*), 0) AS coverage_rate_pct,
     COUNT(DISTINCT o.tenant_id) AS distinct_tenants
   FROM orders o
   WHERE o.status = 'cancelled'
     AND o.cancelled_at >= DATE '2023-01-01'
     AND o.cancelled_at < DATE '2026-01-01'
   
   UNION ALL
   
   SELECT
     'invoice voids' AS entity_type,
     COUNT(*) AS total_events,
     COUNT(*) FILTER (
       WHERE EXISTS (
         SELECT 1 FROM audit_log al
         WHERE al.entity_table = 'invoices'
           AND al.entity_id = i.id
           AND al.tenant_id = i.tenant_id
       )
     ) AS events_with_match,
     100.0 * COUNT(*) FILTER (
       WHERE EXISTS (
         SELECT 1 FROM audit_log al
         WHERE al.entity_table = 'invoices'
           AND al.entity_id = i.id
           AND al.tenant_id = i.tenant_id
       )
     ) / NULLIF(COUNT(*), 0) AS coverage_rate_pct,
     COUNT(DISTINCT i.tenant_id) AS distinct_tenants
   FROM invoices i
   WHERE i.status = 'void'
     AND i.voided_at >= DATE '2023-01-01'
     AND i.voided_at < DATE '2026-01-01'
   
   ORDER BY entity_type NULLS LAST

   • DataFusionThis feature is not implemented: Physical plan does not support logical expression Exists(Exists { subquery: <subquery>, negated: false })
   • ClickHouseCode: 48. DB::Exception: Correlated subqueries in aggregate function argument are not supported. (NOT_IMPLEMENTED)

   Scale factor 1000, c8a.24xlarge, after the last load. q312 on the start page, with every configuration.

7. Everything, as recorded

   The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

   Viktor's whole session, 7 turns

   • The request, as Viktor received it

     Report request from Noah Fischer (analyst for the Chief Information Security Officer):
     
     Title: Audit traceability of order cancellations and invoice voids
     Question: For the two 'deletion-like' events on our financial documents that don't use soft-delete — a cancelled order, a voided invoice — is there any audit_log record at all tying back to that specific row?
     Grain: one row per entity type: 'order cancellations' and 'invoice voids'
     Columns:
     - entity type
     - total events (all-time, 2023-01-01 to 2025-12-31)
     - events with at least one matching audit_log row (any action, entity_table + entity_id match)
     - coverage rate (%)
     - distinct tenants represented
     Details: Row 1: orders where status = 'cancelled'. For each such order, check whether any audit_log row exists with entity_table = 'orders' and entity_id = orders.id (any action, not just 'delete', since the logged action type for a cancellation isn't known in advance). Row 2: invoices where status = 'void'. Same existence check against audit_log with entity_table = 'invoices' and entity_id = invoices.id. coverage_rate = events with a match / total events, rounded to 1 decimal percent. This is a looser test than the soft-delete report (any audit row at all for that record, not specifically a 'delete' action) — flag in the report notes that even a 100% match here wouldn't confirm the cancellation/void itself was logged, only that the record has some audit trail presence.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Viktor calls dry_run

     SELECT
       'order cancellations' AS entity_type,
       COUNT(*) AS total_events,
       COUNT(*) FILTER (
         WHERE EXISTS (
           SELECT 1 FROM audit_log al
           WHERE al.entity_table = 'orders'
             AND al.entity_id = o.id
             AND al.tenant_id = o.tenant_id
         )
       ) AS events_with_match,
       ROUND(
         100.0 * COUNT(*) FILTER (
           WHERE EXISTS (
             SELECT 1 FROM audit_log al
             WHERE al.entity_table = 'orders'
               AND al.entity_id = o.id
               AND al.tenant_id = o.tenant_id
           )
         ) / NULLIF(COUNT(*), 0), 1
       ) AS coverage_rate_pct,
       COUNT(DISTINCT o.tenant_id) AS distinct_tenants
     FROM orders o
     WHERE o.status = 'cancelled'
       AND o.cancelled_at >= DATE '2023-01-01'
       AND o.cancelled_at < DATE '2026-01-01'
     
