CHATBOTS

Notion Access Request Intake with Risk-Tiered Approval

Watches a Notion access-request database, classifies each request by data sensitivity.

CategoryChatbots
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew row added to Notion access-request databaseNotionNotion
  • LogicClassify request by data sensitivity tier
  • ActionEscalate high-risk requests to owner + security in SlackSlack
  • ActionExecute scoped GRANT with expiry in SnowflakeSnowflakeSnowflake
  • ActionUpdate Notion row with status and approversNotionNotion
  • OutputPost grant result to SlackSlack

What it does

Lets teams file warehouse access requests as Notion rows, then applies risk-tiered routing. Low-risk requests (non-PII read on sandbox/analytics schemas) are auto-approved and granted. Anything touching sensitive schemas (PII, finance, raw) requires both the data owner and a security approver before the grant runs. Every outcome is written back to the Notion row.

When to use it

Use it when requests already live in a Notion intake board and you want lightweight requests handled instantly while protecting sensitive data with stricter sign-off. It removes the bottleneck on safe asks without loosening control on the risky ones.

How it works

  1. 1A new row is added to the Notion access-request database (trigger).
  2. 2The flow classifies the requested object against a sensitivity tier map.
  3. 3It branches: low-risk goes straight to grant; high-risk requires owner + security approval via Slack.
  4. 4For high-risk, it collects both approvals before proceeding.
  5. 5It executes the scoped `GRANT` in Snowflake with an expiry tag.
  6. 6It updates the Notion row with status, approvers, and expiry, and posts the result to Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect NotionPages, databases, comments.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  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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