Tag Archives: Intermediate (200)

Land data from databases to a data lake at scale using AWS Glue blueprints

Post Syndicated from Moustafa Mahmoud original https://aws.amazon.com/blogs/big-data/land-data-from-databases-to-a-data-lake-at-scale-using-aws-glue-blueprints/

To build a data lake on AWS, a common data ingestion pattern is to use AWS Glue jobs to perform extract, transform, and load (ETL) data from relational databases to Amazon Simple Storage Service (Amazon S3). A project often involves extracting hundreds of tables from source databases to the data lake raw layer. And for each source table, it’s recommended to have a separate AWS Glue job to simplify operations, state management, and error handling. This approach works perfectly with a small number of tables. However, with hundreds of tables, this results in hundreds of ETL jobs, and managing AWS Glue jobs at this scale may pose an operational challenge if you’re not yet ready to deploy using a CI/CD pipeline. Instead, we tackle this issue by decoupling the following:

  • ETL job logic – We use an AWS Glue blueprint, which allows you to reuse one blueprint for all jobs with the same logic
  • Job definition – We use a JSON file, so you can define jobs programmatically without learning a new language
  • Job deployment – With AWS Step Functions, you can copy workflows to manage different data processing use cases on AWS Glue

In this post, you will learn how to handle data lake landing jobs deployment in a standardized way—by maintaining a JSON file with table names and a few parameters (for example, a workflow catalog). AWS Glue workflows are created and updated after manually running the resources deployment flow in Step Functions. You can further customize the AWS Glue blueprints to make your own multi-step data pipelines to move data to downstream layers and purpose-built analytics services (example use cases include partitioning or importing to an Amazon DynamoDB table).

Overview of solution

The following diagram illustrates the solution architecture, which contains two major areas:

  • Resource deployment (components 1–2) – An AWS Step Functions workflow is run manually on demand to update or deploy the required AWS Glue resources. These AWS Glue resources will be used for landing data into the data lake
  • ETL job runs (components 3–6) – The AWS Glue workflows (one per source table) run on the defined schedule, and extract and land data to the data lake raw layer

The solution workflow contains the following steps:

  1. An S3 bucket stores an AWS Glue blueprint (ZIP) and the workflow catalog (JSON file).
  2. A Step Functions workflow orchestrates the AWS Glue resources creation.
  3. We use Amazon Aurora as the data source with our sample data, but any PostgreSQL database works with the provided script, or other JDBC sources with customization.
  4. AWS Secrets Manager stores the secrets of the source databases.
  5. On the predefined schedule, AWS Glue triggers relevant AWS Glue jobs to perform ETL.
  6. Extracted data is loaded into an S3 bucket that serves as the data lake raw layer.

Prerequisites

To follow along with this post, complete the following prerequisite steps.

  • If you want to use your existing database either in AWS or on premises as a data source, you need network connectivity (a subnet and security group) for the AWS Glue jobs that can access the source database, Amazon S3, and Secrets Manager.

Provision resources with AWS CloudFormation

In this step, we provision our solution resources with AWS CloudFormation.

Database with sample data (optional)

This CloudFormation stack works only in AWS Regions where Amazon Aurora Serverless v1 is supported. Complete the following steps to create a database with sample data:

  1. Choose Launch Stack.
  2. On the Create stack page, choose Next.
  3. For Stack name, enter demo-database.
  4. For DBSecurityGroup, choose select the security group for the database (for example, default).
  5. For DBSubnet, choose two or more private subnets to host the database.
  6. For ETLAZ, choose the Availability Zone for ETL jobs. It must match with ETLSubnet.
  7. For ETLSubnet, choose the subnet for the jobs. This must match with ETLAZ.

To find the subnet and corresponding Availability Zone, go to the Amazon Virtual Private Cloud (Amazon VPC) console and look at the columns Subnet ID and Availability Zone.

  1. Choose Next.
  2. On the Configure stack options page, skip the inputs and choose Next.
  3. On the Review page, choose Create stack.
  4. When the stack is complete, go to the Outputs tab and note the value for SecretsARN.

This CloudFormation stack creates the following resources:

  • An Amazon Aurora PostgreSQL-Compatible Edition (Serverless v1, engine version 11.13) database
  • A Secrets Manager secret (DemoDBSecret) storing the connection details to the source database
  • An AWS Glue network connection (rds_vpc) that can communicate with the source database and Amazon S3

Now you can populate the database with sample data. The data is generated by referencing to the sample HR schema.

  1. Open the Amazon RDS Query Editor.
  2. In the Connect to database section, provide the following information:
    • For Database instance, enter demo-<123456789012>.
    • For Database username, connect with a Secrets Manager ARN.
    • For Secrets Manager ARN, enter the ARN from the outputs of the CloudFormation stack.
    • For Database name, enter hr.
  3. Choose Connect to database.
  4. Enter the contents of the SQL file into the editor, then choose Run.

Main stack (required)

This CloudFormation stack works in all AWS Regions.

  1. Choose Launch Stack.
  2. On the Create stack page, choose Next.
  3. For Stack name, enter data-lake-landing.
  4. For BlueprintName, enter a name for your blueprint (default: data-lake-landing).
  5. For S3BucketNamePrefix, enter a prefix (default: data-lake-raw-layer).
  6. Choose Next.
  7. On the Configure stack options page, skip the inputs and choose Next.
  8. On the Review page, select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
  9. Choose Create stack.
  10. When the stack is complete, go to the Outputs tab and note the names of the S3 bucket (for example, data-lake-raw-layer-123456789012-region) and Step Functions workflow (for example, data-lake-landing).

The CloudFormation stack creates the following resources:

  • An S3 bucket as the data lake raw layer
  • A Step Functions workflow (see the definition on the GitHub repo)
  • AWS Identity and Access Management (IAM) roles and policies for the Step Functions workflow to provision AWS Glue resources and AWS Glue job executions.

The GlueExecutionRole is limited to the DemoDBSecret in Secrets Manager. If you need to connect to other databases which has a different endpoint/address or credentials, don’t forget to create new secrets and grant additional permissions to the IAM role or secrets so your AWS Glue jobs can authenticate with the source databases.

Prepare database connections

If you want to use this solution to perform ETL against your existing databases, follow this section. Otherwise, if you have deployed the CloudFormation stack for the database with sample data, jump to the section “Edit the workflow catalog”.

You need to have a running PostgreSQL database ready. To connect to other database engines, you need to customize this solution, particularly the jdbcUrl in the supplied PySpark script.

Create the database secret

To create your Secrets Manager secret, complete the following steps:

  1. On the Secrets Manager console, choose Store a new secret.
  2. For Secret type, choose Credentials for Amazon RDS database or Credentials for other database.
  3. For Credentials, enter the user name and password to your database.
  4. For Encryption key, keep the default AWS Key Management Service (AWS KMS) managed key aws/secretsmanager.
  5. For Database, choose the database instance, or manually input the engine, server address, database name, and port.
  6. Choose Next.
  7. For Secret name, enter a name for your secret (for example, rds-secrets).
  8. Choose Next.
  9. Skip the Configure rotation – optional page and choose Next.
  10. Review the summary and choose Store.

