DATA OPS

Alert On-Call When a Tagged PII Column Disappears

Detects when a previously classified PII column is dropped or renamed in Snowflake and pages the on-call data engineer via PagerDuty.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled scan interval
  • ActionQuery current Snowflake column inventorySnowflakeSnowflake
  • LogicFind known PII columns now missing
  • LogicBranch only if columns disappeared
  • ActionOpen PagerDuty incidentPagerDutyPagerDuty
  • OutputPost heads-up to SlackSlack

What it does

This workflow watches for the disappearance of columns that were previously confirmed as PII. When a tagged sensitive column is dropped or renamed between scans, it raises a PagerDuty incident so the on-call engineer can confirm the change was intentional and verify that retention, deletion, and consumer obligations are still met.

When to use it

Use it when removing a PII column has real consequences — broken right-to-be-forgotten pipelines, orphaned downstream joins, or audit-trail gaps. Additions get reviewed; deletions often go unnoticed until something downstream breaks. This closes that blind spot.

How it works

  1. 1A scheduled trigger starts the scan.
  2. 2Query the current Snowflake column inventory and compare it to the prior snapshot's set of known PII columns.
  3. 3A logic step isolates PII columns present before but missing now (dropped or renamed).
  4. 4If any disappeared, branch into alerting; otherwise exit quietly.
  5. 5Open a PagerDuty incident naming each removed PII column, its former table, and last-seen timestamp.
  6. 6Post a parallel heads-up to the data-governance Slack channel for visibility.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect PagerDutyIncidents, on-call, escalations.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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