Introduction
Amazon Bedrock has emerged as a powerful foundation for building generative AI agents that can answer questions, draft documents, and automate workflows. The true value of these agents, however, lies in their ability to draw from reliable, up‑to‑date data sources that reflect an organization’s operational reality. In many enterprises, the data that feeds an agent is not confined to a single AWS account; instead, it is distributed across multiple accounts to satisfy security, compliance, and cost‑management requirements. When a Bedrock agent needs to query a knowledge base that lives in an Amazon Redshift cluster in a different account, the integration can become a complex puzzle involving network configuration, cross‑account permissions, and data residency constraints.
This post walks through a practical solution that enables Bedrock agents to access Redshift tables in other accounts without compromising security or performance. By leveraging AWS Resource Access Manager (RAM), AWS PrivateLink, and Bedrock’s built‑in data source connectors, we can create a seamless, auditable data path that respects account boundaries while delivering the low‑latency responses that users expect from conversational AI. The approach is designed to be repeatable, scalable, and compliant with common governance frameworks, making it a valuable pattern for any organization that wants to unlock the full potential of Bedrock across a multi‑account environment.
We will start by revisiting the architecture of Bedrock agents and the typical data‑access patterns they employ. Next, we’ll identify the pain points that arise when the knowledge base resides in another account. Then we’ll outline a step‑by‑step blueprint that ties together AWS services to bridge the accounts securely. Finally, we’ll discuss how to monitor, audit, and optimize the integration so that it remains robust as the data volume and query complexity grow.
By the end of this article, you will have a clear understanding of how to connect Bedrock agents to cross‑account Redshift knowledge bases, the security controls you should enforce, and the operational best practices that keep the system reliable and cost‑effective.
Main Content
Understanding Bedrock Agent Architecture
Bedrock agents are essentially orchestrated workflows that combine a large language model (LLM) with one or more data sources. The agent receives a user prompt, passes it to the LLM, and then, based on the LLM’s output, decides whether to query a database, retrieve a document, or perform a calculation. The data source connectors are the bridge between the agent’s logic and the underlying data store. For relational databases, Bedrock offers a native connector that translates SQL queries into API calls, allowing the LLM to ask questions like “What was the revenue for product X in Q3?” and receive a structured answer.
When the data source is an Amazon Redshift cluster, the connector relies on the cluster’s JDBC endpoint. In a single‑account scenario, the agent simply needs network access to that endpoint, and the connector handles authentication via IAM roles or database credentials. The challenge arises when the Redshift cluster is in a different account: the agent’s execution environment, which runs in the Bedrock account, cannot directly reach the cluster’s endpoint unless specific cross‑account networking and permissions are in place.
Challenges of Cross‑Account Data Access
The primary obstacles to cross‑account Redshift access are network isolation, IAM permissions, and data residency compliance. By default, Amazon Redshift clusters are launched in a VPC that restricts inbound traffic to the cluster’s security group. If the Bedrock agent runs in a different account, it will not be able to resolve the cluster’s private IP address unless a VPC peering or a PrivateLink endpoint is established. Even if network connectivity is achieved, the agent still needs to authenticate against the Redshift cluster. IAM roles cannot be assumed across accounts without explicit trust policies, and database credentials must be stored securely. Finally, many organizations have strict rules about where data can travel; moving data across accounts can violate those rules if not handled correctly.
These constraints mean that a naive approach—such as opening the Redshift endpoint to the public internet or using a shared credential file—would expose the data to unnecessary risk and potentially violate compliance requirements.
Architectural Blueprint for Cross‑Account Integration
A robust solution combines several AWS services to create a secure, auditable data path:
- AWS Resource Access Manager (RAM) – The account that owns the Redshift cluster shares the cluster’s VPC subnet and security group with the Bedrock account using RAM. This allows the Bedshift account to create a PrivateLink endpoint that points to the Redshift cluster without exposing the cluster to the public internet.
- AWS PrivateLink – In the Bedrock account, a VPC endpoint service is created that connects to the shared Redshift subnet. The Bedrock agent’s VPC can then attach an interface endpoint to this service, giving the agent a private IP address that can resolve to the Redshift cluster.
- IAM Cross‑Account Roles – The Bedrock account assumes a role in the Redshift account that has the necessary permissions to query the database. The role’s trust policy allows the Bedrock service principal to assume it, and the role’s permissions grant SELECT access to the relevant schemas and tables.
- Bedrock Data Source Connector Configuration – The connector is configured to use the PrivateLink endpoint as its JDBC URL and to present the assumed IAM role’s temporary credentials. The connector automatically signs the JDBC connection request with the role’s credentials, ensuring that only authorized queries are executed.
