AWS AI Security Best Practices
Comprehensive security guidance for deploying and managing AI workloads on Amazon Web Services, covering SageMaker, Bedrock, and other AWS AI services.
SageMaker Security
- • VPC isolation and network controls
- • IAM roles and permissions
- • Encryption at rest and in transit
- • Model registry access control
Bedrock Security
- • API access controls
- • Data residency compliance
- • Model invocation logging
- • Content filtering policies
Data Protection
- • S3 bucket encryption
- • KMS key management
- • Data classification
- • Access logging and monitoring
Security Architecture
Network Isolation
Deploy AI workloads in private VPCs with strict network segmentation
- • Use VPC endpoints for AWS service access
- • Implement security groups and NACLs
- • Enable VPC Flow Logs for monitoring
- • Use PrivateLink for secure connectivity
Identity and Access Management
Implement least privilege access with fine-grained IAM policies
- • Use IAM roles instead of access keys
- • Implement service control policies (SCPs)
- • Enable MFA for sensitive operations
- • Regular access reviews and audits
Monitoring and Compliance
Comprehensive logging and monitoring for AI workloads
- • CloudTrail for API activity logging
- • CloudWatch for metrics and alarms
- • AWS Config for compliance tracking
- • Security Hub for centralized findings