     UNION ALL
     
     SELECT
       'invoice voids' AS entity_type,
       COUNT(*) AS total_events,
       COUNT(*) FILTER (
         WHERE EXISTS (
           SELECT 1 FROM audit_log al
           WHERE al.entity_table = 'invoices'
             AND al.entity_id = i.id
             AND al.tenant_id = i.tenant_id
         )
       ) AS events_with_match,
       ROUND(
         100.0 * COUNT(*) FILTER (
           WHERE EXISTS (
             SELECT 1 FROM audit_log al
             WHERE al.entity_table = 'invoices'
               AND al.entity_id = i.id
               AND al.tenant_id = i.tenant_id
           )
         ) / NULLIF(COUNT(*), 0), 1
       ) AS coverage_rate_pct,
       COUNT(DISTINCT i.tenant_id) AS distinct_tenants
     FROM invoices i
     WHERE i.status = 'void'
       AND i.voided_at >= DATE '2023-01-01'
       AND i.voided_at < DATE '2026-01-01'
     
     ORDER BY entity_type

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: entity_type, total_events, events_with_match, coverage_rate_pct, distinct_tenants

   • ViktorQuery compiles cleanly. Submitting.
   • Viktor calls submit

     SELECT
       'order cancellations' AS entity_type,
       COUNT(*) AS total_events,
       COUNT(*) FILTER (
         WHERE EXISTS (
           SELECT 1 FROM audit_log al
           WHERE al.entity_table = 'orders'
             AND al.entity_id = o.id
             AND al.tenant_id = o.tenant_id
         )
       ) AS events_with_match,
       ROUND(
         100.0 * COUNT(*) FILTER (
           WHERE EXISTS (
             SELECT 1 FROM audit_log al
             WHERE al.entity_table = 'orders'
               AND al.entity_id = o.id
               AND al.tenant_id = o.tenant_id
           )
         ) / NULLIF(COUNT(*), 0), 1
       ) AS coverage_rate_pct,
       COUNT(DISTINCT o.tenant_id) AS distinct_tenants
     FROM orders o
     WHERE o.status = 'cancelled'
       AND o.cancelled_at >= DATE '2023-01-01'
       AND o.cancelled_at < DATE '2026-01-01'
     
     UNION ALL
     
     SELECT
       'invoice voids' AS entity_type,
       COUNT(*) AS total_events,
       COUNT(*) FILTER (
         WHERE EXISTS (
           SELECT 1 FROM audit_log al
           WHERE al.entity_table = 'invoices'
             AND al.entity_id = i.id
             AND al.tenant_id = i.tenant_id
         )
       ) AS events_with_match,
       ROUND(
         100.0 * COUNT(*) FILTER (
           WHERE EXISTS (
             SELECT 1 FROM audit_log al
             WHERE al.entity_table = 'invoices'
               AND al.entity_id = i.id
               AND al.tenant_id = i.tenant_id
           )
         ) / NULLIF(COUNT(*), 0), 1
       ) AS coverage_rate_pct,
       COUNT(DISTINCT i.tenant_id) AS distinct_tenants
     FROM invoices i
     WHERE i.status = 'void'
       AND i.voided_at >= DATE '2023-01-01'
       AND i.voided_at < DATE '2026-01-01'
     
     ORDER BY entity_type

   • The warehouse answers

     Submitted to tonight's batch. Your work on this request is done.

   • ViktorSubmitted. Summary for the record: - **Grain:** one row per entity type (`order cancellations`, `invoice voids`). - **Logic:** for each cancelled order / voided invoice, an `EXISTS` check against `audit_log` on `(entity_table, entity_id, tenant_id)`, any `action` — matching the "looser than soft-delete" test requested. - **Date scoping:** used `orders.cancelled_at` and `invoices.voided_at` bounded to 2023-01-01–2025-12-31, since those are the natural event timestamps for each deletion-like state. - **Caveat flagged in the note:** a match only proves *some* audit trail exists for that row — not that the cancellation/void action itself was captured.

   Not shown here: 240 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Viktor's transcript (266 KB) · Noah Fischer's transcript (875 KB) · Henrik Aalto's transcript (262 KB) · the night's plan (5 KB) · the night's reports (7 KB) · the week's memo (3 KB)