Create the AWS Glue connection

To create your AWS Glue connection, complete the following steps:

  1. On the AWS Glue Studio console, choose Connectors.
  2. Under Connections, choose Create connection.
  3. For Name, enter a name (for example, rds-vpc).
  4. For Connection type, choose Network.
  5. For the VPC, subnet, and security groups (prepared in the prerequisite steps), enter where the ETL jobs run and are able to connect to the source database, Amazon S3, and Secrets Manager.
  6. Choose Create connection.

You’re now ready to configure the rest of the solution.

Edit the workflow catalog

To download the workflow catalog, complete the following steps:

  1. Download and edit the sample file.
  2. If you are using the provided sample database, you must change the values of GlueExecutionRole and DestinationBucketName. If you are using your own databases, you must change all vaules except WorkflowName, JobScheduleType, and ScheduleCronPattern.
{
  "WorkflowList": [
    {
      "WorkflowName": "rds_to_s3_public_regions",
      "GlueExecutionRole": "arn:aws:iam::123456789012:role/AWSGlueServiceRole-data-lake-landing",
      "NetworkConnectionName": "rds-vpc",
      "SecretName": "DemoDBSecret",
      "SourceTable": "public.regions",
      "DestinationBucketName": "data-lake-raw-layer-123456789012-eu-west-1",
      "TransactionTimestampColumnName": "updated_at",
      "JobScheduleType": "Cron",
      "ScheduleCronPattern": "10 21 * * ? *"
    }
  ]
}
  1. Rename the file your_blueprint_name.json and upload it to your S3 bucket (for example, s3://data-lake-raw-layer-123456789012-eu-west-1/data-lake-landing.json).

The example workflow has the JobScheduleType set to Cron. See Time-based schedules for jobs and crawlers for examples setting cron patterns. Alternatively set JobScheduleType to OnDemand.

See blueprint.cfg for the full list of parameters.

The provided workflow catalog JSON file contains job definitions of seven tables: public.regions, public.countries, public.locations, public.departments, public.jobs, public.employees, and public.job_history.

Review the PySpark script (optional)

The sample script performs the following:

  1. Read the updated records from the source table:
jdbc_df = (spark.read.format("jdbc")
           .option("url", jdbcUrl)
           .option("user", secret["username"])
           .option("password", secret["password"])
           .option("query", sql_query)
           .load()
)
  1. Add the date and timestamp columns:
df_withdate = jdbc_df.withColumn("ingestion_timestamp", lit(current_timestamp()))
  1. Write the DataFrame to Amazon S3 as Parquet files.

Prepare the AWS Glue blueprint

Prepare your AWS Glue blueprint with the following steps:

  1. Download the sample file and unzip it in your local computer.
  2. Make any necessary changes to the PySpark script to include your own logic, and compress the three files (blueprint.cfg, jdbc_to_s3.py, layout.py; exclude any folders) as your_blueprint_name.zip (for example, data-lake-landing.zip):
zip data-lake-landing.zip blueprint.cfg jdbc_to_s3.py layout.py
  1. Upload to the S3 bucket (for example, s3://data-lake-raw-layer-123456789012-region/data-lake-landing.zip).

Now you should have two files uploaded to your S3 bucket.

Run the Step Functions workflow to deploy AWS Glue resources

To run the Step Functions workflow, complete the following steps:

  1. On the Step Functions console, select your state machine (data-lake-landing) and choose View details.
  2. Choose Start execution.
  3. Keep the default values in the pop-up.
  4. Choose Start execution.
  5. Wait until the Success step at the bottom turns green.

It’s normal to have some intermediate steps with the status “Caught error.”

When the workflow catalog contains a large number of ETL job entries, you can expect some delays. In our test environment, creating 100 jobs from a clean state can take around 22 minutes; the second run (deleting existing AWS Glue resources and creating 100 jobs) can take around 27 minutes.

Verify the workflow in AWS Glue

To check the workflow, complete the following steps:

  1. On the AWS Glue console, choose Workflows.
  2. Verify that all AWS Glue workflows defined in workflow_config.json are listed.
  3. Select one of the workflows, and on the Action menu, choose Run.
  4. Wait for about 3 minutes (or longer if not using the provided database with sample data), and verify on the Amazon S3 console that new Parquet files are created in your data lake (for example, s3://data-lake-raw-layer-123456789012-region/database/table/ingestion_date=yyyy-mm-dd/).

Step Functions workflow overview

This section describes the major steps in the Step Functions workflow.

Register the AWS Glue blueprint

A blueprint allows you to parameterize a workflow (defining jobs and crawlers), and subsequently generate multiple AWS Glue workflows reusing the same code logic to handle similar data ETL activities. The following diagram illustrates the AWS Glue blueprint registration part of the Step Functions workflow.

The step Glue: CreateBlueprint takes the ZIP archive in Amazon S3 (sample) and registers it for later use.

To understand how to develop a blueprint, see Developing blueprints in AWS Glue.

Parse the workflow catalog and clean up resources

The step S3: ParseGlueWorkflowsConfig triggers the following Map state, and runs a set of steps for each element of an input array.

We set the maximum concurrency to five parallel iterations to lower the chance of exceeding the maximum allowed API request rate (per account per Region). For each ETL job definition, the Step Functions workflow cleans up relevant AWS Glue resources (if they exist), including the workflow, job, and trigger.

For more information on the Map state, refer to Map.

Run the AWS Glue blueprint

Within the Map state, the step Glue: CreateWorkflowFromBlueprint starts an asynchronous process to create the AWS Glue workflow (for each job definition), and the jobs and triggers that the workflow encapsulates.

In this solution, all AWS Glue workflows share the same logic, beginning with a trigger to handle the schedule, followed by a job to run the ETL logic.

As indicated by the step CreateWorkflowFailed, any AWS Glue blueprint creation failure stops the whole Step Functions workflow and marks it with a failed status. Note that no rollback will happen. Fix the errors and rerun the Step Functions workflow. This will not result in duplicated AWS Glue resources and existing ones will be cleaned up in the process.

Limitations

Note the following limitations of this solution:

  • Each run of the Step Functions workflow deletes all relevant AWS Glue jobs defined in the workflow catalog, and creates new jobs with a different (random) suffix. As a result, you will lose the job run history in AWS Glue. The underlying metrics and logs are retained in Amazon CloudWatch.

