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AI Security Checklist
Ship AI without breaking trust.
A practical checklist for teams building with AI. Each section links to deeper reading from real production experience.
Authentication & Access Control
- ☐API keys stored in secrets manager, never in code or env files
- ☐All AI endpoints require authentication — no open inference APIs
- ☐Role-based access to model admin, training data, and prompt configs
- ☐API key rotation policy in place and automated
Data Handling & Privacy
- ☐PII stripped or masked before reaching the model
- ☐Audit trail for all data sent to and received from AI services
- ☐Data retention policy for prompts, completions, and embeddings
- ☐Third-party AI providers reviewed for data handling compliance
Model Governance & Guardrails
- ☐Output validation layer between model and user-facing response
- ☐Content filtering for harmful, biased, or off-topic outputs
- ☐Fallback behavior defined for when the model fails or hallucinates
- ☐Prompt injection defenses in place for user-facing inputs
Monitoring & Incident Response
- ☐Logging for all AI interactions (inputs, outputs, latency, errors)
- ☐Alerting on anomalous usage patterns or cost spikes
- ☐Incident response plan that covers AI-specific failure modes
- ☐Regular review of model outputs for drift or degradation
Supply Chain & Dependencies
- ☐AI libraries and SDKs pinned to reviewed versions
- ☐Third-party models evaluated for known vulnerabilities
- ☐Self-hosted models isolated from production networks where possible
- ☐Dependency scanning includes AI/ML-specific packages
Deployment & Operations
- ☐AI services deployed behind rate limiting and abuse prevention
- ☐Cost controls and budget alerts on AI API usage
- ☐Staging environment for testing prompt and model changes
- ☐Rollback plan for model updates and prompt engineering changes
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