Set up Bedrock guardrails

This guide shows you how to configure AWS Bedrock Guardrails with AI Studio. After completing this setup, AI Studio can use your Bedrock guardrails to filter content, detect PII, and enforce topic policies across your AI agents. For an overview of how guardrails work in AI Studio, see Configure guardrails.

Prerequisites

Before configuring Bedrock guardrails in AI Studio, you need:

An AWS account with access to Amazon Bedrock
A Bedrock guardrail created in the AWS console
AWS credentials with permission to invoke the guardrail
Enterprise plan access to AI Studio

Create a Bedrock guardrail in AWS

If you haven’t created a Bedrock guardrail yet, follow these steps in the AWS console:

Navigate to Amazon Bedrock > Guardrails in the AWS console
Select Create guardrail
Configure your guardrail policies:
- Content filters: Block harmful content categories (hate, insults, sexual, violence)
- Denied topics: Define custom topics to block
- Word filters: Block specific words or phrases
- Sensitive information filters: Detect and block PII types
- Contextual grounding: Check for hallucinations and relevance
Save your guardrail and note the Guardrail ID and Version

For detailed instructions, see Create a guardrail in the AWS documentation.

Configuration parameters

Configure these parameters when adding a Bedrock guardrail in AI Studio:

Required parameters

Parameter	Description
`guardrailIdentifier`	The unique ID of your Bedrock guardrail (found in the AWS console)
`guardrailVersion`	The version of your guardrail (`DRAFT` or a version number like `1`, `2`)
`aws_region_name`	AWS region where your guardrail is deployed. Define the guardrail in the same region where your inference requests are made. See supported regions in the AWS documentation.
`aws_access_key_id`	AWS access key ID for authentication
`aws_secret_access_key`	AWS secret access key for authentication

Optional parameters

Parameter	Description
`disable_exception_on_block`	When `true`, returns a modified response instead of raising an exception when content is blocked. Useful for chat interfaces where exceptions may disrupt the conversation flow. Default: `false`
`aws_session_token`	Session token for temporary AWS credentials (required when using STS)
`aws_session_name`	Name for the AWS session
`aws_profile_name`	AWS profile name for credential retrieval from `~/.aws/credentials`
`aws_role_name`	IAM role name for cross-account access or role assumption
`aws_web_identity_token`	Web identity token for OIDC-based authentication
`aws_sts_endpoint`	Custom AWS STS endpoint URL
`aws_bedrock_runtime_endpoint`	Custom Bedrock runtime endpoint URL

AWS authentication options

AI Studio supports multiple AWS authentication methods for Bedrock guardrails.

Access keys (recommended for getting started)

Use IAM user access keys for straightforward authentication:

aws_access_key_id: AKIAIOSFODNN7EXAMPLE
aws_secret_access_key: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
aws_region_name: us-east-1

Temporary credentials (STS)

Use temporary credentials from AWS Security Token Service:

aws_access_key_id: ASIAXXX...
aws_secret_access_key: xxx...
aws_session_token: FwoGZXIvYXdzEBY...
aws_region_name: us-east-1

IAM role assumption

Assume an IAM role for cross-account access or elevated permissions:

aws_access_key_id: AKIAIOSFODNN7EXAMPLE
aws_secret_access_key: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
aws_role_name: bedrock-guardrail-role
aws_region_name: us-east-1

Web identity (OIDC)

Use OIDC tokens for container-based or Kubernetes deployments:

aws_web_identity_token: eyJhbGciOiJSUzI1NiIs...
aws_role_name: bedrock-guardrail-role
aws_region_name: us-east-1

Required IAM permissions

The AWS credentials you provide must have permission to invoke your Bedrock guardrail. Create an IAM policy with the following permissions:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["bedrock:ApplyGuardrail"],
      "Resource": "arn:aws:bedrock:*:*:guardrail/*"
    }
  ]
}

For production environments, scope the resource ARN to your specific guardrail:

{
  "Resource": "arn:aws:bedrock:us-east-1:123456789012:guardrail/abc123def456"
}

Guardrail versioning

Bedrock guardrails support versioning, allowing you to test changes before applying them to production:

Version value	Behavior
`DRAFT`	Uses the current draft version (for testing)
`1`, `2`, etc.	Uses a specific published version

