NVIDIA Starts to Tackle GPU Power Smoothing with the NVIDIA GB300 NVL72

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/nvidia-starts-to-tackle-gpu-power-smoothing-with-the-nvidia-gb300-nvl72/

NVIDIA showed GPU burn and its plan to help the NVIDIA GB300 NVL72 clusters handle huge spikes in power during AI training

The post NVIDIA Starts to Tackle GPU Power Smoothing with the NVIDIA GB300 NVL72 appeared first on ServeTheHome.

Help for OpenPrinting needed

Post Syndicated from jzb original https://lwn.net/Articles/1031701/

Till Kamppeter, co-founder and lead of the OpenPrinting project, has
put out a call for sponsors after being laid off by Canonical:

I want to continue doing OpenPrinting for a living, and need a way to
do so. I am currently working with the Linux Foundation to make
OpenPrinting an [organization] which can receive sponsor funding. So now
I am looking for sponsors.

Even greater would be, if independent of this somebody could hire
me to continue OpenPrinting…

Accelerate your data quality journey for lakehouse architecture with Amazon SageMaker, Apache Iceberg on AWS, Amazon S3 tables, and AWS Glue Data Quality

Post Syndicated from Brody Pearman original https://aws.amazon.com/blogs/big-data/accelerate-your-data-quality-journey-for-lakehouse-architecture-with-amazon-sagemaker-apache-iceberg-on-aws-amazon-s3-tables-and-aws-glue-data-quality/

In an era where data drives innovation and decision-making, organizations are increasingly focused on not only accumulating data but on maintaining its quality and reliability. High-quality data is essential for building trust in analytics, enhancing the performance of machine learning (ML) models, and supporting strategic business initiatives.

By using AWS Glue Data Quality, you can measure and monitor the quality of your data. It analyzes your data, recommends data quality rules, evaluates data quality, and provides you with a score that quantifies the quality of your data. With this, you can make confident business decisions. With this launch, AWS Glue Data Quality is now integrated with the lakehouse architecture of Amazon SageMaker, Apache Iceberg on general purpose Amazon Simple Storage Service (Amazon S3) buckets, and Amazon S3 Tables. This integration brings together serverless data integration, quality management, and advanced ML capabilities in a unified environment.

This post explores how you can use AWS Glue Data Quality to maintain data quality of S3 Tables and Apache Iceberg tables on general purpose S3 buckets. We’ll discuss strategies for verifying the quality of published data and how these integrated technologies can be used to implement effective data quality workflows.

Solution overview

In this launch, we’re supporting the lakehouse architecture of Amazon SageMaker, Apache Iceberg on general purpose S3 buckets, and Amazon S3 Tables. As example use cases, we demonstrate data quality on an Apache Iceberg table stored in a general purpose S3 bucket as well as on Amazon S3 Tables. The steps will cover the following:

  1. Create an Apache Iceberg table on a general purpose Amazon S3 bucket and an Amazon S3 table in a table bucket using two AWS Glue extract, transform, and load (ETL) jobs
  2. Grant appropriate AWS Lake Formation permissions on each table
  3. Run data quality recommendations at rest on the Apache Iceberg table on general purpose S3 bucket
  4. Run the data quality rules and visualize the results in Amazon SageMaker Unified Studio
  5. Run data quality recommendations at rest on the S3 table
  6. Run the data quality rules and visualize the results in SageMaker Unified Studio

The following diagram is the solution architecture.

Prerequisites

To implement the instructions, you must have the following prerequisites:

Create S3 tables and Apache Iceberg on general purpose S3 bucket

First, complete the following steps to upload data and scripts:

  1. Upload the attached AWS Glue job scripts to your designated script bucket in S3
    1. create_iceberg_table_on_s3.py
    2. create_s3_table_on_s3_bucket.py
  2. To download the New York City Taxi – Yellow Trip Data dataset for January 2025 (Parquet file), navigate to NYC TLC Trip Record Data, expand 2025, and choose Yellow Taxi Trip records under January section. A file called yellow_tripdata_2025-01.parquet will be downloaded to your computer.
  3. On the Amazon S3 console, open an input bucket of your choice and create a folder called nyc_yellow_trip_data. The stack will create a GlueJobRole with permissions to this bucket.
  4. Upload the yellow_tripdata_2025-01.parquet file to the folder.
  5. Download the CloudFormation stack file. Navigate to the CloudFormation console. Choose Create stack. Choose Upload a template file and select the CloudFormation template you downloaded. Choose Next.
  6. Enter a unique name for Stack name.
  7. Configure the stack parameters. Default values are provided in the following table:
Parameter Default value Description
ScriptBucketName N/A – user-supplied Name of the referenced Amazon S3 general purpose bucket containing the AWS Glue job scripts
DatabaseName iceberg_dq_demo Name of the AWS Glue Database to be created for the Apache Iceberg table on general purpose Amazon S3 bucket
GlueIcebergJobName create_iceberg_table_on_s3 The name of the created AWS Glue job that creates the Apache Iceberg table on general purpose Amazon S3 bucket
GlueS3TableJobName create_s3_table_on_s3_bucket The name of the created AWS Glue job that creates the Amazon S3 table
S3TableBucketName dataquality-demo-bucket Name of the Amazon S3 table bucket to be created.
S3TableNamespaceName s3_table_dq_demo Name of the Amazon S3 table bucket namespace to be created
S3TableTableName ny_taxi Name of the Amazon S3 table to be created by the AWS Glue job
IcebergTableName ny_taxi Name of the Apache Iceberg table on general purpose Amazon S3 to be created by the AWS Glue job
IcebergScriptPath scripts/create_iceberg_table_on_s3.py The referenced Amazon S3 path to the AWS Glue script file for the Apache Iceberg table creation job. Verify the file name matches the corresponding GlueIcebergJobName
S3TableScriptPath scripts/create_s3_table_on_s3_bucket.py The referenced Amazon S3 path to the AWS Glue script file for the Amazon S3 table creation job. Verify the file name matches the corresponding GlueS3TableJobName
InputS3Bucket N/A – user-supplied bucket Name of the referenced Amazon S3 bucket with which the NY Taxi data was uploaded
InputS3Path nyc_yellow_trip_data The referenced Amazon S3 path with which the NY Taxi data was uploaded
OutputBucketName N/A – user-supplied Name of the created Amazon S3 general purpose bucket for the AWS Glue job for Apache Iceberg table data

