Create AI guardrails for Amazon Q in Connect - Amazon Connect
Important things to knowHow to create an AI guardrailChange the default blocked messageSample CLI commands

Create AI guardrails for Amazon Q in Connect

An AI guardrail is a resource that enables you to implement safeguards based on your use cases and responsible AI policies.

Amazon Connect uses Amazon Bedrock guardrails. You can create and edit these guardrails in the Amazon Connect admin website.

Important things to know

  • You can create up to three custom guardrails.

  • Amazon Q in Connect guardrails supports the same languages as Amazon Bedrock guardrails classic tier. For a complete list of supported languages, see Languages supported by Amazon Bedrock Guardrails. Evaluating text content in other languages will be ineffective.

  • When configuring or editing a guardrail, we strongly recommend that you experiment and benchmark with different configurations. It's possible that some of your combinations may have unintended consequences. Test the guardrail to ensure that the results meet your use-case requirements.

How to create an AI guardrail

  1. Log in to the Amazon Connect admin website with an account that has Amazon Q, AI guardrails - Create permission in its security profile.

  2. In the Amazon Connect admin website, on the left navigation menu, choose Amazon Q, AI guardrails.

  3. On the Guardrails page, choose Create Guardrail.

  4. On the Create AI Guardrail dialog box, enter a name and description of the guardrail, and then choose Create.

  5. On the AI Guardrail builder page, complete the following fields as needed to create policies for your guardrail:

    • Content filters: Adjust filter strengths to help block input prompts or model responses containing harmful content. Filtering is done based on detection of certain predefined harmful content categories - Hate, Insults, Sexual, Violence, Misconduct and Prompt Attack.

    • Denied topics: Define a set of topics that are undesirable in the context of your application. The filter will help block them if detected in user queries or model responses. You can add up to 30 denied topics.

    • Contextual grounding check: Help detect and filter hallucinations in model responses based on grounding in a source and relevance to the user query.

    • Word filters: Configure filters to help block undesirable words, phrases, and profanity (exact match). Such words can include offensive terms, competitor names, etc.

    • Sensitive information filters: Configure filters to help block or mask sensitive information, such as personally identifiable information (PII), or custom regex in user inputs and model responses.

      Blocking or masking is done based on probabilistic detection of sensitive information in standard formats in entities such as SSN number, Date of Birth, address, etc. This also allows configuring regular expression based detection of patterns for identifiers.

    • Blocked messaging: Customize the default message that's displayed to the user if your guardrail blocks the input or the model response.

    Amazon Connect does not support Image content filter to help detect and filter inappropriate or toxic image content.

  6. When your guardrail is complete, choose Save.

    When selecting from the versions dropdown, Latest:Draft always returns the saved state of the AI guardrail.

  7. Choose Publish. Updates to the AI guardrail are saved, the AI guardrail Visibility status is set to Published, and a new AI Guardrail version is created.

    The AI guardrail page, the Visibility status set to Published.

    When selecting from the versions dropdown, Latest:Published always returns the saved state of the AI guardrail.

Change the default blocked message

This section explains how to access the AI guardrail builder and editor in the Amazon Connect admin website, using the example of changing the blocked message that is displayed to users.

The following image shows an example of the default blocked message that is displayed to a user. The default message is "Blocked input text by guardrail."

An example of a default guardrail message displayed to a customer.
To change the default blocked message

  1. Log in to the Amazon Connect admin website at https://instance name.my.connect.aws/. Use an admin account, or an account with Amazon Q - AI guardrails - Create permission in it's security profile.

  2. On the navigation menu, choose Amazon Q, AI guardrails.

  3. On the AI Guardrails page, choose Create AI Guardrail. A dialog is displayed for to you assign a name and description.

  4. In the Create AI Guardrail dialog box, enter a name and description, and then choose Create. If your business already has three guardrails, you'll get an error message, as shown in the following image.

    A message that your business already has three guardrails.

    If you receive this message, instead of creating another guardrail, consider editing an existing guardrail to meet your needs. Or, delete one so you can create another.

