DATA OPS

BigQuery PII finding with Slack human approval gate

Scans new BigQuery columns for sensitive values and posts findings to Slack with Approve/Quarantine buttons, so a steward decides whether to lock the table.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps7
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled scan window
  • ActionSample new BigQuery columnsGoogle BigQueryBigQuery
  • LogicClassify and filter to PII matches
  • ActionPost finding to Slack with action buttonsSlack
  • LogicBranch on steward's Approve vs Quarantine
  • ActionApply deny-all access policy on datasetGoogle BigQueryBigQuery
  • OutputReply disposition to Slack threadSlack

What it does

Samples recently created BigQuery columns, flags ones that look like unmasked PII, and routes each finding to a Slack channel as an interactive message. A data steward clicks Approve to dismiss a false positive or Quarantine to have the workflow apply a deny-all access policy on the dataset. Nothing is locked without a human decision.

When to use it

Use it when your team wants automated PII detection but is not comfortable auto-revoking warehouse access, and prefers a fast Slack approval step before any table goes read-restricted.

How it works

  1. 1A schedule triggers the scan window.
  2. 2Query BigQuery for columns created since the last checkpoint and sample their values.
  3. 3Classify each sample and branch to keep only likely PII columns.
  4. 4Post each finding to Slack with the table name, matched categories, and Approve/Quarantine actions.
  5. 5On a Quarantine response, apply a restrictive IAM/access policy on the dataset.
  6. 6Post the final disposition back to the Slack thread for an audit trail.

Set it up

What you configure once, before turning it on.

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

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