- Audit Logging – Amazon CloudTrail in the Redshift account records all API calls, while Amazon Redshift’s own query logging captures the SQL statements executed by the agent. These logs can be forwarded to Amazon S3 or Amazon CloudWatch Logs for long‑term retention and analysis.
By following this blueprint, the Bedrock agent can query the Redshift cluster as if it were in the same account, while all traffic remains within the AWS backbone and all access is governed by fine‑grained IAM policies.
Practical Implementation Steps
The implementation begins with the Redshift account owner creating a RAM resource share that includes the VPC subnet and security group associated with the cluster. The share is then accepted by the Bedrock account, which creates a PrivateLink interface endpoint that points to the shared subnet. Next, an IAM role is defined in the Redshift account with a policy that grants SELECT permissions on the target tables. The trust policy for this role allows the Bedrock service principal to assume it.
In the Bedrock account, the agent’s data source connector is configured with the JDBC URL that points to the PrivateLink endpoint. The connector is also set to use the IAM role’s temporary credentials, which are obtained by assuming the cross‑account role. Because the connector runs inside the Bedrock account’s VPC, it can resolve the PrivateLink endpoint’s DNS name to a private IP address that routes directly to the Redshift cluster.
Once the connector is operational, the agent can be tested by issuing a sample prompt that requires a database lookup. The agent’s logs will show the LLM’s decision to query the database, the SQL statement generated, and the response returned. If any step fails, the logs will reveal whether the issue lies in networking, IAM, or connector configuration.
Security and Governance Considerations
Security is paramount when exposing data across accounts. The use of PrivateLink ensures that all traffic stays within the AWS network, eliminating exposure to the public internet. IAM roles provide the principle of least privilege, allowing the agent to query only the tables it needs. The trust policy can be further tightened by restricting the role to a specific Bedrock service principal or by adding conditions that limit the role’s usage to certain IP ranges.
Governance is enforced through audit logging. CloudTrail records the assumption of the cross‑account role, while Redshift’s query logs capture the exact SQL statements executed. These logs can be integrated with AWS Security Hub or a SIEM solution to detect anomalous queries or unauthorized access attempts. Additionally, the use of AWS Config rules can monitor whether the RAM share and PrivateLink endpoint remain compliant with organizational policies.
Performance and Cost Implications
Because the data path remains within the AWS backbone, latency is typically low, especially when the Bedrock agent and the Redshift cluster are in the same region. However, cross‑region PrivateLink endpoints can introduce additional latency, so it is advisable to keep the Bedrock agent and the Redshift cluster in the same region whenever possible.
Cost considerations include the PrivateLink endpoint hourly charges and data transfer fees. Since the traffic remains private, data transfer costs are minimal compared to public internet transfers. The IAM role assumption incurs negligible cost, and the Bedrock service’s own usage fees are driven by the number of prompts and the size of the LLM model used.
By monitoring the query performance in Redshift and the response times in Bedrock, teams can identify bottlenecks and optimize indexes or query plans to reduce latency and cost.
Conclusion
Connecting Amazon Bedrock agents to knowledge bases that span multiple AWS accounts is a common requirement for modern enterprises that separate workloads for security, compliance, or cost reasons. The combination of AWS Resource Access Manager, PrivateLink, and cross‑account IAM roles provides a secure, auditable, and low‑latency pathway that allows Bedrock agents to query Amazon Redshift clusters as if they were in the same account. This architecture respects the principle of least privilege, keeps all traffic within the AWS network, and offers robust logging for compliance.
Implementing this pattern requires careful planning around networking, IAM, and logging, but the payoff is significant: agents can deliver richer, data‑driven responses without compromising security or governance. As Bedrock continues to evolve, the ability to seamlessly integrate with diverse data sources across accounts will become an essential capability for organizations that want to harness the full power of generative AI.
Call to Action
If you’re ready to unlock the potential of Bedrock agents in a multi‑account environment, start by mapping out the accounts that house your critical data and the Bedrock instances that will consume it. Use the architectural blueprint outlined above to set up a secure, private data path with RAM and PrivateLink, and then test the integration with a simple query. Monitor the logs to ensure compliance, and iterate on IAM policies to enforce least privilege. Once you’re comfortable, expand the integration to additional data sources such as S3, DynamoDB, or external APIs, and explore advanced use cases like real‑time analytics or automated reporting. By embracing this approach, you’ll build a foundation that scales with your organization’s data needs while keeping security and governance at the forefront.
