AWS Security Blog
AWS partners with Anthropic and OpenAI to bring AWS Continuum into developer workflows
Customers have access to models that are continuously getting better with each new generation bringing larger context windows, stronger reasoning, and lower token costs. Getting the strongest AI-powered security will come from tools that combine the most relevant models with deep knowledge of a customer’s specific environment.
AWS Continuum for code vulnerabilities (Preview) is built to be that tool to help secure your code at machine speed. Today, we’re announcing our work with Anthropic and OpenAI that extends AWS Continuum directly into the developer workflows where code is being written: Anthropic Claude Code, OpenAI Codex, and Kiro. Developers can use these integrations to discover vulnerabilities, contextually prioritize, validate, and remediate, within their existing workflows.
Models are getting smarter
AI models are advancing rapidly. Each generation brings new capabilities, and different models excel at different tasks. The latest frontier models can now identify vulnerabilities and reason through multi-step attack paths that would take a human security team weeks to trace manually.
This is a genuine breakthrough in detection, but it creates a new challenge for your security teams: more findings, more complexity, and the need to determine which ones matter most in your environment and how to address them. The next challenge customers face is building the correct harness and orchestration to turn these models into a single interface that goes from detection through remediation. This is what we set out to do when creating Continuum, which brings together many different models and uses the model that’s most effective for each part of the process.
We also partner with the Frontier Model Forum, an industry consortium developing shared safety standards, evaluation methods, and benchmarking to ensure we can evaluate these models effectively together. We’re also working with model providers on shared security performance benchmarking to make sure we’re using the best model for each task within Continuum and our other AWS security products.
The harness
An AI harness is the orchestration layer that wraps around a model to connect it to tools, guardrails, memory, and workflows, so it delivers outcomes. Think of the model as the engine and the harness as everything around it. You need both to have a high-performance car.
Harnesses are becoming increasingly complex. Teams are stitching together multiple models, agents that call agents, and dynamic workflows, and are dealing with constant change driven by innovations in models, agent frameworks, and tool integrations.
As a result of that complexity, customers are implementing shadow infrastructure to manage integration layers across models and tools. Every time the landscape shifts, security and governance controls potentially break, forcing teams to go back to revisit them and make updates.
These challenges extend beyond the model. They arise in the orchestration required to connect different models and developer environments with tools, context, controls, and workflows across a customer’s environment. At AWS, we see managing that complexity as heavy lifting that AWS should solve. We treat the harness as infrastructure and with the same rigor we apply to identity, discovery, policy enforcement, observability, and compliance of the core infrastructure at AWS.
Enter Continuum
AWS Continuum for code vulnerabilities discovers vulnerabilities, prioritizes them within the context of a customer’s business, validates them in a sandbox, and provides remediation at machine speed. Under the hood, Continuum is an agent-team loop architecture. A sophisticated harness that orchestrates all of it: selecting the right model, connecting to a customer environment, and delivering secure code that’s been validated in context. You never need to think about how the orchestration works, or what changed in the latest release.
Anthropic and OpenAI collaborations
We are working with Anthropic and OpenAI to bring Continuum into the developer workflows where code is being written.
How it will work:
Within Claude Code, Codex, and Kiro coding environments, on-demand vulnerability scans identify potential issues and send findings to Continuum. Continuum prioritizes them within the context of the customer’s AWS environment (configurations, AWS Identity and Access Management (IAM) policies, network topology, and exposure surfaces) and validates them in a sandbox. It then returns prioritized, contextual intelligence back to the coding assistant, which adjusts its recommendations accordingly.
This collapses what was traditionally a multi-step, multi-team process (write, scan, triage, prioritize, fix, rescan) into a single outcome: the code suggestion itself. Two modes, one outcome:
- For existing code: Use Continuum for code vulnerabilities from AWS to discover, prioritize, validate, and remediate across your environment.
- For greenfield code: Use the Continuum plugin within Codex, Claude Code, or Kiro to get security-validated suggestions in your development environment.
Early design partners are already seeing results.
“AWS Continuum connects source code with enterprise knowledge, allowing teams to accurately pinpoint security vulnerabilities and verify that flagged issues are truly meaningful. This shortens what really matters: timeline to fix serious vulnerabilities.” – Mike Johnson, CISO, Rivian
Next
- AWS Continuum for code vulnerabilities is available in preview through AWS. Sign up to request access at AWS Continuum.
- Continuum integrated into Claude Code, Codex, and Kiro workflows are coming soon.
If you have feedback about this post, submit comments in the Comments section below.