Securing AI Systems Architecture
Architect, implement, and maintain robust security controls for your AI infrastructure and machine learning models
Architecture
Implementation
Security Framework Principles
Preventive Security
Implement access controls, encryption, and vulnerability mitigation before deployment
- Role-based access controls
- Data encryption at rest & in transit
- Threat modeling for AI systems
Reactive Security
Continuous monitoring, threat detection, and incident response for dynamic environments
- Anomaly detection systems
- Model protection frameworks
- Incident response playbooks
Implementation Requirements
Must-Have Components
Data Validation & Sanitization
Ensure all input data meets quality and security standards before training
Model Protection Controls
Implement watermarking, tamper detection, and anti-stealing measures
Audit Trail Systems
Comprehensive logging of model inputs, predictions, and decisions
Threat Response Engine
Orchestrate security playbooks using threat intelligence models
Recommended Framework Tools
MLSecOps
Security operations framework for machine learning systems monitoring and response
Foolbox
Adversarial example library for testing model robustness
Prometheus
Real-time security monitoring and alerting for AI systems
Common Security Questions
How secure is my model against adversarial attacks?
Use security testing frameworks to validate model robustness against evasion, poisoning, and extraction attacks by using:
- Adversarial example libraries (Foolbox, CleverHans)
- Model watermarking techniques
- Robustness training protocols
What compliance standards must I follow?
Implement security controls per:
- NIST SP 800-190 (Model Security Framework)
- ISO 27001 for information security
- AI Ethical Standards (ENISA/ISO 23894)