Queries · q074 · Head of Risk and Payments · week 1, night 4

Failed login attempts and suspicious access patterns by month

“How much login activity fails, is there visible evidence of brute-force-style patterns, and are access-denied events actually being captured as suspicious activity over time?”

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

1. A business event lands on the Head of Risk and Payments's desk

   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.

   One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events

2. Aylin Demir writes to Claire Dubois

   The manager, at the start of week 1

   Subject: Evidence we need for the security/compliance questionnaire (this week's priority)

   Hi Claire,

   This questionnaire is going to a serious enterprise prospect, and Sales wants it backed by evidence from our own systems, not by our policy wording. Rosa and I are co-owning this, so your reports will feed both the questionnaire answers themselves and a broader readiness review Rosa's side is doing. Given you only get a handful of report slots a night, let's be selective and prioritize what a security reviewer will actually probe on. In my own words, here's what I need to know:

   1. Who can get into a tenant's account, and how tightly is that controlled? I want a sense of how access is granted and revoked in practice — e.g. how many people hold elevated/admin-type access, whether access grants look current and reviewed, and whether we can show that departed or inactive staff lose access promptly.
   2. Do we actually enforce strong login security, or does it just sit in a policy doc? I'd like evidence on real login behavior — things like how much of our staff and customer access is protected by extra verification steps, and whether we can see failed-login and suspicious-access patterns being caught rather than ignored.
   3. When someone asks us to access, correct, or delete their data, do we actually meet our own deadlines? This is the one I most expect to be pressed on. I want to see how data subject requests are tracked end to end — how many come in, how quickly they're handled, and whether any are overdue or slipping past the commitments we've made.
   4. Is there a real, tamper-evident trail of who did what, and is it kept long enough to be useful? I want evidence that meaningful changes and sensitive data access are actually being logged consistently (not just in theory), and that we're not quietly losing history before our stated retention period is up.
   5. If something does go wrong, do we notice? I'd like a sense of how many flagged security or compliance incidents we've had, how quickly they get resolved, and whether any are sitting open for a long time — that's the kind of thing a prospect's security team will zero in on.

   Given the slot constraints, please treat questions 3 and 1 as the top two for this week — those are the ones Sales says the prospect calls out by name. If we only get through three of the five, drop 5 first and fold it into next week.

   One more thing: since this report touches account security and personal data, please keep the output at a safe aggregate level — no need to name individual customers or staff unless I specifically ask.

   Let's sync briefly before you lock in Thursday night's batch so nothing gets wasted on a bounced or bundled request.

   Thanks, Aylin

   Show the whole note
   A note from the Head of Data, Rosa Van den Berg

   Hi Claire,

   Before we hand evidence to this prospect, I'd want us to be confident the underlying records are actually solid enough to stand behind in writing. A few things from my side:

   1. Completeness and continuity of the audit trail itself – do we have reason to believe the security/access logs are gap-free over the period we'd quote, or are there known stretches where logging was thin, delayed, or archived in a way that could make "no incidents" look like "no visibility"?
   2. One actor, one identity – when we pull evidence on who accessed or changed what, can we actually tie an action back to a single, consistent user record, or do duplicate/merged accounts and inconsistent actor references risk us over- or under-counting who touched sensitive data?
   3. Timestamp trust – for anything time-bound (response times on data requests, retention windows, how quickly access was revoked), are we confident the timestamps we'd cite are clean and consistently recorded, rather than something we'd need to caveat?

   If any of these raise flags on your end, I'd rather we know before it goes into a customer-facing answer than after.

   Thanks, Rosa

3. Claire Dubois plans the night

   The analyst · week 1, night 4, request 2 of 3

   Night 3 take-aways — one genuinely important finding, one bug, one solid result:

   Report 1 (audit coverage sweep): decisive and concerning. Across all 13 identity/access-control tables I checked (roles, user_roles, role_permissions, permissions, teams, team_members, api_keys, invitations, mfa_devices, sessions, service_accounts, personal_access_tokens — plus singular/plural variants), only the users table shows ANY audit_log/audit_log_archive activity at all (delete, export, login, logout, update — no create, oddly). Role and permission grant/revocation events are simply not captured in the audit trail, at any point in the 3-year history. This confirms last night's all-zero result wasn't a query bug — it's a real gap. This has to go into the memo plainly for both Aylin (we cannot evidence who granted/revoked a given access right, only today's snapshot) and Rosa (this is exactly the kind of audit-trail incompleteness she asked me to flag before it goes into a customer-facing answer).

