SECOPS

Chat-Driven Cloudflare WAF Investigation and Rule Authoring

An on-demand chat agent that an analyst asks about a suspicious source or path; it investigates the blocked-request history in Cloudflare, enriches the source reputation.

CategorySecOps
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerAnalyst chat message
  • ActionQuery Cloudflare blocked-request historyCloudflareCloudflare
  • ActionEnrich top sources with reputation contextExa
  • LogicRefine rule criteria with analyst in chat
  • ActionDraft and explain candidate firewall ruleOpenAI
  • OutputDeploy approved rule to Cloudflare log-onlyCloudflareCloudflare

What it does

It is an interactive analyst copilot for WAF investigations. You describe what looks off — an IP range, a path, a spike — and the agent queries Cloudflare, enriches the actors, and proposes a rule you can iterate on conversationally before approving deployment.

When to use it

When a human is actively hunting and wants a back-and-forth partner that can fetch evidence and write expressions on request, rather than a fixed scheduled pipeline.

How it works

  1. 1An analyst opens a chat and describes the suspicious activity.
  2. 2The agent queries Cloudflare for matching blocked-request history and volumes.
  3. 3It enriches the top sources with reputation and context via a web research step.
  4. 4The agent drafts a candidate firewall expression and explains the tradeoffs in chat.
  5. 5The analyst refines the criteria in conversation until satisfied and approves.
  6. 6On approval, the agent deploys the rule to Cloudflare in log-only mode and confirms.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
  2. 2
    Connect ExaNeural search across the web.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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.

Run this workflow in your colony.

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