Complete the following steps to configure AWS Identity and Access Management (IAM) and Lake Formation permissions:

  1. If you haven’t previously worked with S3 Tables and analytics services, navigate to Amazon S3.
  2. Choose Table buckets.
  3. Choose Enable integration to enable analytics service integrations with your S3 table buckets.
  4. Navigate to the Resources tab for your AWS CloudFormation stack. Note the IAM role with the logical ID GlueJobRole and the database name with the logical ID GlueDatabase. Additionally, note the name of the S3 table bucket with the logical ID S3TableBucket as well as the namespace name with the logical ID S3TableBucketNamespace. The S3 table bucket name is the portion of the Amazon Resource Name (ARN) which follows: arn:aws:s3tables:<region>:<accountID>:bucket/{S3 Table bucket Name}. The namespace name is the portion of the namespace ARN which follows: arn:aws:s3tables:<region>:<accountID>:bucket/{S3 Table bucket Name}|{namespace name}.
  5. Navigate to the Lake Formation console with a Lake Formation data lake administrator.
  6. Navigate to the Databases tab and select your GlueDatabase. Note the selected default catalog should match your AWS account ID.
  7. Select the Actions dropdown menu and under Permissions, choose Grant.
  8. Grant your GlueJobRole from step 4 the necessary permissions. Under Database permissions, select Create table and Describe, as shown in the following screenshot.

Navigate back to the Databases tab in Lake Formation and select the catalog that matches with the value of S3TableBucket you noted in step 4 in the format: <AWS account ID>:s3tablescatalog/<S3 Table Bucket name>

  1. Select your namespace name. From the Actions dropdown menu, under Permissions, choose Grant.
  2. Grant your GlueJobRole from step 4 the necessary permissions Under Database permissions, select Create table and Describe, as shown in the following screenshot.

To run the jobs created in the CloudFormation stack to create the sample tables and configure Lake Formation permissions for the DataQualityRole, complete the following steps:

  1. In the Resources tab of your CloudFormation stack, note the AWS Glue job names for the logical resource IDs: GlueS3TableJob and GlueIcebergJob.
  2. Navigate to the AWS Glue console and select ETL jobs. Select your GlueIcebergJob from step 11 and choose Run job. Select your GlueS3TableJob and choose Run job.
  3. To verify the successful creation of your Apache Iceberg table on general purpose S3 bucket in the database, navigate to Lake Formation with your Lake Formation data lake administrator permissions. Under Databases, select your GlueDatabase. The selected default catalog should match your AWS account ID.
  4. On the dropdown menu, choose View and then Tables. You should see a new tab with the table name you specified for IcebergTableName. You have verified the table creation.
  5. Select this table and grant your DataQualityRole (<stack_name>-DataQualityRole-<xxxxxx>) the necessary Lake Formation permissions by choosing the Grant link in the Actions tab. Choose Select, Describe from Table permissions for the new Apache Iceberg table.
  6. To verify the S3 table in the S3 table bucket, navigate to Databases in the Lake Formation console with your Lake Formation data lake administrator permissions. Make sure the selected catalog is your S3 table bucket catalog: <AWS account ID>:s3tablescatalog/<S3 Table Bucket name>
  7. Select your S3 table namespace and choose the dropdown menu View.
  8. Choose Tables and you should see a new tab with the table name you specified for S3TableTableName. You have verified the table creation.
  9. Choose the link for the table and under Actions, choose Grant. Grant your DataQualityRole the necessary Lake Formation permissions. Choose Select, Describe from Table permissions for the S3 table.
  10. In the Lake Formation console with your Lake Formation data lake administrator permissions, on the Administration tab, choose Data lake locations .
  11. Choose Register location. Input your OutputBucketName as the Amazon S3 path. Input the LakeFormationRole from the stack resources as the IAM role. Under Permission mode, choose Lake Formation.
  12. On the Lake Formation console under Application integration settings, select Allow external engines to access data in Amazon S3 locations with full table access, as shown in the following screenshot.

Generate recommendations for Apache Iceberg table on general purpose S3 bucket managed by Lake Formation

In this section, we show how to generate data quality rules using the data quality rule recommendations feature of AWS Glue Data Quality for your Apache Iceberg table on a general purpose S3 bucket. Follow these steps:

  1. Navigate to the AWS Glue console. Under Data Catalog, choose Databases. Choose the GlueDatabase.
  2. Under Tables, select your IcebergTableName. On the Data quality tab, choose Run history.
  3. Under Recommendation runs, choose Recommend rules.
  4. Use the DataQualityRole (<stack_name>-DataQualityRole-<xxxxxx>) to generate data quality rule recommendations, leaving the other settings as default. The results are shown in the following screenshot.

