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Boost Your Productivity with awscurl: Simplifying IAM-Secured API Testing in AWS

Xebia

When you use AWS, you can interact with it through the console, sdk, or cli. The consumer of this API only needs to add the AWSSigv4 header, and as long as the role policy allows the invocation of the API, it will work. One of the significant advantages of the cloud is that you get a lot of security controls out of the box.

AWS 162
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New AWS Control Policy on the Block

Tenable

AWS has released an important new feature that allows you to apply permission boundaries around resources at scale called Resource Control Policies (RCPs). AWS just launched Resource Control Policies (RCPs), a new feature in AWS Organizations that lets you restrict the permissions granted to resources.

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Implementing a Version Control System for AWS QuickSight

Xebia

Among the myriads of BI tools available, AWS QuickSight stands out as a scalable and cost-effective solution that allows users to create visualizations, perform ad-hoc analysis, and generate business insights from their data. AWS does not provide a comprehensive list of supported dataset types.

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Amazon Bedrock Guardrails announces IAM Policy-based enforcement to deliver safe AI interactions

AWS Machine Learning - AI

With Amazon Bedrock Guardrails, you can implement safeguards in your generative AI applications that are customized to your use cases and responsible AI policies. Today, were announcing a significant enhancement to Amazon Bedrock Guardrails: AWS Identity and Access Management (IAM) policy-based enforcement.

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What happens when you leak AWS credentials and how AWS minimizes the damage

Xebia

I heard multiple times that AWS scans public GitHub repositories for AWS credentials and informs its users of the leaked credentials. So I am curious to see this for myself, so I decided to intentionally leak AWS credentials to a Public GitHub repository. This policy denies the most important actions.

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Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services

AWS Machine Learning - AI

Recognizing this need, we have developed a Chrome extension that harnesses the power of AWS AI and generative AI services, including Amazon Bedrock , an AWS managed service to build and scale generative AI applications with foundation models (FMs). The user signs in by entering a user name and a password.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. On AWS, you can use the fully managed Amazon Bedrock Agents or tools of your choice such as LangChain agents or LlamaIndex agents.