security
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Using aws-vault to manage access to your AWS resources from Kiro CLI
Feb 19, 2026 | 8 minute read
As we say at AWS, security is our top priority. This is why I have been spending time thinking about how to manage what our agentic AI tools can do. After publishing a previous post on this topic (Getting Kiro CLI to use short lived AWS credentials), this post takes a look at another approach you can take. Vincent reminded me about an awesome open source tool called aws-vault that helps developers move to using short lived, temporary credentials.
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Getting Kiro CLI to use short lived AWS credentials
Feb 19, 2026 | 10 minute read
Agentic AI tools like Kiro IDE and Kiro CLI are pretty awesome. You provide the right context, the right prompt, and away they will go. After a period of time, you can now review the output. Early on developers understood the power, but also the potential harm that could be cause (and we began to read stories like this one. Most developer tools now provide capabilities that allow you as a developer to have fine grain control of what they can and more importantly, should never do.
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Reading and writing data across different AWS accounts with Amazon Managed Workflows for Apache Airflow v2.x
Sep 7, 2021 | 13 minute read
Reading and writing data across different AWS accounts in you Apache Airflow DAGs As regular readers will know, I sometimes lurk in the Apache Airflow slack channel to see what is going on. If you are new to Apache Airflow, or want to get a deeper understanding then I highly recommend spending some time here. The community is super welcoming and eager to help new participants. It was during a recent session I came across an interesting problem that one of the builders was having, which was how to access (read/write) data in an S3 bucket which was in a different account to the one hosting Amazon Managed Workflows for Apache Airflow (MWAA).
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Working with permissions in Amazon Managed Workflows for Apache Airflow
Jan 27, 2021 | 10 minute read
Part of a series of posts to support an up-coming online event, the Innovate AI/ML on February 24th, from 9:00am GMT - you can sign up here Part 1 - Installation and configuration of Managed Workflows for Apache Airflow Part 2 - Working with Permissions <- this post Part 3 - Accessing Amazon Managed Workflows for Apache Airflow environments Part 4 - Interacting with Amazon Managed Workflows for Apache Airflow via the command line Part 5 - A simple CI/CD system for your development workflow Part 6 - Monitoring and logging Part 7 - Automating a simple AI/ML pipeline with Apache Airflow In this post I will be covering Part 2, how to ensure that you control access to Apache Airflow following best practices such as default no access/least privilege.