Amazon Bedrock Guardrails

Amazon Bedrock Guardrails provides configurable safeguards to help safely build generative AI applications at scale. With a consistent and standard approach used across a wide range of foundation models (FMs) including FMs supported in Amazon Bedrock, fine-tuned models, and models hosted outside of Amazon Bedrock, Guardrails delivers industry-leading safety protections:

• Uses Automated Reasoning to minimize AI hallucinations, identifying correct model responses with up to 99% accuracy—the first and only generative AI safeguard to do so
• Industry-leading text and image content safeguards, helping customers block up to 88% of harmful multi modal content

Guardrails is the only responsible AI capability from a major cloud provider that helps you build and customize safety, privacy, and truthfulness safeguards for your generative AI applications. It evaluates user inputs and model responses based on use case-specific policies, providing an additional layer of safeguards beyond what's natively available. Safeguards from Guardrails can be applied to models hosted on Amazon Bedrock or with any third-party models (such as OpenAI and Google Gemini) via the ApplyGuardrail API. You can also use Guardrails with an agent framework such as Strands Agents, including agents deployed using Amazon Bedrock AgentCore. Guardrails helps filter hallucinations and improve factual accuracy through contextual grounding checks against RAG content and Automated Reasoning checks to deliver provably truthful responses. View the step by step guide to implement Amazon Bedrock Guardrails to learn more.

Customers need to deploy truthful and trustworthy generative AI applications to maintain and grow users’ trust. However, FMs can generate incorrect information due to hallucinations: deviating from the source information, conflating multiple pieces of information, or inventing new information. Guardrails supports contextual grounding checks to help detect and filter hallucinations if the responses are not grounded (for example, factually inaccurate or new information) in the source information and irrelevant to a user’s query or instruction. Contextual grounding checks can help detect hallucinations for RAG, summarization, and conversational applications, where the source information can be used as a reference to validate the model response.

Automated Reasoning checks in Amazon Bedrock Guardrails is the first and only generative AI safeguard that helps prevent factual errors from hallucinations using logically accurate and verifiable reasoning that explains why responses are correct. Automated Reasoning helps mitigate hallucinations using sound mathematical techniques to validate/correct, and logically explain the information generated—ensuring that outputs align with known facts and are not based on fabricated or inconsistent data. Developers can create an Automated Reasoning policy by uploading an existing document that defines the right solution space, such as an HR guideline or an operational manual. Amazon Bedrock then generates an Automated Reasoning policy and guides users through testing and refining it. To validate generated content against an Automated Reasoning policy, users need to enable the policy in Guardrails and configure it with a list of Automated Reasoning policies. This logic-based algorithmic verification process ensures that the information generated by a model aligns with known facts and is not based on fabricated or inconsistent data. These checks deliver provably truthful responses from generative AI models, enabling software vendors to enhance the reliability of their applications for use cases in HR, finance, legal, compliance, and more. See the video tutorials to learn more.

Organizational leaders recognize the need to manage interactions within generative AI applications for a relevant and safe user experience. They want to further customize interactions to remain focused on topics relevant to their business and align with company policies. Using a short natural language description, Guardrails helps you to define a set of topics to avoid within the context of your application. Guardrails helps detect and block user inputs and FM responses that fall into the restricted topics. For example, a banking assistant can be designed to avoid topics related to investment advice.

Guardrails provides content filters with configurable thresholds for toxic text and image content. The safeguard helps filter harmful multi modal content containing topics such as hate speech, insults, sex, violence, and misconduct (including criminal activity) and help protect against prompt attacks (prompt injection and jailbreak). Content filters automatically evaluate both user input and model responses to detect and help prevent undesirable and potentially harmful text and/or images. For example, an ecommerce site can design its online assistant to avoid using inappropriate language, such as hate speech or insults.

Guardrails helps you detect sensitive content such as personally identifiable information (PII) in user inputs and FM responses. You can select from a list of predefined PII or define a custom sensitive-information type using regular expressions (RegEx). Based on the use case, you can selectively reject inputs containing sensitive information or redact them in FM responses. For example, you can redact users’ personal information while generating summaries from customer and agent conversation transcripts in a call center.