Run data quality rules for Apache Iceberg table on general purpose S3 bucket managed by Lake Formation

In this section, we show how to create a data quality ruleset with the recommended rules. After creating the ruleset, we run the data quality rules. Follow these steps:

  1. Copy the resulting rules from your recommendation run by selecting the dq-run ID and choosing Copy.
  2. Navigate back to the table under the Data quality tab and choose Create data quality rules. Paste the ruleset from step 1 here. Choose Save ruleset, as shown in the following screenshot.

  1. After saving your ruleset, navigate back to the Data Quality tab for your Apache Iceberg table on the general purpose S3 bucket. Select the ruleset you created. To run the data quality evaluation run on the ruleset using your data quality role, choose Run, as shown in the following screenshot.

Generate recommendations for the S3 table on the S3 table bucket

In this section, we show how to use the AWS Command Line Interface (AWS CLI) to generate recommendations for your S3 table on the S3 table bucket. This will also create a data quality ruleset for the S3 table. Follow these steps:

  1. Fill in your S3 table namespace name, S3 table table name, Catalog ID, and Data Quality role ARN in the following JSON file and save it locally:
{
    "DataSource": {
        "GlueTable": {
            "DatabaseName": "<namespace name>",
            "TableName": "<table name>",
            "CatalogId": "<account ID>:s3tablescatalog/<s3 table bucket name>"
        }
    },
    "Role": "<Data Quality role ARN>",
    "NumberOfWorkers": 5,
    "Timeout": 120,
    "CreatedRulesetName": "data_quality_s3_table_demo_ruleset"
}
  1. Enter the following AWS CLI command replacing local file name and region with your own information:
aws glue start-data-quality-rule-recommendation-run --cli-input-json file://<file name> --region <region>
  1. Run the following AWS CLI command to confirm the recommendation run succeeds:
aws glue get-data-quality-rule-recommendation-run --run-id <input run ID from step 2> --region <region>

Run data quality rules for the S3 table on the S3 table bucket

In this section, we show how to use the AWS CLI to evaluate the data quality ruleset on the S3 tables bucket that we just created. Follow these steps:

  1. Replace S3 table namespace name, S3 tables table name, Catalog ID, and Data Quality role ARN with your own information in the following JSON file and save it locally:
{
    "DataSource": {
         "GlueTable": {
            "DatabaseName": "<namespace name>",
            "TableName": "<table name>",
            "CatalogId": "<account ID>:s3tablescatalog/<s3 table bucket name>"
        }
    },
    "Role": "<>",
    "NumberOfWorkers": 2,
    "Timeout": 120,
    "AdditionalRunOptions": {
        "CloudWatchMetricsEnabled": true,
        "CompositeRuleEvaluationMethod": "COLUMN"
    },
    "RulesetNames": ["data_quality_s3_table_demo_ruleset"]
}
  1. Run the following AWS CLI command replacing local file name and region with your information:
aws glue start-data-quality-ruleset-evaluation-run --cli-input-json file://<file name> --region <region>
  1. Run the following AWS CLI command replacing region and data quality run ID with your information:
aws glue get-data-quality-ruleset-evaluation-run --run-id <input run ID from step 2> --region <region>

View results in SageMaker Unified Studio

Complete the following steps to view results from your data quality evaluation runs in SageMaker Unified Studio:

  1. Log in to the SageMaker Unified Studio portal using your single sign-on (SSO).
  2. Navigate to your project and note the project role ARN
  3. Navigate to the Lake Formation console with your Lake Formation data lake administrator permissions. Select your Apache Iceberg table that you created on general purpose S3 bucket and choose Grant from the Actions dropdown menu. Grant the following Lake Formation permissions to your SageMaker Unified Studio project role from step 2:
    1. Describe for Table permissions and Grantable permissions
  4. Next, select your S3 Table from the S3 Table bucket catalog in Lake Formation and choose Grant from the Actions drop-down. Grant the below Lake Formation permissions to your SageMaker Unified Studio project role from step 2:
    1. Describe for Table permissions and Grantable permissions
  5. Follow the steps at Create an Amazon SageMaker Unified Studio data source for AWS Glue in the project catalog to configure your data source for your GlueDatabase and your S3 tables namespace.
    1. Choose a name and optionally enter a description for your data source details.
    2. Choose AWS Glue (Lakehouse) for your Data source type. Leave connection and data lineage as the default values.
    3. Choose Use the AwsDataCatalog for the Apache Iceberg table on general purpose S3 bucket AWS Glue database.
    4. Choose the Database name corresponding to the GlueDatabase.Choose Next.
    5. Under Data quality, select Enable data quality for this data source. Leave the rest of the defaults.
    6. Configure the next data source with a name for your S3 table namespace. Optionally, enter a description for your data source details.
    7. Choose AWS Glue (Lakehouse) for your Data source type. Leave connection and data lineage as the default values.
    8. Choose to enter the catalog name: s3tablescatalog/<S3TableBucketName>
    9. Choose the Database name corresponding to the S3 table namespace. Choose Next.
    10. Select Enable data quality for this data source. Leave the rest of the defaults.
  6. Run each dataset.
  7. Navigate to your project’s Assets and select the related asset that you created for Apache Iceberg table on general purpose S3 bucket. Navigate to the Data Quality tab to view your data quality results. You should be able to see the data quality results for the S3 table asset similarly.

The data quality results in the following screenshot show each rule evaluated in the selected data quality evaluation run and its result. The data quality score calculates the percentage of rules that passed, and the overview shows how certain rule types faired across the evaluation. For example, Completeness rule types all passed, but ColumnValues rule types passed only three out of nine times.