Recommended workflow:

Test guardrail changes using DRAFT version in a development environment
Publish a new version in the AWS console when satisfied
Update production AI Studio configuration to use the new version number

Bedrock guardrail capabilities

AWS Bedrock Guardrails provide several content filtering capabilities:

Content filters

Block content based on harmful categories with configurable thresholds:

Hate: Discriminatory or prejudiced content
Insults: Demeaning or offensive language
Sexual: Sexually explicit content
Violence: Violent or threatening content
Misconduct: Content promoting illegal activities
Prompt attacks: Attempts to manipulate the model

Denied topics

Define custom topics that should be blocked. Useful for:

Preventing discussion of competitors
Blocking off-topic conversations
Enforcing industry-specific restrictions

Sensitive information filters

Detect and block PII types including:

Names, addresses, phone numbers
Email addresses, URLs
Credit card numbers, bank accounts
Social Security numbers (US)
Driver’s license numbers
Passport numbers

Word filters

Block specific words, phrases, or patterns. Supports:

Exact word matching
Profanity filters
Custom blocked terms

Error handling

When Bedrock blocks content, AI Studio returns an error to the agent. The default behavior raises an exception that halts the request. Set disable_exception_on_block: true to return a modified response instead of raising an exception. This is useful for:

Chat interfaces where exceptions disrupt the conversation
Applications that need to handle blocks gracefully
Scenarios where you want to show a custom message to users

Troubleshooting

Common errors

Error	Cause	Solution
`AccessDeniedException`	Missing IAM permissions	Verify your IAM policy includes `bedrock:ApplyGuardrail`
`ResourceNotFoundException`	Invalid guardrail ID or version	Check the guardrail ID and version in the AWS console
`ValidationException`	Invalid parameters	Verify region name and other parameters are correct
`ThrottlingException`	Rate limit exceeded	Implement retry logic or request a quota increase

Verify your guardrail

Test your guardrail directly in the AWS console before configuring it in AI Studio:

Navigate to your guardrail in the AWS Bedrock console
Select Test to open the testing interface
Enter sample content that should trigger the guardrail
Verify the guardrail blocks or allows content as expected

Next steps

Configure guardrails: Learn about guardrail modes and scoping
Usage and spend: Monitor guardrail activity and usage
AWS Bedrock Guardrails documentation: Detailed AWS documentation
Create a guardrail in AWS: Step-by-step AWS console instructions
AWS Bedrock pricing: Understand Bedrock guardrail costs

Getting started

Monitor and manage

Core concepts

Models and pricing

Connect to your data

Chat completions

No-code agents

Knowledge Graphs

Tool calling

Additional capabilities

Integrations

Security and compliance

Resources

Prerequisites

Create a Bedrock guardrail in AWS

Configuration parameters

Required parameters

Optional parameters

AWS authentication options

Access keys (recommended for getting started)

Temporary credentials (STS)

IAM role assumption

Web identity (OIDC)

Required IAM permissions

Guardrail versioning

Bedrock guardrail capabilities

Content filters

Denied topics

Sensitive information filters

Word filters

Error handling

Troubleshooting

Common errors

Verify your guardrail

Next steps

​Prerequisites

​Create a Bedrock guardrail in AWS

​Configuration parameters

​Required parameters

​Optional parameters

​AWS authentication options

​Access keys (recommended for getting started)

​Temporary credentials (STS)

​IAM role assumption

​Web identity (OIDC)

​Required IAM permissions

​Guardrail versioning

​Bedrock guardrail capabilities

​Content filters

​Denied topics

​Sensitive information filters

​Word filters

​Error handling

​Troubleshooting

​Common errors

​Verify your guardrail

​Next steps

Prerequisites

Create a Bedrock guardrail in AWS

Configuration parameters

Required parameters

Optional parameters

AWS authentication options

Access keys (recommended for getting started)

Temporary credentials (STS)

IAM role assumption

Web identity (OIDC)

Required IAM permissions

Guardrail versioning

Bedrock guardrail capabilities

Content filters

Denied topics

Sensitive information filters

Word filters

Error handling

Troubleshooting

Common errors

Verify your guardrail

Next steps