Protect funnel signal
Keeps ad clicks, pricing visits, trial signups, and form submissions readable by separating legitimate demand from synthetic sessions before they enter attribution and CRM systems.
Rule lane
Each rule should show what it protects, what it blocks, and which commercial lane it changes.
This route compares rule logic by affected lane, action, and health so policy drift is visible before teams start arguing over noisy traffic after the fact.
| Rule | Purpose | Action | Affected lane | Health |
|---|---|---|---|---|
| Known automation fingerprint challenge | Challenge suspicious automation before analytics or form handlers ever see it. | managed_challenge | Bot scraping | healthy |
| Aggressive pricing page rate limit | Throttle repetitive pricing hits that often indicate scraping or competitor reconnaissance. | rate_limit | Pricing reconnaissance | healthy |
| Suspicious ASN blocklist | Block abusive infrastructure known for synthetic traffic and credential abuse. | block | Synthetic sessions | watch |
| Form abuse challenge | Challenge bursty POST activity against lead-capture endpoints. | js_challenge | Lead form abuse | critical |
Depth
Bot protection is a revenue-system control, not just an edge-security toggle.
CF Bot Shield TF connects Cloudflare rules, Terraform ownership, request-quality telemetry, and GTM impact so teams can see where synthetic traffic is inflating analytics, degrading lead quality, and forcing manual cleanup.
Reduce wasted spend
Turns bot pressure into a dollar and operations conversation: which campaigns are noisy, where review queues are polluted, and what controls should be automated first.
Make policy auditable
Shows rule intent, affected lanes, Terraform modules, and verification claims together so WAF changes are repeatable instead of one-off dashboard edits.
Explain risk clearly
Frames bot management as buyer-facing reliability: cleaner analytics, safer forms, lower manual triage, and fewer false confidence signals in board reporting.
Every Kinetic Gain surface turns invisible operating drag into reusable decision evidence.
This repo follows the suite pattern: name the operating risk, map the owner and control plane, expose JSON and static proof, and make the system understandable to both commercial leaders and technical reviewers.