AI Cloud Security Specialist Training

Curriculum:

Module 1: Introduction to AI Cloud Security

• Evolution of cloud security in the age of AI
• Unique challenges in securing AI/ML workloads
• AI governance frameworks and compliance (GDPR, HIPAA, NIST AI RMF)
• Shared responsibility model for AI in the cloud

Module 2: AI/ML Workload Security
Fundamentals

• Securing AI pipelines: data ingestion, training, and inference
• Protecting training data from poisoning and leakage
• Model integrity: preventing adversarial ML attacks
• Secure deployment of AI/ML models in cloud environments

Module 3: Identity and Access Management (IAM) for AI

• Role-based and attribute-based access control for AI systems
• Managing credentials, API keys, and secrets for ML pipelines
• Multi-cloud IAM strategies for AI applications
• Hands-on lab: IAM setup for AI workloads on AWS/Azure

Module 4: Data Security in AI-driven Cloud Environments

• Encryption techniques (at rest, in transit, and in use)
• Tokenization and data anonymization in AI training datasets
• Securing sensitive AI/ML datasets (financial, healthcare, PII)
• Data sovereignty and cross-border compliance

Module 5: Threat Detection with AI

• AI-powered Security Information and Event Management (SIEM)
• Machine learning for anomaly detection and insider threat monitoring
• Integration with SOAR for automated cloud response
• Case study: AI-driven cloud incident detection

Module 6: Securing Multi-Cloud AI Applications

• Security best practices in AWS SageMaker, Azure ML, and GCP AI Platform
• Container and Kubernetes security for AI workloads
• CI/CD security for ML Ops pipelines
• Hands-on lab: Deploying a secure ML model on Kubernetes

Module 7: Advanced AI Cloud Security Threats

• Adversarial attacks on AI models (evasion, poisoning, inference attacks)
• Backdoor attacks and model theft in cloud environments
• Red teaming AI/ML models for vulnerabilities
• Defense mechanisms: adversarial training and monitoring

Module 8: AI Governance, Compliance, and Risk Management

• AI ethics, governance, and compliance frameworks
• Risk assessment for AI/ML projects in the cloud
• Implementing audit trails and explainability (XAI) in AI systems
• Case study: Governance in financial AI cloud applications

Module 9: Incident Response and Forensics for AI Cloud Security

• Incident response lifecycle for AI security breaches
• Forensic analysis of compromised AI/ML pipelines
• Automated remediation using AI-driven cloud security tools
• Lab: Simulated AI model attack and response