Every 6 hours: scan S3/GCS/Azure Blob for untagged sensitive objects (email, phone, SSN, card); alert compliance team with classification and encryption requirements.
What it does: Schedule (every 6h) → query cloud storage (S3, GCS, Azure Blob) for all objects → code uses ML/regex to detect potential PII (email, phone, ssn, credit_card, address) → for each detected object, check if tagged as confidential/sensitive in metadata → IF untagged PII found → LLM classifies risk (high: SSN/card; medium: email) + compliance exposure (GDPR, CCPA, HIPAA) + remediation (tag as confidential, enable encryption, restrict access) → Slack to #security-alerts.
Connect cloud storage: S3 (ListBucket, GetObject), GCS (list, read), Azure Blob (list)
Define PII patterns: regex for object names and sampling (can customize)
Set sensitivity levels: high/medium/low risk classification
Configure Slack channel: #security-alerts
Apps/nodes: Schedule, HTTP request (storage list API), code node (PII detection), Claude/OpenAI (risk classification), Slack.
Credentials required: Cloud storage API key, OpenAI account, Slack workspace.
Difficulty: Medium | Setup time: 9 minutes.
Business outcome: Untagged PII caught regularly → compliance violations ↓ prevented, data minimization ↑ enforced, audit readiness ↑.
With an active subscription, download the workflow file right from this page or your dashboard.
Choose Import from File and select the downloaded JSON. The full agent graph appears, ready to configure.
Each integration node prompts for credentials on first run. The setup guide lists every credential the agent expects.
Run once with sample input, confirm the expected output, then flip the activate toggle.
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