Clean up

To avoid incurring future charges, perform the following steps:

  1. Disable the schedules of the deployed AWS Glue jobs:
    1. Open the workload configuration file in your S3 bucket (s3://data-lake-raw-layer-123456789012-eu-west-1/data-lake-landing.json) and replace the value of JobScheduleType to OnDemand for all workflow definitions.
    2. Run the Step Functions workflow (data-lake-landing).
    3. Observe that all AWS Glue triggers ending with _starting_trigger have the trigger type On-demand instead of Schedule.
  2. Empty the S3 bucket and delete the CloudFormation stack.
  3. Delete the deployed AWS Glue resources:
    1. All AWS Glue triggers ending with _starting_trigger.
    2. All AWS Glue jobs starting with the WorkflowName defined in the workflow catalog.
    3. All AWS Glue workflows with the WorkflowName defined in the workflow catalog.
    4. AWS Glue blueprints.

Conclusion

AWS Glue blueprints allow data engineers to build and maintain AWS Glue jobs landing data from RDBMS to your data lake at scale.By adopting this standardized and reusable approach, instead of maintaining hundreds of AWS Glue jobs, you now keep track the workflow catalog. When you have new tables to land to your data lake, simply add the entries to your workflow catalog and rerun the Step Functions workflow to deploy resources.

We highly encourage you to customize the blueprints for your multi-step data pipeline (for example, detect and mask sensitive data) and make them available to your organization and the AWS Glue community. To get started, see the Performing complex ETL activities using blueprints and workflows in AWS Glue and the sample blueprints on GitHub. If you have any questions, please leave a comment.


About the Authors

Moustafa Mahmoud is a Solutions Architect of AWS Data Lab with a passion for data integration, data analysis, machine learning, and BI. Moustafa helps customers convert their ideas to a production-ready data product on AWS. He has over 10 years of experience as a data engineer, machine learning practitioner, and software developer. In his spare time, Moustafa loves exploring nature, reading, and spending time with friends and family.

Corvus Lee is a Solutions Architect of AWS Data Lab. He enjoys all kinds of data-related discussions, and helps customers build MVPs using AWS Databases, Analytics, and Machine Learning services.

IAM Access Analyzer makes it simpler to author and validate role trust policies

Post Syndicated from Mathangi Ramesh original https://aws.amazon.com/blogs/security/iam-access-analyzer-makes-it-simpler-to-author-and-validate-role-trust-policies/

AWS Identity and Access Management (IAM) Access Analyzer provides many tools to help you set, verify, and refine permissions. One part of IAM Access Analyzer—policy validation—helps you author secure and functional policies that grant the intended permissions. Now, I’m excited to announce that AWS has updated the IAM console experience for role trust policies to make it simpler for you to author and validate the policy that controls who can assume a role. In this post, I’ll describe the new capabilities and show you how to use them as you author a role trust policy in the IAM console.

Overview of changes

A role trust policy is a JSON policy document in which you define the principals that you trust to assume the role. The principals that you can specify in the trust policy include users, roles, accounts, and services. The new IAM console experience provides the following features to help you set the right permissions in the trust policy:

  • An interactive policy editor prompts you to add the right policy elements, such as the principal and the allowed actions, and offers context-specific documentation.
  • As you author the policy, IAM Access Analyzer runs over 100 checks against your policy and highlights issues to fix. This includes new policy checks specific to role trust policies, such as a check to make sure that you’ve formatted your identity provider correctly. These new checks are also available through the IAM Access Analyzer policy validation API.
  • Before saving the policy, you can preview findings for the external access granted by your trust policy. This helps you review external access, such as access granted to a federated identity provider, and confirm that you grant only the intended access when you create the policy. This functionality was previously available through the APIs, but now it’s also available in the IAM console.

In the following sections, I’ll walk you through how to use these new features.

Example scenario

For the walkthrough, consider the following example, which is illustrated in Figure 1. You are a developer for Example Corp., and you are working on a web application. You want to grant the application hosted in one account—the ApplicationHost account—access to data in another account—the BusinessData account. To do this, you can use an IAM role in the BusinessData account to grant temporary access to the application through a role trust policy. You will grant a role in the ApplicationHost account—the PaymentApplication role—to access the BusinessData account through a role—the ApplicationAccess role. In this example, you create the ApplicationAccess role and grant cross-account permissions through the trust policy by using the new IAM console experience that helps you set the right permissions.

Figure 1: Visual explanation of the scenario

Figure 1: Visual explanation of the scenario

Create the role and grant permissions through a role trust policy with the policy editor

In this section, I will show you how to create a role trust policy for the ApplicationAccess role to grant the application access to the data in your account through the policy editor in the IAM console.

To create a role and grant access

  1. In the BusinessData account, open the IAM console, and in the left navigation pane, choose Roles.
  2. Choose Create role, and then select Custom trust policy, as shown in Figure 2.
    Figure 2: Select "Custom trust policy" when creating a role

    Figure 2: Select “Custom trust policy” when creating a role

  3. In the Custom trust policy section, for 1. Add actions for STS, select the actions that you need for your policy. For example, to add the action sts:AssumeRole, choose AssumeRole.
    Figure 3: JSON role trust policy

    Figure 3: JSON role trust policy

  4. For 2. Add a principal, choose Add to add a principal.
  5. In the Add principal box, for Principal type, select IAM roles. This populates the ARN field with the format of the role ARN that you need to add to the policy, as shown in Figure 4.
    Figure 4: Add a principal to your role trust policy

    Figure 4: Add a principal to your role trust policy

  6. Update the role ARN template with the actual account and role information, and then choose Add principal. In our example, the account is ApplicationHost with an AWS account number of 111122223333, and the role is PaymentApplication role. Therefore, the role ARN is arn:aws:iam:: 111122223333: role/PaymentApplication. Figure 5 shows the role trust policy with the action and principal added.
    Figure 5: Sample role trust policy

    Figure 5: Sample role trust policy

  7. (Optional) To add a condition, for 3. Add a condition, choose Add, and then complete the Add condition box according to your needs.

Author secure policies by reviewing policy validation findings

As you author the policy, you can see errors or warnings related to your policy in the policy validation window, which is located below the policy editor in the console. With this launch, policy validation in IAM Access Analyzer includes 13 new checks focused on the trust relationship for the role. The following are a few examples of these checks and how to address them:

  • Role trust policy unsupported wildcard in principal – you can’t use a * in your role trust policy.
  • Invalid federated principal syntax in role trust policy – you need to fix the format of the identity provider.
  • Missing action for condition key – you need to add the right action for a given condition, such as the sts:TagSession when there are session tag conditions.

For a complete list of checks, see Access Analyzer policy check reference.

To review and fix policy validation findings

  1. In the policy validation window, do the following:
    • Choose the Security tab to check if your policy is overly permissive.
    • Choose the Errors tab to review any errors associated with the policy.
    • Choose the Warnings tab to review if aspects of the policy don’t align with AWS best practices.
    • Choose the Suggestions tab to get recommendations on how to improve the quality of your policy.
    Figure 6: Policy validation window in IAM Access Analyzer with a finding for your policy

    Figure 6: Policy validation window in IAM Access Analyzer with a finding for your policy

  2. For each finding, choose Learn more to review the documentation associated with the finding and take steps to fix it. For example, Figure 6 shows the error Mismatched Action For Principal. To fix the error, remove the action sts:AssumeRoleWithWebIdentity.

