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Checklist: AI Infrastructure Audit

Use this checklist when evaluating your current AI infrastructure before scaling, after a production incident, or during quarterly architecture reviews. It translates the five infrastructure failure patterns from Chapter 4 into concrete items you can verify, supplemented with checks for observability, cost management, security, and reliability.

Architecture Review

Mistake 1: Over-Engineering Early

  • Every infrastructure component maps to a specific, current problem (not a future "we'll need it when we scale" scenario)
  • You are using off-the-shelf solutions where they meet your needs at current scale
  • No custom-built systems exist for problems that managed services already solve
  • Your team can ship an AI feature in days, not months of infrastructure setup
  • Infrastructure costs are proportional to the value being delivered today
  • You haven't built multi-region or multi-cluster deployments before validating in a single market

Mistake 2: Single Points of Failure

  • You can name your fallback plan if your primary AI provider goes down for 4+ hours
  • Every AI feature has a defined degradation mode (e.g., chat agents fall back to simpler models)
  • Critical paths have at least one alternative provider configured
  • An abstraction layer exists that allows swapping providers in hours, not weeks
  • Failover paths are tested at least quarterly
  • No single cloud provider or datacenter failure can take down your entire AI capability

Mistake 3: No Observability

  • You can answer "why did this AI request fail?" for any request in the past 24 hours
  • Structured logging is in place for every AI call from day one
  • You can distinguish AI errors from system errors in your logs
  • AI output quality is tracked with measurable metrics, not anecdotal reports
  • You know this week's AI costs by feature without opening a spreadsheet

Mistake 4: Ignoring Cost Signals

  • Cost alerts are configured at 50%, 80%, and 100% of daily budgets
  • Weekly cost reviews are scheduled and happening
  • Every AI feature has an assigned cost target
  • Per-feature cost attribution is in place so you know which capabilities are burning cash
  • You find out about cost spikes within hours, not at month-end
  • You have modeled how costs change at 2x, 5x, and 10x current usage

Mistake 5: Security as an Afterthought

  • A compromised agent credential can't cause a production data breach
  • Each AI agent runs under its own service account with least-privilege permissions
  • System prompts and administrative controls have change logging and audit trails
  • Environment isolation separates development, staging, and production AI systems
  • Agent permissions go through the same approval process as human access requests
  • Quarterly access reviews are scheduled for all AI service accounts

Observability

  • Latency, throughput, and error rates are tracked per AI endpoint
  • Token usage and model response times are logged per request
  • Dashboards exist showing AI system health at a glance
  • Alerts fire automatically when AI output quality degrades beyond defined thresholds
  • Log retention policies are defined and meet your compliance requirements
  • You can trace an end-user request through every AI component it touches

Cost Management

  • Monthly AI spend is tracked and trended over at least the past 3 months
  • You know your cost-per-inference for each model and feature
  • Budget forecasts account for non-linear cost scaling at usage thresholds
  • Unused or underutilized AI resources are identified and reviewed monthly
  • Model selection decisions include cost analysis, not just performance benchmarks
  • You have a defined process for what happens when a feature exceeds its cost target

Security

  • AI inputs are validated and sanitized to prevent prompt injection
  • PII and sensitive data are masked or excluded from AI model inputs where required
  • AI outputs are checked before being presented to users in sensitive contexts
  • Network segmentation isolates AI inference services from core data stores
  • Third-party AI provider data handling agreements are reviewed and signed
  • Incident response procedures exist specifically for AI-related security events

Reliability

  • SLAs are defined for AI-powered features (latency, availability, accuracy)
  • Rate limiting and circuit breakers protect AI endpoints from cascading failures
  • Graceful degradation paths are documented and tested for each AI feature
  • Recovery time objectives (RTO) are defined for AI system outages
  • Load testing has been performed at expected peak traffic levels
  • Rollback procedures exist for AI model updates and configuration changes

Source framework: The 5 Infrastructure Mistakes That Kill AI Initiatives

Full chapter: Chapter 4: Infrastructure for AI-First Operations