SECOPS

Daily Loom library secret audit with findings tracker

Each morning, sweeps Loom recordings published in the last 24 hours for exposed secrets and logs every finding to a Linear-tracked queue with severity and owner.

CategorySecOps
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule
  • ActionList Loom recordings from last 24hLoomLoom
  • ActionScan transcript and frames for secretsOpenAI
  • LogicAssign severity by secret type
  • ActionCreate Linear finding per leakLinearLinear
  • OutputPost daily findings digest to SlackSlack

What it does

This scheduled audit catches anything the real-time webhook missed (older videos re-shared, backfilled libraries, recordings published while a webhook was down). It lists the last day's Loom recordings, scans each for credentials, and files structured findings so security can work a queue rather than chase pings.

When to use it

Use this as the safety net beneath a real-time scanner, or as a standalone control if you'd rather batch-review once a day. Ideal for teams that need an auditable paper trail of who leaked what and whether it was triaged.

How it works

  1. 1A daily schedule triggers the audit.
  2. 2The flow lists all Loom recordings published in the last 24 hours.
  3. 3Each recording's transcript and frame text is fetched and scanned for secret patterns.
  4. 4A logic step assigns severity by secret type (live API key = high, internal URL = low).
  5. 5For each finding, a Linear issue is created with the recording link, secret type, severity, and the recording's owner pre-assigned.
  6. 6A summary digest of all findings is posted to Slack so the team sees the day's volume at a glance.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LoomVideo transcripts, libraries.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  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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