Artificial Intelligence

It’s our goal for AWS to be the most secure place to run any workload, and in support of that we’ve been deeply investing in security across our services since AWS’s inception more than two decades ago. Our AI services like Amazon Bedrock are built on this foundation and with the same focus. 

Frontier teams are not just using AI to code faster. They’re redesigning how software gets built. The result is 4.5x productivity gains, in some cases more than 10x.

Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and uses machine learning to predict SLA risk and prioritize work.

Business intelligence dashboards can fail silently, showing blank, stale, or wrong data even when every infrastructure monitor reports healthy. Learn how an AWS team built an automated, AI-powered content validation solution on Amazon Bedrock that scans hundreds of dashboards and alerts owners, cutting mean time to detection from days to under an hour.

Learn how a global interdealer broker built an automated architecture documentation pipeline on Amazon Bedrock AgentCore that analyzes .NET code bases, generates architecture diagrams, and maintains searchable documentation through Amazon Bedrock Knowledge Bases and AWS CodePipeline.

Learn how University Startups and its AWS partner g/d/n/a scaled Trinity, a conversational AI solution for students with disabilities, into a serverless multi-agent architecture on Amazon Bedrock that produces IDEA-aligned transition plans for school districts across the US.

Claude Fable 5.1 is now available on Amazon Bedrock and Claude Platform on AWS. This post covers Claude Fable 5.1’s improvements, the Enterprise Frontier Safeguards for keeping your data in a cloud environment you control, and how to start building with the model on Amazon Bedrock.

When Atos set out to upskill 400 engineers in agentic AI, hands-on learning was the missing ingredient. Over three days, engineers built multi-agent systems on AWS through an AI League event. This post explains why Atos chose the format, what engineers built and learned, and what other enterprises should consider.

As generative AI adoption scales, cost governance becomes a top challenge. Learn how Jamf built real-time, per-user spend enforcement for Amazon Bedrock using IAM Customer Managed Policies, an Amazon Athena cost view, and a serverless AWS Lambda loop that applies tiered model limits in near-real-time without disrupting active sessions.

Amazon Quick proof-of-concept projects often stall when security teams review the production plan. This post walks through designing dashboards, Spaces, knowledge bases, agents, and Flows with security controls that hold as you scale: dataset shaping, agent isolation, document classification, and approval gates.

t54 built x402-secure, a trust layer on Amazon Bedrock AgentCore payments that scores every endpoint before an autonomous agent pays it. See how session budgets, credential isolation, and a deterministic trust gate have governed more than 20 million agent-initiated transactions with no human in the loop.

Learn how ZS built a security-hardened Amazon SageMaker platform that balances developer agility with healthcare-grade governance, serving 1,000+ daily active users across 200+ SageMaker domains.