   Report 2 (DSAR rejection timing) failed on execution: a cast error pulling in an unrelated product_variant row's after_data, which tells me the query matched change_history on entity_id alone without also constraining entity_table — entity_id isn't globally unique across tables, so it collided with an unrelated row. This is a fixable query bug, not a data problem, and worth one retry since it's the last loose end on our top priority (#3) — the only thing standing between us and a clean, fully-evidenced DSAR story.

   Report 3 (MFA adoption): first real evidence for priority #2. Confirmed-MFA coverage for active staff is 54.9% (57.8% any device) — material but not universal, worth citing with the caveat that ~45% of merchant admin accounts are not MFA-protected. Customer-facing accounts show 0% MFA entirely (not even attempted) — expected if MFA is currently a staff-only feature, but worth stating plainly rather than silently.

   No sync reply from Aylin arrived before lock-in, so I'm proceeding on her original stated priority order: #1 and #3 are solid for the memo (with the audit-gap and rejection-timestamp caveats to close out), so tonight's last 3 slots go to: (1) a defensively-rewritten retry of the DSAR rejection-timing query, (2) the other half of priority #2 — failed-login and suspicious-access patterns over time, since we've only covered MFA adoption so far, and (3) a quick retention-policy inventory for the audit/compliance tables, which gives us a toehold on priority #4 (ahead of #5, which Aylin said to drop first) and directly tests Rosa's "are we quietly losing history early" concern given what we now know about the audit-trail gap.

   Read the whole plan
   Failed login attempts and suspicious access patterns by month

   Question

   How much login activity fails, is there visible evidence of brute-force-style patterns, and are access-denied events actually being captured as suspicious activity over time?

   One row per

   calendar month (occurred_at), platform-wide, from 2023-01 through 2025-12

   Columns

   • month (YYYY-MM)
   • total_login_attempts (from login_attempts)
   • failed_login_attempts (succeeded = false)
   • pct_failed
   • emails_with_5plus_failed_in_month (distinct email values with >=5 failed login_attempts rows in the month — a brute-force proxy)
   • audit_access_denied_events (count from audit_log UNION audit_log_archive where action = 'access_denied', same month)

4. Ines 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)

   WITH months AS (
     SELECT DISTINCT month_label, month_start
     FROM calendar_days
     WHERE calendar_date BETWEEN DATE '2023-01-01' AND DATE '2025-12-31'
   ),
   login_month AS (
     SELECT date_trunc('month', occurred_at)::date AS month_start,
            email,
            succeeded
     FROM login_attempts
   ),
   login_agg AS (
     SELECT month_start,
            COUNT(*) AS total_login_attempts,
            COUNT(*) FILTER (WHERE succeeded = false) AS failed_login_attempts
     FROM login_month
     GROUP BY month_start
   ),
   brute_force AS (
     SELECT month_start, COUNT(DISTINCT email) AS emails_with_5plus_failed
     FROM (
       SELECT month_start, email, COUNT(*) AS cnt
       FROM login_month
       WHERE succeeded = false AND email IS NOT NULL
       GROUP BY month_start, email
       HAVING COUNT(*) >= 5
     ) t
     GROUP BY month_start
   ),
   audit_combined AS (
     SELECT occurred_at, action FROM audit_log WHERE action = 'access_denied'
     UNION ALL
     SELECT occurred_at, action FROM audit_log_archive WHERE action = 'access_denied'
   ),
   audit_agg AS (
     SELECT date_trunc('month', occurred_at)::date AS month_start,
            COUNT(*) AS audit_access_denied_events
     FROM audit_combined
     GROUP BY month_start
   )
   SELECT
     m.month_label AS month,
     COALESCE(la.total_login_attempts, 0) AS total_login_attempts,
     COALESCE(la.failed_login_attempts, 0) AS failed_login_attempts,
     ROUND(CASE WHEN COALESCE(la.total_login_attempts,0) = 0 THEN 0
          ELSE 100.0 * la.failed_login_attempts / la.total_login_attempts END, 2) AS pct_failed,
     COALESCE(bf.emails_with_5plus_failed, 0) AS emails_with_5plus_failed_in_month,
     COALESCE(aa.audit_access_denied_events, 0) AS audit_access_denied_events
   FROM months m
   LEFT JOIN login_agg la ON la.month_start = m.month_start
   LEFT JOIN brute_force bf ON bf.month_start = m.month_start
   LEFT JOIN audit_agg aa ON aa.month_start = m.month_start
   ORDER BY m.month_start

