Harnessing Amazon Bedrock Cross-Region Inference for Scalable Generative AI Applications
Achieving High Throughput and Compliance with Cross-Region Inference Profiles
Key Concepts and Operational Workloads in Generative AI
Exploring Security Considerations and Best Practices
Types of Cross-Region Inference Profiles: Geographic vs. Global
Understanding IAM Permission Requirements and SCP Considerations
Auditing and Monitoring Cross-Region Inference Requests
Implementing Effective Security Controls with AWS Control Tower
AWS Regions Enablement: Streamlining Operations Across Regions
Conclusion: Safely Innovating with Cross-Region Inference
Additional Resources for Further Exploration
Meet the Authors: Expertise Behind the Insights
Scaling Generative AI Applications with Amazon Bedrock: A Focus on Cross-Region Inference
The adoption of generative AI in commercial settings has seen exponential growth. Organizations are increasingly integrating AI capabilities into their operational workloads, with emphasis on scale and reliability. Amazon Bedrock has stepped up to support this trend by introducing cross-Region inference (CRIS) profiles. This powerful feature allows organizations to seamlessly distribute inference processing across multiple AWS regions, optimizing performance and ensuring applications remain responsive under heavy load.
In this blog post, we’ll delve into the security considerations and best practices for implementing Amazon Bedrock’s CRIS profiles, guiding you toward a secure architecture that meets compliance requirements.
Understanding Cross-Region Inference Profiles
Cross-Region inference operates on two key concepts:
- Source Region: The region from which the API request originates.
- Destination Region: The region to which Amazon Bedrock routes the request for inference.
When invoking a CRIS in Amazon Bedrock, your request follows an intelligent routing path. Your API call from the source Region is automatically routed to defined destination Regions. This process occurs over the secure AWS network with end-to-end encryption, ensuring data integrity during transit.
It’s important to note that while inference data may traverse multiple Regions, no customer data is stored in any destination Region. All customer-managed logs, knowledge bases, and stored configurations remain within the source Region.
Types of Cross-Region Inference Profiles
Amazon Bedrock offers two types of CRIS profiles:
-
Geographic Cross-Region Inference: This profile automatically selects the optimal Region within a defined geography (e.g., US, EU, Australia) to process your inference requests. Geographic profiles help organizations adhere to regional data residency requirements.
-
Global Cross-Region Inference: This profile routes inference requests to supported commercial Regions worldwide, optimizing resources and enabling higher throughput without geographic restrictions.
Security Considerations
As organizations begin to leverage CRIS, understanding the associated security implications is paramount. You must evaluate the fit of cross-Region inference with your compliance requirements, particularly for organizations with strict data residency policies.
IAM Permission Requirements and Service Control Policies (SCPs)
Establishing secure access to Amazon Bedrock’s resources requires careful configuration of IAM policies and SCPs.
For both Geographic and Global CRIS:
- Users and roles within your AWS account need specific IAM permissions to create, modify, or use Amazon Bedrock resources.
- SCPs provide organization-wide controls, ensuring that required actions are permissible.
Handling Security for Geographic and Global CRIS
| Inference Type | Key Advantages | Use Cases | IAM Requirements | SCP Requirements |
|---|---|---|---|---|
| Geographic CRIS | Data residency | Customers needing to keep all data processing within specific geographic boundaries (e.g., US, EU, AU) | Access to geo-specific inference profile and relevant models | Allow access to all defined destination Regions |
| Global CRIS | Higher throughput | Users wanting broader coverage and higher throughput at a lower cost | Access to global inference profile and relevant models | Must not block "unspecified" in Region list |
- These tables summarize how different types of CRIS align with security and operational requirements.
Implementing IAM Policies
Geographic CRIS
To enable a user or role to utilize a Geographic cross-Region inference profile, implement the following IAM policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "GrantGeoCrisInferenceProfileAccess",
"Effect": "Allow",
"Action": "bedrock:InvokeModel",
"Resource": [
"arn:aws:bedrock:us-east-1::inference-profile/us.anthropic.claude-sonnet-4-5-20250929-v1:0"
]
},
{
"Sid": "GrantGeoCrisModelAccess",
"Effect": "Allow",
"Action": "bedrock:InvokeModel",
"Resource": [
"arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-sonnet-4-5-20250929-v1:0",
"arn:aws:bedrock:us-east-2::foundation-model/anthropic.claude-sonnet-4-5-20250929-v1:0",
"arn:aws:bedrock:us-west-2::foundation-model/anthropic.claude-sonnet-4-5-20250929-v1:0"
],
"Condition": {
"StringEquals": {
"bedrock:InferenceProfileArn": "arn:aws:bedrock:us-east-1::inference-profile/us.anthropic.claude-sonnet-4-5-20250929-v1:0"
}
}
}
]
}Global CRIS
For Global cross-Region inference, the IAM policy must include:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "GrantGlobalCrisInferenceProfileRegionAccess",
"Effect": "Allow",
"Action": "bedrock:InvokeModel",
"Resource": [
"arn:aws:bedrock:us-east-1::inference-profile/global.anthropic.claude-sonnet-4-5-20250929-v1:0"
]
},
{
"Sid": "GrantGlobalCrisInferenceProfileGlobalModelAccess",
"Effect": "Allow",
"Action": "bedrock:InvokeModel",
"Resource": [
"arn:aws:bedrock:::foundation-model/anthropic.claude-sonnet-4-5-20250929-v1:0"
],
"Condition": {
"StringEquals": {
"aws:RequestedRegion": "unspecified"
}
}
}
]
}Monitoring and Auditing
AWS CloudTrail logs all cross-Region calls, providing insights through an additionalEventData field that traces requests effectively. Proper logging ensures that any unusual activities can be identified swiftly.
Conclusion
Amazon Bedrock’s cross-Region inference enables organizations to scale their generative AI applications effectively while maintaining security and compliance. By understanding the intricate relationship between CRIS capabilities and necessary security controls, businesses can leverage these advanced features without compromising their governance stance. Clear IAM policies, careful SCP configuration, and continuous monitoring will empower teams to innovate confidently.
Additional Resources
For a deeper dive, refer to the official Amazon Bedrock documentation.
About the Authors
- Zohreh Norouzi: A Security Solutions Architect at AWS, committed to guiding customers toward secure cloud solutions, especially in generative AI.
- Satveer Khurpa: A Senior Specialist Solutions Architect at Amazon Bedrock, adept at crafting innovative AI solutions.
- Melanie Li, PhD: A Generative AI Specialist Solutions Architect focused on harnessing cutting-edge AI tools.
- Saurabh Trikande: A Senior Product Manager passionate about democratizing AI and simplifying inference deployment.
- Jan Catarata & Harlan Verthein: Engineers specializing in crafting robust distributed systems and enhancing cross-region performance.
By implementing these best practices and leveraging the capabilities of Amazon Bedrock, organizations can unlock new possibilities in generative AI while maintaining a secure and compliant infrastructure.