Security and Compliance for ML Systems
Description
Comprehensive security framework, compliance requirements, and data protection strategies for machine learning systems on AWS.
Learning Objectives
Learners will master security best practices for ML systems including data encryption, access control, network security, and compliance frameworks. They will understand identity and access management, data privacy protection, audit trail management, and regulatory compliance requirements. Students will learn to implement secure ML pipelines, data governance, and risk management strategies for enterprise ML deployments.
Topics (10)
Advanced IAM implementation including service roles, cross-account access, fine-grained permissions, and access control strategies for ML workflows and data.
Comprehensive encryption implementation including KMS key management, S3 encryption, EBS encryption, and end-to-end data protection for ML workflows.
Advanced network security including VPC design, private subnets, security groups, NACLs, VPC endpoints, and secure connectivity patterns for ML workloads.
Comprehensive compliance coverage including regulatory requirements, audit preparation, documentation standards, and compliance automation for ML systems.
Advanced privacy protection including data anonymization, differential privacy, federated learning, and privacy-preserving analytics for ML applications.
Comprehensive secure pipeline design including secret management, secure data transfer, container security, and secure CI/CD practices for ML workflows.
Advanced audit implementation including CloudTrail configuration, log aggregation, security monitoring, and compliance reporting for ML systems.
Comprehensive threat analysis including ML-specific threats, adversarial attacks, data poisoning, model stealing, and comprehensive risk mitigation strategies.
Advanced model security including model watermarking, secure model deployment, IP protection strategies, and secure model collaboration frameworks.
Comprehensive security operations including security monitoring setup, incident detection, response procedures, and recovery planning for ML system security incidents.