AI Best Practices Checklist

🎯 Purpose & Scope

Before You Deploy

• Define clear business objectives for AI implementation
• Identify specific use cases and success metrics
• Assess current data readiness and quality
• Evaluate budget and resource requirements
• Determine compliance requirements (GDPR, HIPAA, SOC 2, etc.)

🔐 Security & Access Control

Authentication & Authorization

• Implement role-based access control (RBAC)
• Use strong authentication (MFA/SSO)
• Define least-privilege access policies
• Audit and rotate API keys regularly
• Monitor access logs for anomalies

Data Protection

• Encrypt data at rest and in transit
• Implement data retention and deletion policies
• Use secure storage for sensitive information
• Establish data classification levels
• Create backup and disaster recovery procedures

🛡️ Agent Supervision & Governance

Guardrails & Boundaries

• Define explicit action boundaries for AI agents
• Implement approval workflows for high-risk actions
• Set spending limits and rate limits
• Create escalation procedures for edge cases
• Document prohibited actions clearly

Monitoring & Oversight

• Log all AI agent actions and decisions
• Set up real-time alerting for anomalies
• Review agent behavior regularly
• Track accuracy and error rates
• Monitor for bias and fairness issues

Human-in-the-Loop

• Require human approval for sensitive operations
• Establish clear escalation paths
• Train staff on AI oversight procedures
• Create feedback mechanisms
• Plan for graceful degradation (fallback to humans)

📊 Data Quality & Training

Data Preparation

• Audit training data for bias and completeness
• Validate data sources and provenance
• Clean and normalize datasets
• Test with representative samples
• Document data processing steps

Model Selection & Testing

• Choose appropriate models for use cases
• Test accuracy across diverse scenarios
• Validate against edge cases
• Benchmark against alternatives
• Document model limitations

🔄 Continuous Improvement

Performance Monitoring

• Track key performance indicators (KPIs)
• Monitor response times and latency
• Measure user satisfaction
• Analyze cost per transaction
• Review uptime and reliability

Iteration & Updates

• Schedule regular model retraining
• Update based on new data and feedback
• Version control all changes
• Test updates in staging environment
• Communicate changes to stakeholders

📋 Compliance & Documentation

Regulatory Compliance

• Map AI use to applicable regulations
• Implement required consent mechanisms
• Create data processing agreements
• Prepare for audits and inspections
• Stay current with changing regulations

Documentation

• Maintain system architecture diagrams
• Document data flows and integrations
• Create runbooks for common scenarios
• Write user guides and training materials
• Keep incident response plans updated

🚨 Risk Management

Threat Assessment

• Identify potential attack vectors
• Assess impact of system failures
• Evaluate reputational risks
• Consider adversarial manipulation risks
• Plan for worst-case scenarios

Mitigation Strategies

• Implement input validation and sanitization
• Use prompt injection defenses
• Set up anomaly detection
• Create incident response procedures
• Maintain insurance coverage if applicable

👥 Team & Training

Staff Readiness

• Train teams on AI capabilities and limitations
• Establish AI ethics guidelines
• Define roles and responsibilities
• Create communication protocols
• Foster a culture of responsible AI use

External Partners

• Vet AI vendors and service providers
• Review third-party compliance certifications
• Establish SLAs and support agreements
• Define data ownership and usage rights
• Plan for vendor lock-in or migration