Queries · q285 · Head of Risk and Payments · week 2, night 4
“Do the 12 data_subject_requests rows with received_at before 2023-01-01 represent any hidden backlog or unresolved risk, outside the window we've otherwise been reporting on?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 1; shared with Rosa Van den Berg, Head of Data
An enterprise prospect sent a security and compliance questionnaire covering account security, access control, privacy requests, data retention and audit trails. Sales wants answers backed by evidence from our own systems, not by policy documents.
The manager, at the start of week 2
Subject: Re: Questionnaire memo — good work, a few threads to pull
Claire,
This is a strong memo, and the gaps you flagged are exactly the kind of thing I'd rather surface now than have a prospect's security team find later. A few things stand out and I want to understand them better before we finalize anything.
The February 2025 login spike is the one that worries me most. A 10x jump in failures with no corresponding rise in flagged suspicious activity is a real detection gap, not just a reporting curiosity — if that was a credential-stuffing attempt, I need to know whether it actually got anywhere.
Questions for this week:
Keep the role/permission logging gap and the broken query as noted — no need to keep spending slots chasing those; I'll raise the first with engineering myself and we can note the second as a known limitation in the memo.
Thanks, Aylin
The analyst · week 2, night 4, request 1 of 2
Night 3 results round out the picture nicely:
Report 1 (pre-2023 anomaly): change_history (2 outlier rows out of ~31,099) and compliance_events (1 outlier row out of 9) are clearly negligible noise. data_subject_requests is different: 12 of 60 total rows (20%) predate 2023-01-01 (1 in 2020, 4 in 2021, 7 in 2022) — not negligible. Reassuringly, the 2023-2025 rows sum to exactly 48 (10+23+15), matching the "~48 requests since 2023" figure I've already reported, so that number is internally consistent and unaffected — but I want to quickly check whether any of those 12 pre-2023 rows represent unresolved/backlog risk before I close this out, since they're sitting entirely outside the window I've been reporting on.
Reports 2 and 3 (Feb 2025 pattern + quarterly history) make the spike's character much clearer: 17/11/8 distinct user agents at the three merchants respectively with no single user agent dominating (~25-27% share) — not bot-like; and the quarterly history shows every other quarter at all three merchants sits at a stable 4.8-7.3% fail rate, with Q1 2025 alone spiking to 61-68% — a clean, isolated one-quarter event, not a recurring problem. One more thing worth nailing down before I write the memo: at each merchant individually, ~74-75% of failures cluster in just 3 days — I want to check whether it's the same 3 days across all three merchants, which would point to one shared platform-level cause rather than three coincidentally-timed separate issues, and whether access_denied logging was silent specifically on those days (not just the month overall).
This is the last night, so I'm keeping it tight: (1) a quick status check on the 12 pre-2023 DSAR rows, to close out any residual backlog question; (2) a day-by-day breakdown for February 2025 across the three merchants (failed logins per merchant plus access_denied events) to test the shared-cause hypothesis precisely. I'm leaving the third slot unused — I have what I need to write a confident memo and don't have a well-justified third question tonight.
The engineer · 1 dry run on the empty schema, then the submission
select status, count(*) as count, count(completed_at) as count_with_completed_at, min(received_at) as min_received_at, max(received_at) as max_received_at from data_subject_requests where received_at < date '2023-01-01' group by status order by status
“Query compiles cleanly. Submitting.”
“Submitted. The report groups the 12 pre-2023 data_subject_requests rows by status, showing count, how many have completed_at populated, and the min/max received_at per status — Claire can use this to check for any open/unresolved statuses among them.”
As Claire Dubois saw it, from the small dataset the agents worked on
status | count | count_with_completed_at | min_received_at | max_received_at ----------+-------+-------------------------+----------------------------+--------------------------- completed | 12 | 12 | 2020-04-24 22:06:57.286184 | 2022-11-20 04:39:40.390136 (1 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 status, count(*) as count, count(completed_at) as count_with_completed_at, min(received_at) as min_received_at, max(received_at) as max_received_atfrom data_subject_requestswhere received_at < date '2023-01-01'group by statusorder by statusorder by status NULLS LAST
select status, count(*) as count, count(completed_at) as count_with_completed_at, min(received_at) as min_received_at, max(received_at) as max_received_at from data_subject_requests where received_at < date '2023-01-01' group by status order by 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 Claire Dubois (analyst for the Head of Risk and Payments): Title: Pre-2023 data subject requests: status and completion check Question: Do the 12 data_subject_requests rows with received_at before 2023-01-01 represent any hidden backlog or unresolved risk, outside the window we've otherwise been reporting on? Grain: one row per status value, restricted to data_subject_requests rows with received_at < 2023-01-01 Columns: - status - count - count_with_completed_at - min_received_at - max_received_at Details: Source: data_subject_requests, filtered to received_at < 2023-01-01 (expect 12 rows total across statuses). Purpose: these rows predate the system's stated 2023-01-01 history start and are likely migration/seed artifacts, but before treating them as pure noise I want to confirm none of them show an open/unresolved status that would be backlog we haven't accounted for. Order by status. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runselect status, count(*) as count, count(completed_at) as count_with_completed_at, min(received_at) as min_received_at, max(received_at) as max_received_at from data_subject_requests where received_at < date '2023-01-01' group by status order by status
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: status, count, count_with_completed_at, min_received_at, max_received_at
submitselect status, count(*) as count, count(completed_at) as count_with_completed_at, min(received_at) as min_received_at, max(received_at) as max_received_at from data_subject_requests where received_at < date '2023-01-01' group by status order by status
Submitted to tonight's batch. Your work on this request is done.