Preview external access by reviewing cross-account access findings

IAM Access Analyzer also generates findings to help you assess if a policy grants access to external entities. You can review the findings before you create the policy to make sure that the policy grants only intended access. To preview the findings, you create an analyzer and then review the findings.

To preview findings for external access

  1. Below the policy editor, in the Preview external access section, choose Go to Access Analyzer, as shown in Figure 7.

    Note: IAM Access Analyzer is a regional service, and you can create a new analyzer in each AWS Region where you operate. In this situation, IAM Access Analyzer looks for an analyzer in the Region where you landed on the IAM console. If IAM Access Analyzer doesn’t find an analyzer there, it asks you to create an analyzer.

    Figure 7: Preview external access widget without an analyzer

    Figure 7: Preview external access widget without an analyzer

  2. On the Create analyzer page, do the following to create an analyzer:
    • For Name, enter a name for your analyzer.
    • For Zone of trust, select the correct account.
    • Choose Create analyzer.
    Figure 8: Create an analyzer to preview findings

    Figure 8: Create an analyzer to preview findings

  3. After you create the analyzer, navigate back to the role trust policy for your role to review the external access granted by this policy. The following figure shows that external access is granted to PaymentApplication.
    Figure 9: Preview finding

    Figure 9: Preview finding

  4. If the access is intended, you don’t need to take any action. In this example, I want the PaymentApplication role in the ApplicationHost account to assume the role that I’m creating.
  5. If the access is unintended, resolve the finding by updating the role ARN information.
  6. Select Next and grant the required IAM permissions for the role.
  7. Name the role ApplicationAccess, and then choose Save to save the role.

Now the application can use this role to access the BusinessData account.

Conclusion

By using the new IAM console experience for role trust policies, you can confidently author policies that grant the intended access. IAM Access Analyzer helps you in your least-privilege journey by evaluating the policy for potential issues to make it simpler for you to author secure policies. IAM Access Analyzer also helps you preview external access granted through the trust policy to help ensure that the granted access is intended. To learn more about how to preview IAM Access Analyzer cross-account findings, see Preview access in the documentation. To learn more about IAM Access Analyzer policy validation checks, see Access Analyzer policy validation. These features are also available through APIs.

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 at AWS IAM re:Post or contact AWS Support.

Mathangi Ramesh

Mathangi Ramesh

Mathangi is the product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

Automate ETL jobs between Amazon RDS for SQL Server and Azure Managed SQL using AWS Glue Studio

Post Syndicated from Daniel Maldonado original https://aws.amazon.com/blogs/big-data/automate-etl-jobs-between-amazon-rds-for-sql-server-and-azure-managed-sql-using-aws-glue-studio/

Nowadays many customers are following a multi-cloud strategy. They might choose to use various cloud-managed services, such as Amazon Relational Database Service (Amazon RDS) for SQL Server and Azure SQL Managed Instances, to perform data analytics tasks, but still use traditional extract, transform, and load (ETL) tools to integrate and process the data. However, traditional ETL tools may require you to develop custom scripts, which makes ETL automation difficult.

In this post, I show you how to automate ETL jobs between Amazon RDS for SQL Server and Azure SQL Managed Instances using AWS Glue Studio, which is part of AWS Glue, a fully managed serverless integration service. AWS Glue Studio has a graphical interface that makes it easy to create, run, and monitor ETL jobs, and you can create a schedule to run your jobs at specific times.

Solution overview

To move data from one database to another, there are different services available either on-premise or in the cloud, varied by bandwidth limits, ongoing changes (CDC), schema and table modifications, and other features. Beyond that, we need to apply advanced data transformations, monitor, and automate the ETL jobs. This is where AWS Glue Studio can help us facilitate these activities.

As shown in the following diagram, we use AWS Glue Studio as the middleware to pull data from the source database (in this case an Azure SQL Managed Instance), then create and automate the ETL job using one of the pre-built transformations in AWS Glue Studio. Finally, we load the data to the target database (in this case an RDS for SQL Server instance).

The solution workflow consists of the following steps:

  1. Create connections for the source and target databases.
  2. Create and run AWS Glue crawlers.
  3. Create and run an ETL job that transforms the data and loads it from source to target.
  4. Schedule the ETL job to run automatically.
  5. Monitor the ETL job.

Prerequisites

Complete the following prerequisite steps:

  1. Install SQL Server Management Studio (SSMS) or an equivalent client tool.
  2. Set up a VPN connection between Amazon Virtual Private Cloud (Amazon VPC) and the Azure private subnet.
  3. Create a security group for AWS Glue ENI in your VPC.
  4. Create an AWS Identity and Access Management (IAM) role for AWS Glue. For instructions, refer to Setting up IAM permissions for AWS Glue.
  5. Open the appropriate firewall ports in the Azure private subnet.
  6. Create a source database table (Azure SQL Managed Instance). You can deploy the Azure database instance using the following QuickStart. For testing purposes, I import the public AdventureWorks sample database and use the dbo.Employee table. See the following code:
    #Query table
    SELECT * FROM [AdventureWorksLT2019].[dbo].[Employee]

  7. Create the target database table (Amazon RDS for SQL Server). To deploy the RDS instance, refer to Create and Connect to a Microsoft SQL Server Database with Amazon RDS. You can create an empty database and table with the following statements. This is the table where the data coming from Azure will be stored.
#Create database
CREATE DATABASE AdventureWorksonRDS;
#Create table
CREATE TABLE Employee
(EmpID INT NOT NULL,
EmpName VARCHAR(50) NOT NULL,
Designation VARCHAR(50) NULL,
Department VARCHAR(50) NULL,
JoiningDate DATETIME NULL,
CONSTRAINT [PK_Employee] PRIMARY KEY CLUSTERED (EmpID)

#Query table
SELECT * FROM [AdventureWorksonRDS].[dbo].[Employee]

Create connections

The first step is to populate our AWS Glue Data Catalog with the schema information coming from our source and target data sources.

To do that, we first create connections. A connection is a Data Catalog object that stores connection information for a particular data store. Connections store login credentials, URI strings, VPC information, and more. Creating connections in the Data Catalog saves the effort of having to specify the connection details every time you create a crawler or job.

Create a connection for Azure SQL Managed Instance

To create the connection to our source database, complete the following steps:

  1. On the AWS Glue console, choose AWS Glue Studio.
  2. In the navigation pane of the AWS Glue Studio console, choose Connectors.
  3. Choose Create connection.
  4. For Name, enter AzureSQLManaged.
  5. For Connection type, choose JDBC.
  6. For JDBC URL, use the SQL Server syntax jdbc:protocol://host:port;database=db_name.