Cleanup

To avoid incurring future charges, clean up the resources you created during this walkthrough:

  1. Navigate to the blog post output bucket and delete its contents.
  2. Un-register the data lake location for your output bucket in Lake Formation
  3. Revoke the Lake Formation permissions for your SageMaker project role, for your data quality role, and for your AWS Glue job role.
  4. Delete the input data file and the job scripts from your bucket.
  5. Delete the S3 table.
  6. Delete the CloudFormation stack.
  7. [Optional] Delete your SageMaker Unified Studio domain and the associated CloudFormation stacks it created on your behalf.

Conclusion

In this post, we demonstrated how you can now generate data quality recommendation for your lakehouse architecture using Apache Iceberg tables on general purpose Amazon S3 buckets and Amazon S3 Tables. Then we showed how to integrate and view these data quality results in Amazon SageMaker Unified Studio. Try this out for your own use case and share your feedback and questions in the comments.


About the Authors

Brody Pearman is a Senior Cloud Support Engineer at Amazon Web Services (AWS). He’s passionate about helping customers use AWS Glue ETL to transform and create their data lakes on AWS while maintaining high data quality. In his free time, he enjoys watching football with his friends and walking his dog.

Shiv Narayanan is a Technical Product Manager for AWS Glue’s data management capabilities like data quality, sensitive data detection and streaming capabilities. Shiv has over 20 years of data management experience in consulting, business development and product management.

Shriya Vanvari is a Software Developer Engineer in AWS Glue. She is passionate about learning how to build efficient and scalable systems to provide better experience for customers. Outside of work, she enjoys reading and chasing sunsets.

Narayani Ambashta is an Analytics Specialist Solutions Architect at AWS, focusing on the automotive and manufacturing sector, where she guides strategic customers in developing modern data and AI strategies. With over 15 years of cross-industry experience, she specializes in big data architecture, real-time analytics, and AI/ML technologies, helping organizations implement modern data architectures. Her expertise spans across lakehouse architecture, generative AI, and IoT platforms, enabling customers to drive digital transformation initiatives. When not architecting modern solutions, she enjoys staying active through sports and yoga.

AWS Weekly Roundup: SQS fair queues, CloudWatch generative AI observability, and more (July 28, 2025)

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-sqs-fair-queues-cloudwatch-generative-ai-observability-and-more-july-28-2025/

To be honest, I’m still recovering from the AWS Summit in New York, doing my best to level up on launches like Amazon Bedrock AgentCore (Preview) and Amazon Simple Storage Service (S3) Vectors. There’s a lot of new stuff to learn!

Meanwhile, it’s been an exciting week for AWS builders focused on reliability and observability. The standout announcement has to be Amazon SQS fair queues, which tackles one of the most persistent challenges in multi-tenant architectures: the “noisy neighbor” problem. If you’ve ever dealt with one tenant’s message processing overwhelming shared infrastructure and affecting other tenants, you’ll appreciate how this feature enables more balanced message distribution across your applications.

On the AI front, we’re also seeing AWS continue to enhance our observability capabilities with the preview launch of Amazon CloudWatch generative AI observability. This brings AI-powered insights directly into your monitoring workflows, helping you understand infrastructure and application performance patterns in new ways. And for those managing Amazon Connect environments, the addition of AWS CloudFormation for message template attachments makes it easier to programmatically deploy and manage email campaign assets across different environments.

Last week’s launches

  • Amazon SQS Fair Queues — AWS launched Amazon SQS fair queues to help mitigate the “noisy neighbor” problem in multi-tenant systems, enabling more balanced message processing and improved application resilience across shared infrastructure.
  • Amazon CloudWatch Generative AI Observability (Preview) — AWS launched a preview of Amazon CloudWatch generative AI observability, enabling users to gain AI-powered insights into their cloud infrastructure and application performance through advanced monitoring and analysis capabilities.
  • Amazon Connect CloudFormation Support for Message Template Attachments —AWS has expanded the capabilities of Amazon Connect by introducing support for AWS CloudFormation for Outbound Campaign message template attachments, enabling customers to programmatically manage and deploy email campaign attachments across different environments.
  • Amazon Connect Forecast Editing — Amazon Connect introduces a new forecast editing UI that allows contact center planners to quickly adjust forecasts by percentage or exact values across specific date ranges, queues, and channels for more responsive workforce planning.
  • Bloom Filters for Amazon ElastiCache — Amazon ElastiCache now supports Bloom filters in version 8.1 for Valkey, offering a space-efficient way to quickly check if an item is in a set with over 98% memory efficiency compared to traditional sets.
  • Amazon EC2 Skip OS Shutdown Option — AWS has introduced a new option for Amazon EC2 that allows customers to skip the graceful operating system shutdown when stopping or terminating instances, enabling faster application recovery and instance state transitions.
  • AWS HealthOmics Git Repository Integration — AWS HealthOmics now supports direct Git repository integration for workflow creation, allowing researchers to seamlessly pull workflow definitions from GitHub, GitLab, and Bitbucket repositories while enabling version control and reproducibility.
  • AWS Organizations Tag Policies Wildcard Support — AWS Organizations now supports a wildcard statement (ALL_SUPPORTED) in Tag Policies, allowing users to apply tagging rules to all supported resource types for a given AWS service in a single line, simplifying policy creation and reducing complexity.

Blogs of note

Beyond IAM Access Keys: Modern Authentication Approaches — AWS recommends moving beyond traditional IAM access keys to more secure authentication methods, reducing risks of credential exposure and unauthorized access by leveraging modern, more robust approaches to identity management.

Upcoming AWS events

AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — AWS’s flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.

AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Mexico City (August 6) and Jakarta (August 7).

AWS Community Days — Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Singapore (August 2), Australia (August 15), Adria (September 5), Baltic (September 10), and Aotearoa (September 18).