   “Query compiles and returns the expected columns. Submitting now.”

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

   “Submitted. The report gives one row per month (2023-01 through 2025-12) with: total and failed login attempts, pct_failed, a distinct-email brute-force proxy (≥5 failed logins by the same email in that month), and the parallel access_denied audit signal from audit_log+audit_log_archive. Months with no activity still appear with zeros, thanks to the calendar join, so Aylin can see any spikes or gaps across the full history.”

5. The report comes back overnight

   As Claire Dubois saw it, from the small dataset the agents worked on

   month   | total_login_attempts | failed_login_attempts | pct_failed | emails_with_5plus_failed_in_month | audit_access_denied_events
   --------+----------------------+-----------------------+------------+-----------------------------------+---------------------------
   2023-01 |                 3491 |                   213 |        6.1 |                                 0 |                         11
   2023-02 |                 2949 |                   176 |       5.97 |                                 0 |                          3
   2023-03 |                 3368 |                   185 |       5.49 |                                 1 |                          6
   2023-04 |                 3307 |                   208 |       6.29 |                                 0 |                          5
   2023-05 |                 3614 |                   223 |       6.17 |                                 1 |                          7
   2023-06 |                 3611 |                   250 |       6.92 |                                 0 |                          3
   2023-07 |                 3637 |                   208 |       5.72 |                                 1 |                          3
   2023-08 |                 3905 |                   260 |       6.66 |                                 0 |                          3
   2023-09 |                 3779 |                   244 |       6.46 |                                 1 |                          6
   2023-10 |                 4071 |                   242 |       5.94 |                                 0 |                          8
   2023-11 |                 5038 |                   302 |       5.99 |                                 2 |                          5
   2023-12 |                 4551 |                   288 |       6.33 |                                 0 |                          4
   2024-01 |                 4590 |                   263 |       5.73 |                                 1 |                          3
   2024-02 |                 4070 |                   233 |       5.72 |                                 0 |                          7
   2024-03 |                 4654 |                   291 |       6.25 |                                 1 |                          5
   2024-04 |                 4676 |                   286 |       6.12 |                                 0 |                          4
   2024-05 |                 4977 |                   317 |       6.37 |                                 1 |                          5
   2024-06 |                 4559 |                   314 |       6.89 |                                 0 |                          5
   2024-07 |                 5051 |                   289 |       5.72 |                                 0 |                          5
   2024-08 |                 4990 |                   289 |       5.79 |                                 0 |                          5
   2024-09 |                 4952 |                   286 |       5.78 |                                 0 |                          3
   2024-10 |                 5453 |                   312 |       5.72 |                                 1 |                          5
   2024-11 |                 6131 |                   360 |       5.87 |                                 0 |                          4
   2024-12 |                 6198 |                   360 |       5.81 |                                 1 |                          5
   2025-01 |                 5933 |                   377 |       6.35 |                                 0 |                          5
   2025-02 |                12211 |                  7429 |      60.84 |                               102 |                          6
   2025-03 |                 5627 |                   373 |       6.63 |                                 0 |                         10
   2025-04 |                 5636 |                   323 |       5.73 |                                 0 |                          2
   2025-05 |                 5886 |                   376 |       6.39 |                                 0 |                          2
   2025-06 |                 5439 |                   306 |       5.63 |                                 1 |                          5
   2025-07 |                 6110 |                   349 |       5.71 |                                 1 |                          5
   2025-08 |                 5877 |                   393 |       6.69 |                                 0 |                          3
   2025-09 |                 6125 |                   377 |       6.16 |                                 0 |                          1
   2025-10 |                 6711 |                   411 |       6.12 |                                 0 |                          7
   2025-11 |                 7532 |                   477 |       6.33 |                                 0 |                          5
   2025-12 |                 7598 |                   443 |       5.83 |                                 1 |                          5
   (36 rows)

6. What came of it

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

   Later queries that build on this report:

   • q276February 2025 failed login concentration by merchant
   • q277February 2025 repeat-failure accounts: did they succeed afterward?

