Skill: afrexai-cto/afrexai-incident-response-plan (version 1.0.1)

--- name: incident-response-plan description: > Generate a tailored incident response plan for AI agent deployments and SaaS operations. Covers detection, triage, containment, recovery, and post-mortem. Use when deploying agents to production, preparing for SOC2 audits, or building operational resilience. Built by AfrexAI. metadata: version: 1.0.0 author: AfrexAI tags: [incident-response, security, operations, devops, enterprise] --- # Incident Response Plan Generator Generate a production-ready incident response plan tailored to your AI agent deployment. ## When to Use - Deploying AI agents to production for the first time - Preparing for SOC2 or ISO 27001 audits - Client asks "what happens when something breaks?" - Building operational runbooks for managed AI services - After an incident — to prevent recurrence ## Input ``` Service: [Name of AI agent/service] Environment: [cloud provider, region, architecture] Data Sensitivity: [low/medium/high/critical] Team Size: [number of responders] SLA: [uptime target, e.g., 99.9%] Integrations: [list of connected systems] ``` ## Plan Structure ### 1. Severity Classification | Level | Description | Response Time | Examples | |-------|------------|---------------|----------| | SEV1 — Critical | Service down, data breach, financial impact | 15 min | Agent sending wrong data to clients, API keys exposed | | SEV2 — High | Degraded service, partial outage | 1 hour | Agent responses slow, one integration failing | | SEV3 — Medium | Non-critical issue, workaround exists | 4 hours | Minor accuracy drop, cosmetic errors | | SEV4 — Low | Enhancement, no immediate impact | Next business day | Feature request, optimization | ### 2. Detection & Alerting - Health check endpoints (every 60s) - Error rate thresholds (>1% = SEV3, >5% = SEV2, >25% = SEV1) - Response time monitoring (p99 > 2x baseline = alert) - Cost anomaly detection (>150% daily average) - Output quality sampling (random audit of agent responses) - Uptime monitoring (UptimeRobot, Pingdom, or custom) ### 3. Triage Checklist ```markdown □ Confirm the alert is real (not false positive) □ Classify severity (SEV1-4) □ Identify affected scope (which agents, which clients) □ Check recent changes (deploys, config changes, upstream) □ Assign incident commander □ Open incident channel/thread □ Notify affected stakeholders per SLA ``` ### 4. Containment Actions by Type **Agent Misbehavior:** - Pause agent processing (kill switch) - Revert to last known good config - Enable human-in-the-loop mode - Queue messages for manual review **Infrastructure Failure:** - Failover to backup region/instance - Scale horizontally if capacity issue - Check upstream dependencies (API providers, databases) - Enable circuit breakers **Security Incident:** - Rotate all credentials immediately - Isolate affected systems - Preserve logs and evidence - Engage security team / legal if data breach **Data Quality Issue:** - Halt automated outputs - Identify contamination window - Notify affected clients with timeline - Prepare correction batch ### 5. Communication Templates **Client notification (SEV1/2):** ``` Subject: [Service Name] — Incident Update We've identified an issue affecting [description]. - Impact: [what's affected] - Status: [investigating/identified/monitoring/resolved] - ETA: [estimated resolution time] - Workaround: [if available] We'll provide updates every [30 min / 1 hour]. ``` **Internal escalation:** ``` 🚨 SEV[X] — [Service]: [Brief description] Impact: [scope] Started: [time] Commander: [name] Channel: [link] Action needed: [specific ask] ``` ### 6. Recovery & Validation ```markdown □ Root cause identified and documented □ Fix deployed and verified □ All affected data corrected/reconciled □ Client communication sent (resolution) □ Monitoring confirms stable for 30+ min □ Incident timeline documented ``` ### 7. Post-Mortem Template ```markdown # Incident Post-Mortem: [Title] **Date:** YYYY-MM-DD **Severity:** SEV[X] **Duration:** [start] — [end] ([total time]) **Commander:** [name] ## Summary [2-3 sentence description] ## Timeline - HH:MM — [event] - HH:MM — [event] ## Root Cause [Technical root cause] ## Impact - Users affected: [number] - Duration: [time] - Data impact: [description] - Financial impact: [if applicable] ## What Went Well - [item] ## What Went Wrong - [item] ## Action Items | Action | Owner | Due Date | Status | |--------|-------|----------|--------| | [item] | [name] | [date] | Open | ## Lessons Learned - [lesson] ``` ## Best Practices - Test your incident response plan quarterly (tabletop exercises) - Keep runbooks next to the code they support - Automate detection — humans are slow at noticing things - Over-communicate during incidents — silence breeds anxiety - Blameless post-mortems — focus on systems, not people - Track MTTR (mean time to recover) as your north star metric --- *Need incident response built into your AI operations from day one? AfrexAI deploys production-grade AI agents with monitoring, alerting, and response plans included. Book a call: [calendly.com/cbeckford-afrexai/30min](https://calendly.com/cbeckford-afrexai/discovery-call)*


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