You can find the host and database name on the Azure SQL Managed Instance service console, on the Overview page.For this specific example, we use the following information for our Azure SQL Instance:

    • Protocol – sqlserver
    • Host – adi-qa-sql-managed-instance-test.public.xxxxxxxxxxxx.database.windows.net
    • Port – 3342
    • Database name – AdventureWorksLT2019

Enter your user name and password.
Choose Create connection.

Create a connection for Amazon RDS for SQL Server

To create a connection for our target data source, complete the following steps:

  1. On the AWS Glue Studio console, choose Connectors in the navigation pane.
  2. Choose Create connection.
  3. For Name, enter AWSRDSforSQL.
  4. For Connection type, choose Amazon RDS.
  5. For Database engine, choose Microsoft SQL Server.
  6. For Database instances, choose your RDS DB instance.
  7. For Database name, enter AdventureWorksonRDS.
  8. Enter your user name and password.
  9. Choose Create connection.

You can now see the two connections created in the Connections section.

Create and run AWS Glue crawlers

You can use a crawler to populate the AWS Glue Data Catalog with tables. This is the most common method used by most AWS Glue users. A crawler can crawl multiple data stores in a single run. Upon completion, it updates the Data Catalog with the tables it found. The ETL jobs that you define in AWS Glue use these Data Catalog tables as sources and targets.

Create a crawler for Azure SQL Managed Instance

To create a crawler for our source database, complete the following steps:

  1. On the AWS Glue console, choose Crawlers in the navigation pane.
  2. Choose Create crawler.
  3. If the data hasn’t been mapped into an AWS Glue table, select Not yet and choose Add a data source.
  4. For Data source¸ choose JDBC.
  5. For Connection, choose AzureSQLManaged.
  6. For Include path, specify the path of the database including the schema: AdventureWorksLT2019/dbo/%.
  7. Choose Add a JDBC data source.                                                                                     
  8. Choose Next.
  9. Choose the IAM role created as part of the prerequisites and choose Next.
  10. Choose Add database to create the target database in the AWS Glue Data Catalog.
  11. For Name, enter azuresqlmanaged_db.
  12. Choose Create database.
  13. For Target database, choose azuresqlmanaged_db.
  14. Choose Next.
  15. Review if everything looks correct and choose Create crawler.

Create a crawler for Amazon RDS for SQL Server

Repeat the crawler creation steps to create the crawler for the target RDS for SQL Server database, using the following information:

  • Crawler name – AmazonRDSSQL_Crawler
  • Data source – JDBC
  • Connection – AWSRDSforSQL
  • Include path – AdventureWorksonRDS/dbo/%
  • IAM role – AWSGlueServiceRoleDefault
  • Database name – amazonrdssql_db

Run the crawlers

Now it’s time to run the crawlers.

  1. On the AWS Glue console, choose Crawlers in the navigation pane.
  2. Select the crawlers you created and choose Run.
  3. When the crawler is complete, choose Databases in the navigation pane. Here you can find the databases discovered by the crawler.
  4. Choose Tables in the navigation pane and explore the tables discovered by the crawler that correctly identified the data type as SQL Server.
  5. Choose the table adventureworkslt2019_dbo_employee and review the schema created for the data source.

Create and run an ETL job

Now that we have crawled our source and target databases, and we have the data in the AWS Glue Data Catalog, we can create an ETL job to load and transform this data.

  1. On the AWS Glue Studio console, choose Jobs in the navigation pane.
  2. Select Visual with a blank canvas to use a visual interface to create our ETL jobs.
  3. Choose Create.
  4. On the Source menu, choose AWS Glue Data Catalog.
  5. On the Data source properties tab, specify the database and table (for this post, azuresqlmanaged_db and adventureworkslt2019_dbo_employee).
  6. On the Transform menu, choose Apply mapping to map the source fields to the target database.
  7. On the Transform tab, you can see the data fields to be loaded, and you even can drop some of them if needed.
  8. On the Target menu, choose AWS Glue Data Catalog.
  9. On the Data target properties tab, choose the database and table where you want to load the transformed data (for this post, amazonrdssql_db and adventureworksrds_dbo_employee).
  10. On the Job details tab, for Name, enter ETL_Azure_to_AWS.
  11. For IAM Role, choose the appropriate role.
  12. Choose Save.
  13. Choose Run to run the job.

If the ETL job ran successfully, it should map the data from the source database (Azure SQL) to the target database (Amazon RDS for SQL). To confirm it, you can connect to the target database using SQL Server Management Studio (SSMS), and query the empty database/table AdventureWorksonRDS/dbo.Employee. It should have the data coming from the Azure SQL Managed Instance.

#Query table
SELECT * FROM [AdventureWorksonRDS].[dbo].[Employee]

Schedule your ETL job

In AWS Glue Studio, you can create a schedule to have your jobs run at specific times. This will reimport the full dataset and reference the use of bookmarks to do incremental loads. You can schedule your ETL jobs on an hourly, daily, weekly, monthly, or custom basis, depending on your needs. To schedule a job, complete the following steps:

  1. On the AWS Glue Studio, navigate to the job you created.
  2. On the Schedules tab, choose Create schedule.
  3. For Name, enter a name (for example, dbo_employee_daily_load).
  4. Choose your preferred frequency, start hour, and minute of the hour. For this post, we schedule it daily at 3:00 UTC.
  5. For Description, enter an optional description.
  6. Choose Create schedule.

Confirm on the Schedules tab that the schedule was created and activated successfully.

You have now automated your ETL job to run at your desired frequency.

Monitor your ETL job

The job monitoring dashboard provides an overall summary of the job runs, with totals for the jobs with a status of Running, Canceled, Success, or Failed.

The Runs tab shows the jobs for the specified date range and filters. You can filter the jobs on additional criteria, such as status, worker type, job type, and job name.

Conclusion

In this post, I went through the steps to automate ETL jobs using AWS Glue Studio, which is a user-friendly graphical interface to perform data integration tasks such as discovering and extracting data from various sources; enriching, cleaning, normalizing, and combining data; and loading and organizing data in databases, data warehouses, and data lakes. You can easily find and access this data using the AWS Glue Data Catalog. Data engineers and ETL developers can visually create, run, and monitor ETL workflows with a few clicks in AWS Glue Studio.


About the author

Daniel Maldonado is an AWS Solutions Architect, specializing in Microsoft workloads and big data technologies, and focused on helping customers migrate their applications and data to AWS. Daniel has over 13 years of experience working with information technologies and enjoys helping clients reap the benefits of running their workloads in the cloud.

Announcing an update to IAM role trust policy behavior

Post Syndicated from Mark Ryland original https://aws.amazon.com/blogs/security/announcing-an-update-to-iam-role-trust-policy-behavior/

AWS Identity and Access Management (IAM) is changing an aspect of how role trust policy evaluation behaves when a role assumes itself. Previously, roles implicitly trusted themselves from a role trust policy perspective if they had identity-based permissions to assume themselves. After receiving and considering feedback from customers on this topic, AWS is changing role assumption behavior to always require self-referential role trust policy grants. This change improves consistency and visibility with regard to role behavior and privileges. This change allows customers to create and understand role assumption permissions in a single place (the role trust policy) rather than two places (the role trust policy and the role identity policy). It increases the simplicity of role trust permission management: “What you see [in the trust policy] is what you get.”