[$] Smaller Fedora quality team proposes cuts

Post Syndicated from jzb original https://lwn.net/Articles/1031066/

Fedora’s quality
team
is looking to reduce the scope of test coverage and change
the project’s release criteria to drop some features from the list of
release blockers. This is, in part, an exercise in getting rid of
criteria, such as booting from optical media, that are less relevant.
It is also a necessity, since the Red Hat team focusing on Fedora
quality assurance (QA) is only half the size it was a year ago.

How to automatically disable users in AWS Managed Microsoft AD based on GuardDuty findings

Post Syndicated from Tim Kingdon original https://aws.amazon.com/blogs/security/how-to-automatically-disable-users-in-aws-managed-microsoft-ad-based-on-guardduty-findings/

Organizations are facing an increasing number of security threats, especially in the form of compromised user accounts. Manually monitoring and acting on suspicious activities is not only time-consuming but also prone to human error. The lack of automated responses to security incidents can lead to disastrous consequences, such as data breaches and financial loss.

In this blog post, I show you how to detect suspicious events using Amazon GuardDuty and create an automation from those findings to disable user accounts in AWS Directory Service for Microsoft Active Directory.

This post addresses scenarios where, for example, you have a web server that uses a Microsoft Active Directory user account (service account) to access an application or database resources on other servers, and you want to automate disabling the user account if suspicious activity is detected.

I walk you through how to deploy Microsoft Active Directory in AWS Directory Services, set up GuardDuty to monitor Amazon Elastic Compute Cloud (Amazon EC2) instances, and configure Amazon EventBridge with AWS Step Functions to trigger AWS Systems Manager Run Command to obtain the username and disable the user in Active Directory.

Solution overview

In this example, shown in Figure 1, you deploy a test EC2 instance and enable GuardDuty runtime monitoring. Findings will trigger an EventBridge rule that executes a Step Functions state machine, which runs two Systems Manager Run Command documents that discover the username and disable that user using the directory administration EC2 instance.

Figure 1: Solution architecture

Figure 1: Solution architecture

GuardDuty

GuardDuty is an automated threat detection service that continuously monitors for suspicious activity and unauthorized behavior to protect your AWS accounts, workloads, and data stored in Amazon Simple Storage Service (Amazon S3).

To activate GuardDuty:

  1. Go to GuardDuty on the AWS Management Console.
    1. If you’re activating GuardDuty for the first time, under Try threat detection with GuardDuty, select All Features and then choose Get Started.
    2. If you’ve used GuardDuty before, select Runtime Monitoring and then choose Enable under Runtime Monitoring.
    Figure 2: GuardDuty Runtime Monitoring enabled with EC2 monitoring

    Figure 2: GuardDuty Runtime Monitoring enabled with EC2 monitoring

AWS Managed Microsoft AD

AWS Managed Microsoft AD provides a fully managed service for Microsoft Active Directory (AD) in the AWS Cloud. When you create your directory, AWS deploys two domain controllers in separate Availability Zones that are exclusively yours for high availability. For use cases that require even higher resilience and performance in a specific AWS Region or during specific hours, you can scale AWS Managed Microsoft AD by deploying additional domain controllers to meet your needs. These domain controllers can help load balance, increase overall performance, or provide additional nodes to protect against temporary availability issues. Using AWS Managed Microsoft AD, you can define the correct number of domain controllers for your directory based on your use case.

To deploy a new AWS Managed Microsoft AD:

  1. Go to the Directory Service console.
  2. Choose Set up directory and select AWS Managed Microsoft AD.
  3. Select Standard Edition and enter a Directory DNS name and password.
  4. Select a virtual private cloud (VPC), for this example use the Default VPC.
  5. Choose Create directory.

Directory administration EC2 instance

This directory administration EC2 instance will be used to control the Microsoft Active Directory using AWS Systems Manager.

To deploy the directory administration EC2 instance:

  1. If you have deployed a new directory, you might need to wait 20–45 minutes until the directory status is Active.
  2. Select the Directory ID.
  3. Choose Actions and select Launch directory Administration EC2 Instance, using the default options.

Alternatively, you can build your own Windows EC2 instances with a role that has the AmazonSSMManagedInstanceCore policy, join it to the Active Directory domain, and install Active Directory management tools.

To remotely connect to the directory administration EC2 instance:

  1. Go to the Systems Manager console.
  2. Open Fleet Manager from the navigation pane.
  3. Select the Node ID for the instance with the name ending managementInstance.
  4. Choose Node Actions (top right), select Connect, and then choose Connect with Remote Desktop.
  5. Enter the username admin and the directory password that you set earlier.

Create a test Active Directory user

You will use this test user account to sign in to an EC2 instance and initiate a command that simulates suspicious activity that results in this account being disabled.

To use the directory administration EC2 instance to create a test user on the Active Directory:

  1. From the management EC2 instance, open the start menu, select Windows Administrative Tools and then open Active Directory Users and Computers.
  2. Browse to your Domain, the Domain OU, and then the Users OU, right-click and choose New and then select User.
  3. Create a TestUser user, making sure that you don’t select Account is disabled.

Create a privileged domain service account

You will create this domain user account with delegated permissions to be used by Systems Manager Windows Service.

To use the directory administration EC2 instance to create a service account in AD:

  1. From the management EC2 instance, open the start menu, select Windows Administrative Tools, and then open Active Directory Users and Computers.
  2. Browse to your Domain, the Domain OU, and then the Users OU. Right-click and select New, and then select User
  3. Create an SSMService user, making sure that you don’t select Account is disabled.

