DATA OPS

Detect and redact PII in uploaded CSVs before warehouse load

On a CSV upload to S3, scans columns for PII, redacts or hashes sensitive fields, writes the sanitized file to R2, loads it into Snowflake.

CategoryData Ops
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew CSV uploaded to S3AWS S3
  • ActionScan columns and classify PII fields
  • LogicApply redaction policy to flagged columns
  • ActionWrite sanitized file to R2CloudflareCloudflare R2
  • ActionLoad redacted data into SnowflakeSnowflakeSnowflake
  • OutputPost redaction audit summary to SlackSlack

What it does

Screens every incoming CSV for personally identifiable information before it enters the warehouse. It detects PII columns (emails, phone numbers, SSNs, names), applies redaction or one-way hashing per a policy, stores the sanitized copy in R2, then loads the clean data into Snowflake. An audit summary of what was redacted is posted to Slack.

When to use it

Use it when CSV feeds may contain regulated personal data you are not permitted to land raw in analytics storage. This enforces a redaction policy automatically so engineers never have to manually scrub files, and gives compliance a per-file audit record.

How it works

  1. 1An S3 object-created event triggers the run.
  2. 2The pipeline scans each column with PII detection rules and classifies sensitive fields.
  3. 3A logic step applies the policy — hash, mask, or drop — to the flagged columns.
  4. 4The sanitized file is written to an R2 bucket as the retained clean copy.
  5. 5The redacted data is loaded into Snowflake.
  6. 6A redaction audit summary (fields touched, action taken, row count) is posted to Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AWS S3Buckets, objects, signed URLs.
  2. 2
    Connect Cloudflare R2Object storage, S3-compatible.
  3. 3
    Connect SnowflakeWarehouses, queries, shares.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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