Therefore, beginning today, for any role that has not used the identity-based behavior since June 30, 2022, a role trust policy must explicitly grant permission to all principals, including the role itself, that need to assume it under the specified conditions. Removal of the role’s implicit self-trust improves consistency and increases visibility into role assumption behavior.

Most AWS customers will not be impacted by the change at all. Only a tiny percentage (approximately 0.0001%) of all roles are involved. Customers whose roles have recently used the previous implicit trust behavior are being notified, beginning today, about those roles, and may continue to use this behavior with those roles until February 15, 2023, to allow time for making the necessary updates to code or configuration. Or, if these customers are confident that the change will not impact them, they can opt out immediately by substituting in new roles, as discussed later in this post.

The first part of this post briefly explains the change in behavior. The middle sections answer practical questions like: “why is this happening?,” “how might this change impact me?,” “which usage scenarios are likely to be impacted?,” and “what should I do next?” The usage scenario section is important because it shows that, based on our analysis, the self-assuming role behavior exhibited by code or human users is very likely to be unnecessary and counterproductive. Finally, for security professionals interested in better understanding the reasons for the old behavior, the rationale for the change, as well as its possible implications, the last section reviews a number of core IAM concepts and digs in to additional details.

What is changing?

Until today, an IAM role implicitly trusted itself. Consider the following role trust policy attached to the role named RoleA in AWS account 123456789012.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": "arn:aws:iam::123456789012:role/RoleB"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

This role trust policy grants role assumption access to the role named RoleB in the same account. However, if the corresponding identity-based policy for RoleA grants the sts:AssumeRole action with respect to itself, then RoleA could also assume itself. Therefore, there were actually two roles that could assume RoleA: the explicitly permissioned RoleB, and RoleA, which implicitly trusted itself as a byproduct of the IAM ownership model (explained in detail in the final section). Note that the identity-based permission that RoleA must have to assume itself is not required in the case of RoleB, and indeed an identity-based policy associated with RoleB that references other roles is not sufficient to allow RoleB to assume them. The resource-based permission granted by RoleA’s trust policy is both necessary and sufficient to allow RoleB to assume RoleA.

Although earlier we summarized this behavior as “implicit self-trust,” the key point here is that the ability of Role A to assume itself is not actually implicit behavior. The role’s self-referential permission had to be explicit in one place or the other (or both): either in the role’s identity-based policy (perhaps based on broad wildcard permissions), or its trust policy. But unlike the case with other principals and role trust, an IAM administrator would have to look in two different policies to determine whether a role could assume itself.

As of today, for any new role, or any role that has not recently assumed itself while relying on the old behavior, IAM administrators must modify the previously shown role trust policy as follows to allow RoleA to assume itself, regardless of the privileges granted by its identity-based policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": [
                    "arn:aws:iam::123456789012:role/RoleB",
                    "arn:aws:iam::123456789012:role/RoleA"
                ]
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

This change makes role trust behavior clearer and more consistent to understand and manage, whether directly by humans or as embodied in code.

How might this change impact me?

As previously noted, most customers will not be impacted by the change at all. For those customers who do use the prior implicit trust grant behavior, AWS will work with you to eliminate your usage prior to February 15, 2023. Here are more details for the two cases of customers who have not used the behavior, and those who have.

If you haven’t used the implicit trust behavior since June 30, 2022

Beginning today, if you have not used the old behavior for a given role at any time since June 30, 2022, you will now experience the new behavior. Those existing roles, as well as any new roles, will need an explicit reference in their own trust policy in order to assume themselves. If you have roles that are used only very occasionally, such as once per quarter for a seldom-run batch process, you should identify those roles and if necessary either remove the dependency on the old behavior or update their role trust policies to include the role itself prior to their next usage (see the second sample policy above for an example).

If you have used the implicit trust behavior since June 30, 2022

If you have a role that has used the implicit trust behavior since June 30, 2022, then you will continue to be able to do so with that role until February 15, 2023. AWS will provide you with notice referencing those roles beginning today through your AWS Health Dashboard and will also send an email with the relevant information to the account owner and security contact. We are allowing time for you to make any necessary changes to your existing processes, code, or configurations to prepare for removal of the implicit trust behavior. If you can’t change your processes or code, you can continue to use the behavior by making a configuration change—namely, by updating the relevant role trust policies to reference the role itself. On the other hand, you can opt out of the old behavior at any time by creating a new role with a different Amazon Resource Name (ARN) with the desired identity-based and trust-policy-based permissions and substituting it for any older role that was identified as using the implicit trust behavior. (The new role will not be allow-listed, because the allow list is based on role ARNs.) You can also modify an existing allow-listed role’s trust policy to explicitly deny access to itself. See the “What should I do next?” section for more information.

Notifications and retirement

As we previously noted, starting today, accounts with existing roles that use the implicit self-assume role assumption behavior will be notified of this change by email and through their AWS Health Dashboard. Those roles have been allow-listed, and so for now their behavior will continue as before. After February 15, 2023, the old behavior will be retired for all roles and all accounts. IAM Documentation has been updated to make clear the new behavior.

After the old behavior is retired from the allow-listed roles and accounts, role sessions that make self-referential role assumption calls will fail with an Access Denied error unless the role’s trust policy explicitly grants the permission directly through a role ARN. Another option is to grant permission indirectly through an ARN to the root principal in the trust policy that acts as a delegation of privilege management, after which permission grants in identity-based policies determine access, similar to the typical cross-account case.

Which usage scenarios are likely to be impacted?

Users often attach an IAM role to an Amazon Elastic Compute Cloud (Amazon EC2) instance, an Amazon Elastic Container Service (Amazon ECS) task, or AWS Lambda function. Attaching a role to one of these runtime environments enables workloads to use short-term session credentials based on that role. For example, when an EC2 instance is launched, AWS automatically creates a role session and assigns it to the instance. An AWS best practice is for the workload to use these credentials to issue AWS API calls without explicitly requesting short-term credentials through sts:AssumeRole calls.

However, examples and code snippets commonly available on internet forums and community knowledge sharing sites might incorrectly suggest that workloads need to call sts:AssumeRole to establish short-term sessions credentials for operation within those environments.

We analyzed AWS Security Token Service (AWS STS) service metadata about role self-assumption in order to understand the use cases and possible impact of the change. What the data shows is that in almost all cases this behavior is occurring due to unnecessarily reassuming the role in an Amazon EC2, Amazon ECS, Amazon Elastic Kubernetes Services (EKS), or Lambda runtime environment already provided by the environment. There are two exceptions, discussed at the end of this section under the headings, “self-assumption with a scoped-down policy” and “assuming a target compute role during development.”