To delegate permission to the service account in AD:

  1. Right-click on the Users OU and select Delegate Control.
  2. Choose Next on the Delegation of Control Wizard.
  3. Add the new service user you created earlier and choose Next.
  4. Select Create a custom task to delegate and choose Next.
  5. Select Only the following objects in the folder and select User Objects, then choose Next.
  6. Select General and Property-specific to show the permissions, select Read userAccountControl and Write userAccountControl (near the end of the list), then choose Next and Finish.

To add a service account to the local administrators group:

  1. From the management EC2 instance, open the start menu, select Windows Administrative Tools, and then open Computer Management.
  2. Browse to Local Users and Groups, then to Groups.
  3. Right-click on Administrators and select Properties.
  4. Choose Add to add the new service user you created earlier and choose OK.

Configure Systems Manager

Configure Systems Manager on the directory administration EC2 instance with permission to manage the Active Directory.

To configure Systems Manager:

  1. From the management EC2 instance from the Start Menu, select Windows Administrative Tools, and then open Services.
  2. Locate the Amazon SSM Agent, right-click, and select Properties.
  3. Select the Log On tab and select This account.
  4. Within This account enter the privileged domain username you created earlier followed by @ and then the domain name, for example [email protected]. Enter your password and choose OK.
    Figure 3: Microsoft Windows Services showing Systems Manager Agent settings

    Figure 3: Microsoft Windows Services showing Systems Manager Agent settings

  5. Choose OK on the This account has been granted Log On As A Service right and The new logon name will not take effect until you stop and restart the service popups.
  6. Right-click Amazon SSM Agent and select Restart.

Systems Manager Run Command

Run Command is a feature of Systems Manager that can remotely and securely manage the configuration of your managed nodes. You can use Run Command to automate common administrative tasks and perform one-time configuration changes at scale. You can use Run Command from the console, the AWS Command Line Interface (AWS CLI), AWS Tools for PowerShell, or the AWS SDKs. Run Command is offered at no additional cost.

To create a Run Command document with a PowerShell command to disable domain user accounts:

  1. Go to the AWS Systems Manager console.
  2. Select Documents under Change Management Tools.
  3. Choose Create document and select Command or Session.
  4. Enter a name, for example DisableADUser.
  5. Select document type Command.
  6. Select YAML and then enter the following code:
    ---
    schemaVersion: "2.2"
    description: "Disable AD Users"
    parameters:
      UserName:
        type: String
        description: "(Required) The username to disable."
    mainSteps:
    - action: "aws:runPowerShellScript"
      name: "DisableUser"
      inputs:
        runCommand:
        - "import-module activedirectory"
        - "$disableuser = get-aduser {{ UserName }} | select-object -ExpandProperty DistinguishedName"
        - "dsmod user $disableuser -disabled yes"
    

  7. Choose Create document.

To create a Run Command document with a bash command to find a username from a UserID:

  1. Follow steps 1–3 from the previous procedure.
  2. Enter a name, for example GetUsernameFromID.
  3. Select document type Command.
  4. Select YAML and then enter the following code:
    ---
    description: "Get Username from Linux"
    schemaVersion: "2.2"
    parameters:
      UserId:
        type: String
        description: "(Required) The User ID to find."
        default: "1000"
    mainSteps:
    - action: aws:runShellScript
      name: GetLinuxUsername
      precondition:
        StringEquals:
        - platformType
        - Linux
      inputs:
        timeoutSeconds: 7200
        runCommand:
          - "#!/bin/bash"
          - "#"
          - "UserName=$(id -nu {{ UserId }})"
          - "if [[ $UserName == *'@'* ]]; then"
          - "echo ${UserName%@*} "
          - "else if [[ $UserName == *'\\'* ]]; then"
          - "echo $UserName | sed 's/.*\\\\//g'"
          - "fi"
          - "fi"
      outputs:
        - Name: output
          Selector: $.Payload.output
          Type: String
    

  5. Choose Create document.

Step Functions

Step Functions is a serverless orchestration service that you can use to coordinate multiple AWS services, microservices, and third-party integrations into business-critical applications. Step Functions is widely used for orchestrating complex workflows, such as loan processing, fraud detection, risk management, and compliance processes. By breaking down these processes into a series of steps, Step Functions provides a clear overview and control of the entire workflow. This helps make sure that it executes each stage correctly and in the right order. One of the critical aspects of using Step Functions in regulated industries is the importance of security and data protection.

By the end of this section, your state machine should have a sequential flow that starts with a choice that defaults to No UserID found and with the UserID present, includes the steps Find Username, Wait, Get Username, and Disable AD User. If it doesn’t, you can drag the actions into the correct order or change the next state associated with each action. Alternatively, copy this state machine definition JSON and import it directly into Step Functions.

To create a Step Functions state machine to execute the Systems Manager Run Commands:

  1. Go to the Step Functions console.
  2. Choose Get Started.
  3. Choose Create your own.
  4. Enter a name for the state machine, select Standard, and choose Continue.
  5. Select JSONPath as the state machine query language.
  6. From the navigation pane, search for and add the Pass action by dragging the action to the center window.
  7. Add the Systems Manager: SendCommand Action for Finding the Username using Run Command.
  8. Select the SendCommand, change the state name to Find Username, and then enter the following code into API Parameters on the right side of the screen.
    {
      "DocumentName": "GetUsernameFromID",
      "Parameters": {
        "UserId.$": "States.Array(States.JsonToString($.detail.service.runtimeDetails.process.euid))"
      },
      "Targets": [
        {
          "Key": "InstanceIds",
          "Values.$": "States.Array($.detail.resource.instanceDetails.instanceId)"
        }
      ]
    }
    

