How to Secure Bedrock Agent-Initiated Actions in the Cloud

Read full article here: https://goteleport.com/blog/4-ways-to-secure-bedrock-agent-actions/?utm_source=nhimg

The Rise of Agentic AI in AWS

AI agents powered by Amazon Bedrock are rapidly becoming integral to cloud operations. They interact directly with core AWS services, like Lambda, S3, RDS, and EC2, to automate support tasks, run training pipelines, and even provision infrastructure.

Most organizations rely on the Model Context Protocol (MCP) to translate natural language into structured agent actions. But there’s a catch: MCP doesn’t validate who is making the request or whether it should be allowed. Without security guardrails, AI agents risk gaining over-privileged access, exposing sensitive data, and bypassing enterprise policies.

This is where Teleport comes in. By binding identity to every agent action, enforcing just-in-time (JIT) access, and logging every session, Teleport ensures that Bedrock agent activity is not only powerful but also secure and auditable.

Why Identity is the Foundation

Sharing static credentials between MCP servers and agents creates a blind spot. If all agents use the same key, there’s no way to distinguish actions, limit scope, or revoke access cleanly.

Teleport solves this by:

• Assigning unique identities to each MCP server and AI agent.
• Using x.509 certificates and IAM roles for secure, short-lived access.
• Enforcing least privilege policies tied to tasks, not permanent credentials.
• Logging every action for audit and compliance.

With this model, every agent action becomes traceable, controlled, and revocable—just like access for humans.

4 Ways Teleport Secures Bedrock AI Agent Actions

1- Safe Lambda Invocation for Support Agents

Support agents often trigger Lambda functions for tasks like password resets. Without policy enforcement, these agents could invoke any function, creating risk.

Solution with Teleport:

• Limit agent access to specific Lambda ARNs.
• Grant short-lived tokens scoped only to approved functions.
• Enforce per-task approval and logging.

Result: Agents invoke only the Lambdas they’re authorized for—nothing more.

2- Just-in-Time EC2 Access for Remediation Agents

Incident response agents may need to connect to EC2 instances during emergencies. Granting blanket access introduces risk of lateral movement across environments.

Solution with Teleport:

• Define roles with tag-based policies (e.g., env=prod, purpose=remediation).
• Require agents to request temporary sessions for specific nodes.
• Enforce audit trails for every diagnostic or fix.

Result: EC2 access is time-bound, scoped, and fully auditable.

3- Controlled S3 Access for Training Pipelines

AI training agents often need large datasets stored in S3. Long-lived credentials risk exposing unrelated or sensitive data.

Solution with Teleport:

• Restrict S3 access to specific prefixes and read-only actions.
• Issue short-lived certificates tied to training jobs.
• Log all S3 interactions for compliance.

 Result: Training agents access only the data they need—no sprawl, no leakage.

4- Secure RDS Access for Finance Agents

Finance agents generating monthly reports require access to RDS. With static credentials, there’s no way to enforce session-level security or track queries per agent.

Solution with Teleport:

• Assign unique agent IDs tied to reporting tasks.
• Allow read-only, time-limited sessions for specific databases.
• Enforce MFA and per-query logging.

 Result: Every query is identity-traceable, temporary, and fully policy-controlled.

Why This Matters for AI Innovation

AI agents are not just automation tools—they’re active participants in enterprise operations. Without identity-driven security, organizations risk creating a shadow ecosystem of uncontrolled agents.

By combining Amazon Bedrock, MCP, and Teleport, enterprises can:

• Adopt AI-driven automation with confidence.
• Ensure every action is auditable and compliant.
• Scale AI adoption while keeping security debt in check.

With Teleport, every agent session has a purpose, every action has a boundary, and every request is logged. This is how organizations can embrace AI innovation without losing control.