   It builds on:

   • q073MFA device adoption by user segment and status

7. It becomes benchmark query q074

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

   • spellingcast (2×)
   • determinismtie-breaker: month_start does not determine month_label in the schema
   • 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 (2×)
   The benchmark text, against the submitted SQL

   WITH months AS (  SELECT DISTINCT month_label, month_start  FROM calendar_days  WHERE calendar_date BETWEEN DATE '2023-01-01' AND DATE '2025-12-31'),login_month AS (  SELECT date_trunc('month', occurred_at)::date AS month_start,  SELECT CAST(date_trunc('month', occurred_at) AS date) AS month_start,         email,         succeeded  FROM login_attempts),login_agg AS (  SELECT month_start,         COUNT(*) AS total_login_attempts,         COUNT(*) FILTER (WHERE succeeded = false) AS failed_login_attempts  FROM login_month  GROUP BY month_start),brute_force AS (  SELECT month_start, COUNT(DISTINCT email) AS emails_with_5plus_failed  FROM (    SELECT month_start, email, COUNT(*) AS cnt    FROM login_month    WHERE succeeded = false AND email IS NOT NULL    GROUP BY month_start, email    HAVING COUNT(*) >= 5  ) t  GROUP BY month_start),audit_combined AS (  SELECT occurred_at, action FROM audit_log WHERE action = 'access_denied'  UNION ALL  SELECT occurred_at, action FROM audit_log_archive WHERE action = 'access_denied'),audit_agg AS (  SELECT date_trunc('month', occurred_at)::date AS month_start,  SELECT CAST(date_trunc('month', occurred_at) AS date) AS month_start,         COUNT(*) AS audit_access_denied_events  FROM audit_combined  GROUP BY month_start)SELECT  m.month_label AS month,  COALESCE(la.total_login_attempts, 0) AS total_login_attempts,  COALESCE(la.failed_login_attempts, 0) AS failed_login_attempts,  ROUND(CASE WHEN COALESCE(la.total_login_attempts,0) = 0 THEN 0       ELSE 100.0 * la.failed_login_attempts / la.total_login_attempts END, 2) AS pct_failed,  CASE WHEN COALESCE(la.total_login_attempts,0) = 0 THEN 0 ELSE 100.0 * la.failed_login_attempts / la.total_login_attempts END AS pct_failed,  COALESCE(bf.emails_with_5plus_failed, 0) AS emails_with_5plus_failed_in_month,  COALESCE(aa.audit_access_denied_events, 0) AS audit_access_denied_eventsFROM months mLEFT JOIN login_agg la ON la.month_start = m.month_startLEFT JOIN brute_force bf ON bf.month_start = m.month_startLEFT JOIN audit_agg aa ON aa.month_start = m.month_startORDER BY m.month_startORDER BY m.month_start NULLS LAST, m.month_label 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