There are many variations on this theme, but overall, most role self-assumption occurs in scenarios where the person or code is unnecessarily reassuming the role that the code was already running as. Although this practice and code style can still work with a configuration change (by adding an explicit self-reference to the role trust policy), the better practice will almost always be to remove this unnecessary behavior or code from your AWS environment going forward. By removing this unnecessary behavior, you save CPU, memory, and network resources.

Common mistakes when using Amazon EKS

Some users of the Amazon EKS service (or possibly their shell scripts) use the command line interface (CLI) command aws eks get-token to obtain an authentication token for use in managing a Kubernetes cluster. The command takes as an optional parameter a role ARN. That parameter allows a user to assume another role other than the one they are currently using before they call get-token. However, the CLI cannot call that API without already having an IAM identity. Some users might believe that they need to specify the role ARN of the role they are already using. We have updated the Amazon EKS documentation to make clear that this is not necessary.

Common mistakes when using AWS Lambda

Another example is the use of an sts:AssumeRole API call from a Lambda function. The function is already running in a preassigned role provided by user configuration within the Lambda service, or else it couldn’t successfully call any authenticated API action, including sts:AssumeRole. However, some Lambda functions call sts:AssumeRole with the target role being the very same role that the Lambda function has already been provided as part of its configuration. This call is unnecessary.

AWS Software Development Kits (SDKs) all have support for running in AWS Lambda environments and automatically using the credentials provided in that environment. We have updated the Lambda documentation to make clear that such STS calls are unnecessary.

Common mistakes when using Amazon ECS

Customers can associate an IAM role with an Amazon ECS task to give the task AWS credentials to interact with other AWS resources.

We detected ECS tasks that call sts:AssumeRole on the same role that was provided to the ECS task. Amazon ECS makes the role’s credentials available inside the compute resources of the ECS task, whether on Amazon EC2 or AWS Fargate, and these credentials can be used to access AWS services or resources as the IAM role associated with the ECS talk, without being called through sts:AssumeRole. AWS handles renewing the credentials available on ECS tasks before the credentials expire. AWS STS role assumption calls are unnecessary, because they simply create a new set of the same temporary role session credentials.

AWS SDKs all have support for running in Amazon ECS environments and automatically using the credentials provided in that ECS environment. We have updated the Amazon ECS documentation to make clear that calling sts:AssumeRole for an ECS task is unnecessary.

Common mistakes when using Amazon EC2

Users can configure an Amazon EC2 instance to contain an instance profile. This instance profile defines the IAM role that Amazon EC2 assigns the compute instance when it is launched and begins to run. The role attached to the EC2 instance enables your code to send signed requests to AWS services. Without this attached role, your code would not be able to access your AWS resources (nor would it be able to call sts:AssumeRole). The Amazon EC2 service handles renewing these temporary role session credentials that are assigned to the instance before they expire.

We have observed that workloads running on EC2 instances call sts:AssumeRole to assume the same role that is already associated with the EC2 instance and use the resulting role-session for communication with AWS services. These role assumption calls are unnecessary, because they simply create a new set of the same temporary role session credentials.

AWS SDKs all have support for running in Amazon EC2 environments and automatically using the credentials provided in that EC2 environment. We have updated the Amazon EC2 documentation to make clear that calling sts:AssumeRole for an EC2 instance with a role assigned is unnecessary.

For information on creating an IAM role, attaching that role to an EC2 instance, and launching an instance with an attached role, see “IAM roles for Amazon EC2” in the Amazon EC2 User Guide.

Other common mistakes

If your use case does not use any of these AWS execution environments, you might still experience an impact from this change. We recommend that you examine the roles in your account and identify scenarios where your code (or human use through the AWS CLI) results in a role assuming itself. We provide Amazon Athena and AWS CloudTrail Lake queries later in this post to help you locate instances where a role assumed itself. For each instance, you can evaluate whether a role assuming itself is the right operation for your needs.

Self-assumption with a scoped-down policy

The first pattern we have observed that is not a mistake is the use of self-assumption combined with a scoped-down policy. Some systems use this approach to provide different privileges for different use cases, all using the same underlying role. Customers who choose to continue with this approach can do so by adding the role to its own trust policy. While the use of scoped-down policies and the associated least-privilege approach to permissions is a good idea, we recommend that customers switch to using a second generic role and assume that role along with the scoped-down policy rather than using role self-assumption. This approach provides more clarity in CloudTrail about what is happening, and limits the possible iterations of role assumption to one round, since the second role should not be able to assume the first. Another possible approach in some cases is to limit subsequent assumptions is by using an IAM condition in the role trust policy that is no longer satisfied after the first role assumption. For example, for Lambda functions, this would be done by a condition checking for the presence of the “lambda:SourceFunctionArn” property; for EC2, by checking for presence of “ec2:SourceInstanceARN.”

Assuming an expected target compute role during development

Another possible reason for role self-assumption may result from a development practice in which developers attempt to normalize the roles that their code is running in between scenarios in which role credentials are not automatically provided by the environment, and scenarios where they are. For example, imagine a developer is working on code that she expects to run as a Lambda function, but during development is using her laptop to do some initial testing of the code. In order to provide the same execution role as is expected later in product, the developer might configure the role trust policy to allow assumption by a principal readily available on the laptop (an IAM Identity Center role, for example), and then assume the expected Lambda function execution role when the code is initializing. The same approach could be used on a build and test server. Later, when the code is deployed to Lambda, the actual role is already available and in use, but the code need not be modified in order to provide the same post-role-assumption behavior that existing outside of Lambda: the unmodified code can automatically assume what is in this case the same role, and proceed. While this approach is not illogical, as with the scope-down policy case we recommend that customers configure distinct roles for assumption both in development and test environments as well as later production environments. Again, this approach provides more clarity in CloudTrail about what is happening, and limits the possible iterations of role assumption to one round, since the second role should not be able to assume the first.

What should I do next?

If you receive an email or AWS Health Dashboard notification for an account, we recommend that you review your existing role trust policies and corresponding code. For those roles, you should remove the dependency on the old behavior, or if you can’t, update those role trust policies with an explicit self-referential permission grant. After the grace period expires on February 15, 2023, you will no longer be able to use the implicit self-referential permission grant behavior.

If you currently use the old behavior and need to continue to do so for a short period of time in the context of existing infrastructure as code or other automated processes that create new roles, you can do so by adding the role’s ARN to its own trust policy. We strongly encourage you to treat this as a temporary stop-gap measure, because in almost all cases it should not be necessary for a role to be able to assume itself, and the correct solution is to change the code that results in the unnecessary self-assumption. If for some reason that self-service solution is not sufficient, you can reach out to AWS Support to seek an accommodation of your use case for new roles or accounts.

If you make any necessary code or configuration changes and want to remove roles that are currently allow-listed, you can also ask AWS Support to remove those roles from the allow list so that their behavior follows the new model. Or, as previously noted, you can opt out of the old behavior at any time by creating a new role with a different ARN that has the desired identity-based and trust-policy–based permissions and substituting it for the allow-listed role. Another stop-gap type of option is to add an explicit deny that references the role to its own trust policy.