  9. With SendCommand selected, select the Input/Output tab, select Add original input to output using ResultPath, select Combine original input with result, and enter the following:
    $.RunCommand.State
    

  10. Add a Wait Action and set the number of seconds to wait before resuming the execution to 5 seconds.
  11. Add a Systems Manager: GetCommandInvocation action, which will get the Username value from Run Command and change the state name to Get Username, then enter the following API Parameters.
    {
      "CommandId.$": "$.RunCommand.State.Command.CommandId",
      "InstanceId.$": "$.detail.resource.instanceDetails.instanceId"
    }
    

  12. On the Input/Output tab, select Transform result with ResultSelector and enter the following:
    {
      "StandardOutputContent.$": "States.StringSplit($.StandardOutputContent,'\n')"
    }
    

  13. Add a Systems Manager: SendCommand action which will disable the Active Directory user using Run Command. Change the state name to Disable AD User then enter the following API Parameters, changing the InstanceIds value to the ID of your Active Directory Management server.
    {
      "DocumentName": "DisableADUser",
      "Parameters": {
        "UserName.$": "$.StandardOutputContent"
      },
      "Targets": [
        {
          "Key": "InstanceIds",
          "Values": [
            "i-0b22a22eec53b9321"
          ]
        }
      ]
    }
    

  14. Add a Choice action, choose the pencil icon next to Rule #1, choose Edit conditions, enter the variable $.detail.service.runtimeDetails.process.euid, select operator is present, value true, leave Not as blank, and choose Save Conditions.
  15. Re-arrange the state machine layout to the same structure as displayed in Figure 4, with a sequential flow that starts with a choice that defaults to No UserID found and with the UserID present includes the steps Find Username, Wait, Get Username, and Disable AD User.
    Figure 4: Step Functions state machine structure

    Figure 4: Step Functions state machine structure

  16. Choose Create (top right) and then Confirm to create the step function state machine.

To add permissions to enable the State Machine to run System Manager commands:

  1. Within the newly created state machine, choose Config (top center).
  2. Choose View in IAM, under Permissions, Execution role.
  3. Choose Add permissions, Attach Polices (center right).
  4. Search for and select AmazonSSMAutomationRole and choose Add permission.

EventBridge

EventBridge helps developers build event-driven architectures (EDA) by connecting loosely coupled publishers and consumers using event routing, filtering, and transformation. To create an EventBridge rule that triggers the Systems Manger Run Command document you created earlier:

  1. Go to the Amazon EventBridge console and select Create rule with EventBridge Rule.
  2. Enter a name, for example GuardDutyDisableADuser.
  3. Select Rule with an event pattern and choose Next.
  4. Under the Event pattern JSON window, choose Edit pattern and enter the following:
    {
      "source": ["aws.guardduty"],
      "detail-type": ["GuardDuty Finding"]
    }
    

  5. Choose Next.
  6. Select AWS Service.
  7. Select Step Functions state machine as the target.
  8. Select the state machine you created earlier, for example MyStateMachine-A123456789.
  9. Choose Next twice and choose Create rule

Create a test EC2 instance

To generate alerts on GuardDuty, you create a domain joined Linux EC2 instance. For this example, you’ll use two separate EC2 instances so you can monitor for activity from each instance within GuardDuty and use EventBridge to create automations.

To create an AWS Identity and Access Management (IAM) role to permit the EC2 instance to join the AD:

  1. Go to the IAM console.
  2. Select Policies from the navigation pane.
  3. Choose Create policy (top right).
  4. Select Policy editor JSON, enter the following code and choose Next.
    {
    "Version": "2012-10-17",
    "Statement": [
    	{
    		"Effect": "Allow",
    		"Action": [
    			"secretsmanager:GetSecretValue",
    			"secretsmanager:DescribeSecret"
    			],
    		"Resource": "*"
    	}
    	]
    }
    

  5. Enter the Policy name, for example SecretsManagerGetSecrets, and choose Create policy.
  6. Select Roles from navigation pane.
  7. Choose Create role (top right).
  8. Select AWS service and choose EC2 from the service or use case selection, then choose Next.
  9. Search for and select the following policies and choose Next
    • AmazonSSMDirectoryServiceAccess
    • AmazonSSMManagedInstanceCore
    • SecretsManagerGetSecrets (created earlier)
  10. Enter the role name, for example EC2DomainJoin, and choose Create role.

To create a secret that will be used to store privileged credentials used to join EC2 instances to the domain:

  1. Go to the Secrets Manager console.
  2. Select Store a new secret.
  3. Select Other type of secret.
  4. Add the following keys with the corresponding value of a domain username and password that have permissions to join computers to the domain:
    1. awsSeamlessDomainUsername
    2. awsSeamlessDomainPassword
  5. Choose Next.
  6. Enter the following secret name, replacing <d-1234567890> with your directory ID.
    aws/directory-services/<d-1234567890>/seamless-domain-join
    

  7. Choose Next twice, then Store.

For more information more, see Seamlessly joining an Amazon EC2 Linux instance to your AWS Managed Microsoft AD Active Directory.

To create a domain joined EC2 instance for testing this GuardDuty automation:

  1. Go to the Amazon EC2 console.
  2. Select Instances from navigation pane.
  3. Choose Launch Instances.
  4. Select Amazon Linux AMI.
  5. Select an existing Key Pair or create a new key pair.
  6. Scroll to the bottom and select Advanced details.
  7. Within Domain join directory, select the domain
  8. Within IAM instance profile, select the EC2DomainJoin role that you created earlier.
  9. Choose Launch Instance.