   WITH months AS (
     SELECT DISTINCT month_label, month_start
     FROM calendar_days
     WHERE calendar_date BETWEEN DATE '2023-01-01' AND DATE '2025-12-31'
   ),
   login_month AS (
     SELECT CAST(date_trunc('month', occurred_at) AS date) AS month_start,
            email,
            succeeded
     FROM login_attempts
   ),
   login_agg AS (
     SELECT month_start,
            COUNT(*) AS total_login_attempts,
            COUNT(*) FILTER (WHERE succeeded = false) AS failed_login_attempts
     FROM login_month
     GROUP BY month_start
   ),
   brute_force AS (
     SELECT month_start, COUNT(DISTINCT email) AS emails_with_5plus_failed
     FROM (
       SELECT month_start, email, COUNT(*) AS cnt
       FROM login_month
       WHERE succeeded = false AND email IS NOT NULL
       GROUP BY month_start, email
       HAVING COUNT(*) >= 5
     ) t
     GROUP BY month_start
   ),
   audit_combined AS (
     SELECT occurred_at, action FROM audit_log WHERE action = 'access_denied'
     UNION ALL
     SELECT occurred_at, action FROM audit_log_archive WHERE action = 'access_denied'
   ),
   audit_agg AS (
     SELECT CAST(date_trunc('month', occurred_at) AS date) AS month_start,
            COUNT(*) AS audit_access_denied_events
     FROM audit_combined
     GROUP BY month_start
   )
   SELECT
     m.month_label AS month,
     COALESCE(la.total_login_attempts, 0) AS total_login_attempts,
     COALESCE(la.failed_login_attempts, 0) AS failed_login_attempts,
     CASE WHEN COALESCE(la.total_login_attempts,0) = 0 THEN 0
    ELSE 100.0 * la.failed_login_attempts / la.total_login_attempts END AS pct_failed,
     COALESCE(bf.emails_with_5plus_failed, 0) AS emails_with_5plus_failed_in_month,
     COALESCE(aa.audit_access_denied_events, 0) AS audit_access_denied_events
   FROM months m
   LEFT JOIN login_agg la ON la.month_start = m.month_start
   LEFT JOIN brute_force bf ON bf.month_start = m.month_start
   LEFT JOIN audit_agg aa ON aa.month_start = m.month_start
   ORDER BY m.month_start NULLS LAST, m.month_label NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q074 on the start page, with every configuration.

8. 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

   Ines's whole session, 7 turns

   • The request, as Ines received it

     Report request from Claire Dubois (analyst for the Head of Risk and Payments):
     
     Title: Failed login attempts and suspicious access patterns by month
     Question: How much login activity fails, is there visible evidence of brute-force-style patterns, and are access-denied events actually being captured as suspicious activity over time?
     Grain: one row per calendar month (occurred_at), platform-wide, from 2023-01 through 2025-12
     Columns:
     - month (YYYY-MM)
     - total_login_attempts (from login_attempts)
     - failed_login_attempts (succeeded = false)
     - pct_failed
     - emails_with_5plus_failed_in_month (distinct email values with >=5 failed login_attempts rows in the month — a brute-force proxy)
     - audit_access_denied_events (count from audit_log UNION audit_log_archive where action = 'access_denied', same month)
     Details: Sources: login_attempts for the login outcome series; audit_log + audit_log_archive (action='access_denied') as a separate parallel signal of flagged suspicious access, same month grain, not joined row-by-row. Purpose: give Aylin real evidence that failed logins and suspicious access attempts are visible in the data (not just policy), and whether volumes/patterns look stable or show spikes worth a follow-up. Order by month ascending.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Ines calls dry_run

     WITH months AS (
       SELECT DISTINCT month_label, month_start
       FROM calendar_days
       WHERE calendar_date BETWEEN DATE '2023-01-01' AND DATE '2025-12-31'
     ),
     login_month AS (
       SELECT date_trunc('month', occurred_at)::date AS month_start,
              email,
              succeeded
       FROM login_attempts
     ),
     login_agg AS (
       SELECT month_start,
              COUNT(*) AS total_login_attempts,
              COUNT(*) FILTER (WHERE succeeded = false) AS failed_login_attempts
       FROM login_month
       GROUP BY month_start
     ),
     brute_force AS (
       SELECT month_start, COUNT(DISTINCT email) AS emails_with_5plus_failed
       FROM (
         SELECT month_start, email, COUNT(*) AS cnt
         FROM login_month
         WHERE succeeded = false AND email IS NOT NULL
         GROUP BY month_start, email
         HAVING COUNT(*) >= 5
       ) t
       GROUP BY month_start
     ),
     audit_combined AS (
       SELECT occurred_at, action FROM audit_log WHERE action = 'access_denied'
       UNION ALL
       SELECT occurred_at, action FROM audit_log_archive WHERE action = 'access_denied'
     ),
     audit_agg AS (
       SELECT date_trunc('month', occurred_at)::date AS month_start,
              COUNT(*) AS audit_access_denied_events
       FROM audit_combined
       GROUP BY month_start
     )
     SELECT
       m.month_label AS month,
       COALESCE(la.total_login_attempts, 0) AS total_login_attempts,
       COALESCE(la.failed_login_attempts, 0) AS failed_login_attempts,
       ROUND(CASE WHEN COALESCE(la.total_login_attempts,0) = 0 THEN 0
            ELSE 100.0 * la.failed_login_attempts / la.total_login_attempts END, 2) AS pct_failed,
       COALESCE(bf.emails_with_5plus_failed, 0) AS emails_with_5plus_failed_in_month,
       COALESCE(aa.audit_access_denied_events, 0) AS audit_access_denied_events
     FROM months m
     LEFT JOIN login_agg la ON la.month_start = m.month_start
     LEFT JOIN brute_force bf ON bf.month_start = m.month_start
     LEFT JOIN audit_agg aa ON aa.month_start = m.month_start
     ORDER BY m.month_start

