AI Safety Checklist

Comprehensive Safety Evaluation for AI Systems

📋 Pre-Deployment Safety Checks

Hallucination Testing
Verify accuracy of AI outputs against ground truth data. Test edge cases and ambiguous inputs.
Prompt Injection Defense
Test system against adversarial prompts, jailbreak attempts, and manipulation tactics.
Data Privacy Protection
Ensure no PHI/PII leakage. Verify data handling complies with HIPAA, GDPR, or relevant regulations.
Bias & Fairness Audit
Test for demographic bias, protected class discrimination, and outcome fairness.
RAG Grounding Validation
Verify retrieval accuracy, source attribution, and response grounding in retrieved context.

🔒 Security & Compliance

Access Control Testing
Verify role-based permissions, authentication mechanisms, and authorization boundaries.
Audit Logging
Ensure all AI decisions are logged with timestamps, inputs, outputs, and confidence scores.
Model Documentation
Complete System Card and Model Card documenting architecture, training data, limitations.
Compliance Validation
Verify alignment with EO 14110, NIST AI RMF, SR 11-7 (MRM), or industry regulations.

⚡ Performance & Reliability

Latency Testing
Measure response times under normal and peak load conditions.
Error Rate Analysis
Establish baseline error rates and acceptable thresholds for production.
Failure Mode Analysis
Identify and document potential failure scenarios and mitigation strategies.
Monitoring Setup
Implement continuous monitoring for drift, performance degradation, and anomalies.

🎯 Deployment Readiness

Stakeholder Approval
Obtain sign-off from security, compliance, legal, and business stakeholders.
Rollback Plan
Document procedures for reverting to previous version if issues arise.
Incident Response Plan
Establish escalation procedures for safety incidents, failures, or attacks.
User Training
Provide documentation and training on proper AI system usage and limitations.

BeaconShield Labs
AI Safety, Red Teaming & Model Assurance
beaconshieldlabs.com | [email protected]