AWS Bedrock Guardrails

A safety and compliance platform that provides configurable safeguards to detect and filter harmful text and image content, redact sensitive information, detect hallucinations, and enforce responsible AI policies across foundation models in Amazon Bedrock and third-party models.

First-party excerpt: Amazon Bedrock Guardrails helps you safely build and deploy responsible generative AI applications with confidence. With industry-leading safety protections that block up to 88% of harmful content and deliver auditable, mathematically verifiable explanations for validation decisions with 99% accuracy

An API that applies Bedrock Guardrails safeguards to any foundation model, including self-hosted and third-party models, enabling consistent safety enforcement across diverse AI stacks.

First-party excerpt: The ApplyGuardrail API allows you to use the configurable safeguards offered by Bedrock Guardrails with any foundation model whether hosted on Amazon Bedrock or self-hosted models, including third-party models such as OpenAI and Google Gemini. You can also use Guardrails with an agent framework such as Strands Agents, including agents deployed using Amazon Bedrock AgentCore.

A formal logic-based safeguard that validates AI-generated information against logical rules to detect and correct hallucinations with up to 99% accuracy, providing mathematically verifiable compliance with policies.

First-party excerpt: Automated Reasoning checks in Amazon Bedrock Guardrails is the first and only generative AI safeguard to use formal logic to help prevent factual errors from hallucinations. Using sound mathematical techniques to verify, correct, and explain AI-generated information, Automated Reasoning checks systematically validate correct model responses with up to 99% accuracy.

A configurable safeguard platform for generative AI applications that blocks harmful content, detects prompt attacks, redacts PII, detects hallucinations, and applies safety controls consistently across any foundation model.

First-party excerpt: Amazon Bedrock Guardrails helps you safely build and deploy responsible generative AI applications with confidence. With industry-leading safety protections that block up to 88% of harmful content and deliver auditable, mathematically verifiable explanations for validation decisions with 99% accuracy, Guardrails provides configurable safeguards to help detect and filter harmful text and image content, redact sensitive information, detect model hallucinations, and more.

Centralized management of AI safety controls across multiple AWS accounts within an organization, enabling consistent application of guardrails across organizational units and accounts.

First-party excerpt: With cross-account safeguards available in Amazon Bedrock Guardrails, you can enable centralized enforcement of safety controls across multiple AWS accounts within an organization. Instead of configuring individual safeguards for each account, your security teams can implement a central guardrail in Bedrock policies within your organization that will automatically apply safeguards to all entities including organizational units and AWS accounts.

A fully managed service for building and scaling generative AI applications and agents with access to hundreds of foundation models from leading AI companies, with built-in security, compliance, and production infrastructure.

An end-to-end platform to build, connect, and optimize AI agents securely at scale using any framework and model with no infrastructure management required.

A fully managed agent solution combining OpenAI frontier models with AWS infrastructure, delivering faster execution, built-in memory, and enterprise security.

An end-to-end platform for building, connecting, and optimizing highly capable AI agents at scale using any framework and model, with production-grade security and no infrastructure management required.

Flexible batch processing capability for handling large volumes of AI requests asynchronously, optimized for cost-effectiveness while maintaining quality for non-real-time use cases.

A data customization tool to automate data preparation and integration with foundation models for improved AI performance.

A data customization tool for training foundation models on proprietary data to improve performance for domain-specific applications.

An optimization feature that intelligently routes prompts to the most cost-effective models while maintaining quality, reducing costs by up to 30%.

A data customization tool for securely adding organizational data to foundation models for more personalized and contextual AI responses.

A managed agent solution combining OpenAI frontier models with AWS infrastructure, delivering faster execution, built-in memory, and security for teams building with OpenAI models.

An optimization feature that creates smaller, faster models from larger ones, running up to 500% faster and costing up to 75% less with minimal accuracy impact.

An optimization feature that reduces costs and latency by caching frequently used prompts and data.

Feature that automatically routes prompts to the most appropriate and cost-effective model based on complexity and requirements, reducing costs by up to 30% while maintaining output quality.

Optimization technique that creates smaller, faster, and more cost-effective models from larger foundation models, running up to 500% faster and costing up to 75% less with minimal accuracy loss.

Access to OpenAI's frontier models through Amazon Bedrock, expanding model choice for diverse use cases.

Performance optimization feature that caches prompt inputs to reduce latency and costs for repeated or similar queries without sacrificing response quality.