OTHER

SOC2 Evidence Gap Agent Report (analyze S3 + register, narrate to Confluence)

An agent reviews SOC2 evidence in S3 against the control register in Confluence, reasons about which controls are stale, mislabeled, or missing artifacts entirely.

CategoryOther
Enginepaperclip
Difficultyadvanced
Triggermanual
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerChairman or scheduled review starts the agent
  • ActionRead evidence inventory from S3AWS S3
  • ActionRead current control register from ConfluenceConfluenceConfluence
  • LogicAgent classifies controls: fresh, stale, missing, mismatched
  • OutputPublish narrative gap-analysis page to ConfluenceConfluenceConfluence

What it does

An agent cross-references the SOC2 evidence actually present in S3 with the controls listed in your Confluence register. Beyond a simple date check, it reasons about three failure modes: artifacts that have aged past the audit window, controls in the register that have no matching evidence at all, and artifacts whose naming or folder doesn't map cleanly to a known control. It then writes a readable gap-analysis narrative back to Confluence with recommended next steps per control.

When to use it

Use it before an audit kickoff or quarterly review when you want judgment, not just a status table — surfacing orphaned evidence and unmapped controls that a deterministic date check would miss.

How it works

  1. 1The Chairman or a scheduled review kicks off the agent run.
  2. 2The agent reads the evidence inventory from S3 and the current control register from Confluence.
  3. 3It reasons over the two sets to classify each control as fresh, stale, missing-evidence, or mismatched, and drafts remediation guidance.
  4. 4It composes a narrative gap-analysis report.
  5. 5The report is published as a Confluence page as the output.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AWS S3Buckets, objects, signed URLs.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  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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