Showing only posts by Jonathan Nguyen. Show all posts.

How to generate security findings to help your security team with incident response simulations

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Continually reviewing your organization’s incident response capabilities can be challenging without a mechanism to create security findings with actual Amazon Web Services (AWS) resources within your AWS estate. As prescribed within the AWS Security Incident Response whitepaper, it’s important to periodically review your incident response capabilities to …

Generate AI powered insights for Amazon Security Lake using Amazon SageMaker Studio and Amazon Bedrock

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In part 1, we discussed how to use Amazon SageMaker Studio to analyze time-series data in Amazon Security Lake to identify critical areas and prioritize efforts to help increase your security posture. Security Lake provides additional visibility into your environment by consolidating and normalizing security data from both AWS …

Generate machine learning insights for Amazon Security Lake data using Amazon SageMaker

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Amazon Security Lake automatically centralizes the collection of security-related logs and events from integrated AWS and third-party services. With the increasing amount of security data available, it can be challenging knowing what data to focus on and which tools to use. You can use native AWS services such as …

Use Amazon Macie for automatic, continual, and cost-effective discovery of sensitive data in S3

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Customers have an increasing need to collect, store, and process data within their AWS environments for application modernization, reporting, and predictive analytics. AWS Well-Architected security pillar, general data privacy and compliance regulations require that you appropriately identify and secure sensitive information. Knowing where your data is allows you to …

Export historical Security Hub findings to an S3 bucket to enable complex analytics

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AWS Security Hub is a cloud security posture management service that you can use to perform security best practice checks, aggregate alerts, and automate remediation. Security Hub has out-of-the-box integrations with many AWS services and over 60 partner products. Security Hub centralizes findings across your AWS accounts and supported …

Best practices for setting up Amazon Macie with AWS Organizations

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In this post, we’ll walk through the best practices to implement before you enable Amazon Macie across all of your AWS accounts within AWS Organizations. Amazon Macie is a data classification and data protection service that uses machine learning and pattern matching to help secure your critical data …

How to automatically build forensic kernel modules for Amazon Linux EC2 instances

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In this blog post, we will walk you through the EC2 forensic module factory solution to deploy automation to build forensic kernel modules that are required for Amazon Elastic Compute Cloud (Amazon EC2) incident response automation. When an EC2 instance is suspected to have been compromised, it’s strongly …

Learn more about the new allow list feature in Macie

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Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and help you protect your sensitive data in Amazon Web Services (AWS). The data that is available within your AWS account can grow rapidly, which increases your need …

Use Security Hub custom actions to remediate S3 resources based on Macie discovery results

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The amount of data available to be collected, stored and processed within an organization’s AWS environment can grow rapidly and exponentially. This increases the operational complexity and the need to identify and protect sensitive data. If your security teams need to review and remediate security risks manually, it …