Secure AI Deployment Checklist & Controls
Security controls, architecture patterns, and deployment checklist for shipping AI systems to production — covering access controls, monitoring, data protection, and incident response.
Secure AI Deployment — Security Controls for Production AI
Security controls checklist, architecture guidance, and continuous monitoring configuration for secure AI deployment — covering data protection, access control, runtime monitoring, and AI incident response.
- Pre-deployment security review checklist covering authentication, authorisation, and data handling
- Input/output filtering implementation and model endpoint access control hardening
- Data classification and protection controls for AI-processed data
- AI-specific monitoring configuration — injection, anomaly, leakage, and behaviour drift alerts
- AI incident response playbook creation for common AI failure and exploitation scenarios
- Continuous security regression and periodic adversarial red team scheduling
Security Is 10x Cheaper Before Deployment
Implementing security controls pre-deployment avoids emergency hotfix releases, regulatory breach notifications, and incident response costs — the economics of AI security mirror traditional secure development.
AI Systems Need AI-Specific Monitoring
Standard APM and SIEM tools do not detect prompt injection, model behaviour drift, or data leakage via LLM responses. AI-specific monitoring rules are required from day one.
Incident Response Must Be Planned Before It Is Needed
AI incidents require different response procedures than network intrusions or web application breaches — playbooks must be designed before an AI incident forces improvisation.