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: month, total_login_attempts, failed_login_attempts, pct_failed, emails_with_5plus_failed_in_month, audit_access_denied_events

   • InesQuery compiles and returns the expected columns. Submitting now.
   • Ines calls submit

     WITH months AS (
       SELECT DISTINCT month_label, month_start
       FROM calendar_days
       WHERE calendar_date BETWEEN DATE '2023-01-01' AND DATE '2025-12-31'
     ),
     login_month AS (
       SELECT date_trunc('month', occurred_at)::date AS month_start,
              email,
              succeeded
       FROM login_attempts
     ),
     login_agg AS (
       SELECT month_start,
              COUNT(*) AS total_login_attempts,
              COUNT(*) FILTER (WHERE succeeded = false) AS failed_login_attempts
       FROM login_month
       GROUP BY month_start
     ),
     brute_force AS (
       SELECT month_start, COUNT(DISTINCT email) AS emails_with_5plus_failed
       FROM (
         SELECT month_start, email, COUNT(*) AS cnt
         FROM login_month
         WHERE succeeded = false AND email IS NOT NULL
         GROUP BY month_start, email
         HAVING COUNT(*) >= 5
       ) t
       GROUP BY month_start
     ),
     audit_combined AS (
       SELECT occurred_at, action FROM audit_log WHERE action = 'access_denied'
       UNION ALL
       SELECT occurred_at, action FROM audit_log_archive WHERE action = 'access_denied'
     ),
     audit_agg AS (
       SELECT date_trunc('month', occurred_at)::date AS month_start,
              COUNT(*) AS audit_access_denied_events
       FROM audit_combined
       GROUP BY month_start
     )
     SELECT
       m.month_label AS month,
       COALESCE(la.total_login_attempts, 0) AS total_login_attempts,
       COALESCE(la.failed_login_attempts, 0) AS failed_login_attempts,
       ROUND(CASE WHEN COALESCE(la.total_login_attempts,0) = 0 THEN 0
            ELSE 100.0 * la.failed_login_attempts / la.total_login_attempts END, 2) AS pct_failed,
       COALESCE(bf.emails_with_5plus_failed, 0) AS emails_with_5plus_failed_in_month,
       COALESCE(aa.audit_access_denied_events, 0) AS audit_access_denied_events
     FROM months m
     LEFT JOIN login_agg la ON la.month_start = m.month_start
     LEFT JOIN brute_force bf ON bf.month_start = m.month_start
     LEFT JOIN audit_agg aa ON aa.month_start = m.month_start
     ORDER BY m.month_start

   • The warehouse answers

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

   • InesSubmitted. The report gives one row per month (2023-01 through 2025-12) with: total and failed login attempts, pct_failed, a distinct-email brute-force proxy (≥5 failed logins by the same email in that month), and the parallel `access_denied` audit signal from `audit_log`+`audit_log_archive`. Months with no activity still appear with zeros, thanks to the calendar join, so Aylin can see any spikes or gaps across the full history.

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

   Ines's transcript (267 KB) · Claire Dubois's transcript (838 KB) · Aylin Demir's transcript (263 KB) · the night's plan (7 KB) · the night's reports (7 KB) · the week's memo (3 KB)