  5. To change the default message that's displayed when guardrail blocks the model response, scroll to the Blocked messaging section.

  6. Enter the block message text that you want to be displayed, choose Save, and then Publish.

Sample CLI commands to configure AI guardrail policies

Following are examples of how to configure the AI guardrail policies by using the AWS CLI.

Block undesirable topics

Use the following sample AWS CLI command to block undesirable topics.

{
        "assistantId": "a0a81ecf-6df1-4f91-9513-3bdcb9497e32",
        "name": "test-ai-guardrail-2",
        "description": "This is a test ai-guardrail",
        "blockedInputMessaging": "Blocked input text by guardrail",
        "blockedOutputsMessaging": "Blocked output text by guardrail",
        "visibilityStatus": "PUBLISHED",
        "topicPolicyConfig": {
                  "topicsConfig": [
                    {
                      "name": "Financial Advice",
                      "definition": "Investment advice refers to financial inquiries, guidance, or recommendations with the goal of generating returns or achieving specific financial objectives.",
                      "examples": ["- Is investment in stocks better than index funds?", "Which stocks should I invest into?", "- Can you manage my personal finance?"],
                      "type": "DENY"
                    }
                  ]
                }
    }

Filter harmful and inappropriate content

Use the following sample AWS CLI command to filter harmful and inappropriate content. 

{
        "assistantId": "a0a81ecf-6df1-4f91-9513-3bdcb9497e32",
        "name": "test-ai-guardrail-2",
        "description": "This is a test ai-guardrail",
        "blockedInputMessaging": "Blocked input text by guardrail",
        "blockedOutputsMessaging": "Blocked output text by guardrail",
        "visibilityStatus": "PUBLISHED",
        "contentPolicyConfig": {
                  "filtersConfig": [
                    {
                       "inputStrength": "HIGH",
                       "outputStrength": "HIGH",
                       "type": "INSULTS"
                    }
                  ]
                }
    }

Filter harmful and inappropriate words

Use the following sample AWS CLI command to filter harmful and inappropriate words.  

{
        "assistantId": "a0a81ecf-6df1-4f91-9513-3bdcb9497e32",
        "name": "test-ai-guardrail-2",
        "description": "This is a test ai-guardrail",
        "blockedInputMessaging": "Blocked input text by guardrail",
        "blockedOutputsMessaging": "Blocked output text by guardrail",
        "visibilityStatus": "PUBLISHED",
        "wordPolicyConfig": {
                  "wordsConfig": [
                    {
                       "text": "Nvidia",
                    },
                  ]
                }
    }

Detect hallucinations in the model response

Use the following sample AWS CLI command to detect hallucinations in the model response.  

{
        "assistantId": "a0a81ecf-6df1-4f91-9513-3bdcb9497e32",
        "name": "test-ai-guardrail-2",
        "description": "This is a test ai-guardrail",
        "blockedInputMessaging": "Blocked input text by guardrail",
        "blockedOutputsMessaging": "Blocked output text by guardrail",
        "visibilityStatus": "PUBLISHED",
        "contextualGroundPolicyConfig": {
                  "filtersConfig": [
                    {
                       "type": "RELEVANCE",
                       "threshold": 0.50
                    },
                  ]
                }
    }

Redact sensitive information

Use the following sample AWS CLI command to redact sensitive information such as personal identifiable information (PII).

{
        "assistantId": "a0a81ecf-6df1-4f91-9513-3bdcb9497e32",
        "name": "test-ai-guardrail-2",
        "description": "This is a test ai-guardrail",
        "blockedInputMessaging": "Blocked input text by guardrail",
        "blockedOutputsMessaging": "Blocked output text by guardrail",
        "visibilityStatus": "PUBLISHED",
        "sensitiveInformationPolicyConfig": {
                  "piiEntitiesConfig": [
                    {
                       "type": "CREDIT_DEBIT_CARD_NUMBER",
                       "action":"BLOCK",
                    },
                  ]
                }
    }