How TUI scales threat modeling for 100+ teams with Amazon Bedrock
AWS Architecture Blog · 软件开发
When your security team has four engineers and your organization has over 100 digital product teams, threat modeling becomes a bottleneck fast. That was the challenge TUI faced. TUI operates across the travel and tourism industry with airlines, hotels, cruise ships, and local travel experiences. Our Platform & Engineering division needs security assessments to keep pace. At TUI, each threat modeling session took 4–6 hours of manual work: reviewing architecture diagrams, mapping threats to internal standards, researching security issues, and documenting findings in Confluence. The team could complete only 3–4 sessions per month, leaving a constant backlog. TUI needed threat modeling to become mandatory for every new service, not a luxury TUI reserved for the highest-priority projects. TUI built an AI-powered threat modeling system using Amazon Bedrock , collaborating with the AWS Generative AI Innovation Center during a 10-week proof of concept (PoC) to develop and validate the architecture. Amazon Bedrock is a fully managed service for building generative AI applications, and TUI used the Claude Sonnet 4.5 foundation model from Anthropic. TUI chose Amazon Bedrock for managed access to Claude Sonnet 4.5 without operating model infrastructure, its extended thinking support that the workflow uses for deeper analysis, and its integration with the AWS services already in TUI’s stack. TUI deployed the agentic workflow on Amazon Bedrock AgentCore runtime , part of Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale with any framework or model. AgentCore runtime hosts the agent in production without provisioning or scaling infrastructure. After the PoC, TUI took the system to production within 30 days. In this post, we describe how TUI built an agentic AI workflow for threat modeling, the iteration process that shaped the architecture, and the results after deployment. The challenge TUI’s security engineering team followed a thorough but man