Testing

To simulate a threat, use a GuardDuty test domain that GuardDuty will recognize as a command and control server.

  1. Go to the Amazon EC2 console.
  2. Choose Instances from the navigation pane.
  3. Select the test EC2 instance that you created earlier.
  4. Choose Connect, select the Session Manager tab, and choose Connect
  5. Authenticate with your test user by entering su followed by the test user with the domain name that you created earlier. For example su [email protected], then enter the password.
  6. Enter the command curl guarddutyc2activityb.com.
    • You will receive an error because the page won’t resolve, but GuardDuty will have detected suspicious events.
  7. Go to the GuardDuty console and select Findings from the navigation pane.
  8. Within 3–5 minutes, you should see a high severity finding for Backdoor:EC2/C&CActivity.B!DNS.

Note: You must archive the GuardDuty finding before re-running this test, because the EventBridge rule only runs once against a GuardDuty finding with the same details. To archive the finding, select the check box next to the Backdoor:EC2/C&CActivity.B!DNS finding, choose Actions (top right), and select Archive.

Figure 5: GuardDuty simulated findings

Figure 5: GuardDuty simulated findings

If you go back to Active Directory Users and Computers on the Directory Administration EC2 instance, you should see that the Test User is now disabled. You can enable the user by right-clicking on the user and selecting Enable Account.

Figure 6: Active Directory Users and Computers showing the disabled test use

Figure 6: Active Directory Users and Computers showing the disabled test use

Conclusion

In this post, you learned how to deploy AWS Managed AD, Systems Manager Run Command, EventBridge, Step Functions, and GuardDuty to monitor for suspicious events and disable the associated Active Directory user account.

You can expand this scenario by creating Run Command documents that reset Active Directory passwords, disable computer accounts, or Active Directory tasks supported by Microsoft PowerShell. Additionally, you can add steps within the Step Functions state machine to notify administrators through Amazon Simple Notification Service (Amazon SNS) or add additional checks with AWS Lambda.

Although this post uses AWS Managed Microsoft AD, the same functionality can be achieved with a manual deployment of Active Directory on Amazon EC2 or on-premises, either by using an EC2 instance joined to the Active Directory domain with the Active Directory administration tools installed or by installing Systems Manager agent onto a management server on-premises.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on AWS re:Post GuardDuty or contact AWS Support.

Tim Kingdon

Tim Kingdon

Tim is a Senior Partner Solutions Architect at AWS and has more than 25 years of experience in healthcare, financial, government, and defence industries. In his role, he provides strategic technical guidance to partners and helps drive their success through technical enablement initiatives.

Security updates for Monday

Post Syndicated from jake original https://lwn.net/Articles/1031667/

Security updates have been issued by Debian (audiofile, libcaca, libetpan, libxml2, php7.4, snapcast, and thunderbird), Fedora (glibc, iputils, mingw-binutils, and thunderbird), Red Hat (kernel, kernel-rt, mod_auth_openidc, and mod_auth_openidc:2.3), SUSE (afterburn, apache2, atop, chromedriver, chromium, cloud-init, deepin-feature-enable, firefox, firefox-esr, grafana, grype-db, gstreamer-plugins-bad, javamail, jupyter-jupyterlab-templates, jupyter-nbdime, konsole, libetebase, libxmp, minio-client-20250721T052808Z, MozillaFirefox, MozillaFirefox-branding-SLE, opera, pdns-recursor, perl-Authen-SASL, polkit, python-Django, python3-pycares, python311-starlette, rpi-imager, ruby3.4-rubygem-thor, spdlog, thunderbird, varnish, viewvc, and xtrabackup), and Ubuntu (openjdk-21-crac).

Microsoft SharePoint Zero-Day

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/07/microsoft-sharepoint-zero-day.html

Chinese hackers are exploiting a high-severity vulnerability in Microsoft SharePoint to steal data worldwide:

The vulnerability, tracked as CVE-2025-53770, carries a severity rating of 9.8 out of a possible 10. It gives unauthenticated remote access to SharePoint Servers exposed to the Internet. Starting Friday, researchers began warning of active exploitation of the vulnerability, which affects SharePoint Servers that infrastructure customers run in-house. Microsoft’s cloud-hosted SharePoint Online and Microsoft 365 are not affected.

Here’s Microsoft on patching instructions. Patching isn’t enough, as attackers have used the vulnerability to steal authentication credentials. It’s an absolute mess. CISA has more information. Also these four links. Two Slashdot threads.

This is an unfolding security mess, and quite the hacking coup.

LWN is back

Post Syndicated from corbet original https://lwn.net/Articles/1031536/

The good folks at Linode still have not managed to fix whatever broke in
their data center, so we are running on an emergency backup server. Things
seem to be working, but the occasional glitch is to be expected. Please
accept our apologies for the extended downtime!

The 6.16 kernel is out

Post Syndicated from corbet original https://lwn.net/Articles/1031534/

Linus has released the 6.16 kernel:

It’s Sunday afternoon, and the release cycle has come to an end. Last
week was nice and calm, and there were no big show-stopper surprises
to keep us from the regular schedule, so I’ve tagged and pushed out
6.16 as planned.

Headline changes in this release include
enabling five-level page tables by default
on x86 systems,
a number of core-dump changes including
the ability to send core dumps to a socket,
the ability to create
pipes
in io_uring,
atomic-write support in the XFS
filesystem,
the elimination of block-layer bounce
buffering,
a new DMA-mapping API,
an option to block file descriptors passed
in via Unix-domain sockets,
and more.

See the LWN merge-window summaries (part 1,
part 2) and the KernelNewbies 6.16 page for
more information.

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