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warmbox/docs/19-risk-register.md

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Risk Register

ID Risk Likelihood Impact Mitigation Owner
R1 ESP suspends satellite Gmail account Medium High Own pool, volume caps, app passwords, monitor errors Operator
R2 ESP detects warmup patterns Medium High Jitter, reply sampling, content diversity, no links Engineering
R3 AI generates placeholder/spam content Medium High Post-validation, regen loop, blocklists Engineering
R4 Shared peer pool toxicity (phase 3) High Critical Delay peer network; bounce monitoring Product
R5 SQLite corruption / lock under load Low Medium Migrate to Postgres before SaaS; queue writes Engineering
R6 Credential breach Low Critical Encryption, no log leaks, KMS for SaaS Security
R7 IMAP spam folder mismatch Medium Medium Auto-discovery, provider presets Engineering
R8 100% reply rate detection High (now) Medium Reply rate engine (E4) Engineering
R9 Operator sets max 50 on day 1 Medium High Grow recipe default; UI warnings Product
R10 Google Postmaster spam rate >0.3% Medium High Auto-pause, reduce volume, rescue Operations
R11 Legal/ToS violation (warmup as deception) Low High ToS: only owned mailboxes; internal engagement Legal
R12 OpenRouter free model quality Medium Low Model fallback list; paid tier option Engineering
R13 Cron overlap double-sends Medium Medium Idempotency keys, job queue Engineering
R14 Primary warms too fast vs domain age Medium High Domain age check; min 14-day warning Product
R15 Competitor ESP crackdown on warmup industry Low High Differentiate on own-pool + self-hosted control Product

Risk heat map

Impact
  High   | R4 R6 R9 R10 R11 R15
         | R1 R2 R3 R8 R14
  Medium | R5 R7 R13
  Low    | R12
         +------------------
           Low   Med   High
                Likelihood

Top 5 actions (before scaling FillCare)

  1. Implement E4 — reply rate sampling (addresses R8, R2)
  2. Implement E6 — AI validator (addresses R3)
  3. Implement E2 — Grow recipe enforced (addresses R9, R14)
  4. Run DNS checklist on fillcareapp.com (addresses R10, R14)
  5. Add auto-pause on spam rate (addresses R10, R1)

Monitoring triggers

Metric Warning Critical
Spam placement rate >15% 7d avg >30% 3d avg
Bounce rate >2% >5%
Auth failures 1 per mailbox 3 consecutive
AI validation fail rate >5% >15%
Reply rate deviation >±10% from target >±20%

Review cadence

  • Weekly during active warmup campaigns
  • Update register after each sprint retro