If you would like to understand better the history of your usage of role self-assumption in a given account or organization, you can follow these instructions on querying CloudTrail data with Athena and then use the following Athena query against your account or organization CloudTrail data, as stored in Amazon Simple Storage Services (Amazon S3). The results of the query can help you understand the scenarios and conditions and code involved. Depending on the size of your CloudTrail logs, you may need to follow the partitioning instructions to query subsets of your CloudTrail logs sequentially. If this query yields no results, the role self-assumption scenario described in this blog post has never occurred within the analyzed CloudTrail dataset.

SELECT eventid, eventtime, userIdentity.sessioncontext.sessionissuer.arn as RoleARN, split_part(userIdentity.principalId, ':', 2) as RoleSessionName from cloudtrail_logs t CROSS JOIN UNNEST(t.resources) unnested (resources_entry) where eventSource = 'sts.amazonaws.com' and eventName = 'AssumeRole' and userIdentity.type = 'AssumedRole' and errorcode IS NULL and substr(userIdentity.sessioncontext.sessionissuer.arn,12) = substr(unnested.resources_entry.ARN,12)

As another option, you can follow these instructions to set up CloudTrail Lake to perform a similar analysis. CloudTrail Lake allows richer, faster queries without the need to partition the data. As of September 20, 2022, CloudTrail Lake now supports import of CloudTrail logs from Amazon S3. This allows you to perform a historical analysis even if you haven’t previously enabled CloudTrail Lake. If this query yields no results, the scenario described in this blog post has never occurred within the analyzed CloudTrail dataset.

SELECT eventid, eventtime, userIdentity.sessioncontext.sessionissuer.arn as RoleARN, userIdentity.principalId as RoleIdColonRoleSessionName from $EDS_ID where eventSource = 'sts.amazonaws.com' and eventName = 'AssumeRole' and userIdentity.type = 'AssumedRole' and errorcode IS NULL and userIdentity.sessioncontext.sessionissuer.arn = element_at(resources,1).arn

Understanding the change: more details

To better understand the background of this change, we need to review the IAM basics of identity-based policies and resource-based policies, and then explain some subtleties and exceptions. You can find additional overview material in the IAM documentation.

The structure of each IAM policy follows the same basic model: one or more statements with an effect (allow or deny), along with principals, actions, resources, and conditions. Although the identity-based and resource-based policies share the same basic syntax and semantics, the former is associated with a principal, the latter with a resource. The main difference between the two is that identity-based policies do not specify the principal, because that information is supplied implicitly by associating the policy with a given principal. On the other hand, resource policies do not specify an arbitrary resource, because at least the primary identifier of the resource (for example, the bucket identifier of an S3 bucket) is supplied implicitly by associating the policy with that resource. Note that an IAM role is the only kind of AWS object that is both a principal and a resource.

In most cases, access to a resource within the same AWS account can be granted by either an identity-based policy or a resource-based policy. Consider an Amazon S3 example. An identity-based policy attached to an IAM principal that allows the s3:GetObject action does not require an equivalent grant in the S3 bucket resource policy. Conversely, an s3:GetObject permission grant in a bucket’s resource policy is all that is needed to allow a principal in the same account to call the API with respect to that bucket; an equivalent identity-based permission is not required. Either the identity-based policy or the resource-based policy can grant the necessary permission. For more information, see IAM policy types: How and when to use them.

However, in order to more tightly govern access to certain security-sensitive resources, such as AWS Key Management Service (AWS KMS) keys and IAM roles, those resource policies need to grant access to the IAM principal explicitly, even within the same AWS account. A role trust policy is the resource policy associated with a role that specifies which IAM principals can assume the role by using one of the sts:AssumeRole* API calls. For example, in order for RoleB to assume RoleA in the same account, whether or not RoleB’s identity-based policy explicitly allows it to assume RoleA, RoleA’s role trust policy must grant access to RoleB. Within the same account, an identity-based permission by itself is not sufficient to allow assumption of a role. On the other hand, a resource-based permission—a grant of access in the role trust policy—is sufficient. (Note that it’s possible to construct a kind of hybrid permission to a role by using both its resource policy and other identity-based policies. In that case, the role trust policy grants permission to the root principal ARN; after that, the identity-based policy of a principal in that account would need to explicitly grant permission to assume that role. This is analogous to the typical cross-account role trust scenario.)

Until now, there has been a nonintuitive exception to these rules for situations where a role assumes itself. Since a role is both a principal (potentially with an identity-based policy) and a resource (with a resource-based policy), it is in the unique position of being both a subject and an object within the IAM system, as well as being an object owned by itself rather than its containing account. Due to this ownership model, roles with identity-based permission to assume themselves implicitly trusted themselves as resources, and vice versa. That is to say, roles that had the privilege as principals to assume themselves implicitly trusted themselves as resources, without an explicit self-referential Allow in the role trust policy. Conversely, a grant of permission in the role trust policy was sufficient regardless of whether there was a grant in the same role’s identity-based policy. Thus, in the self-assumption case, roles behaved like most other resources in the same account: only a single permission was required to allow role self-assumption, either on the identity side or the resource side of their dual-sided nature. Because of a role’s implicit trust of itself as a resource, the role’s trust policy—which might otherwise limit assumption of the role with properties such as actions and conditions—was not applied, unless it contained an explicit deny of itself.

The following example is a role trust policy attached to the role named RoleA in account 123456789012. It grants explicit access only to the role named RoleB.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": "arn:aws:iam::123456789012:role/RoleB"
            },
            "Action": ["sts:AssumeRole", "sts:TagSession"],
            "Condition": {
                "StringEquals": {
                    "aws:PrincipalTag/project": "BlueSkyProject"
                }
            }
        }
    ]
}

Assuming that the corresponding identity-based policy for RoleA granted the sts:AssumeRole action with regard to RoleA, this role trust policy provided that there were two roles that could assume RoleA: RoleB (explicitly referenced in the trust policy) and RoleA (assuming it was explicitly referenced in its identity policy). RoleB could assume RoleA only if it had the principal tag project:BlueSkyProject because of the trust policy condition. (The sts:TagSession permission is needed here in case tags need to be added by the caller as parted of the RoleAssumption call.) RoleA, on the other hand, did not need to meet that condition because it relied on a different explicit permission—the one granted in the identity-based policy. RoleA would have needed the principal tag project:BlueSkyProject to meet the trust policy condition if and only if it was relying on the trust policy to gain access through the sts:AssumeRole action; that is, in the case where its identity-based policy did not provide the needed privilege.

As we previously noted, after considering feedback from customers on this topic, AWS has decided that requiring self-referential role trust policy grants even in the case where the identity-based policy also grants access is the better approach to delivering consistency and visibility with regard to role behavior and privileges. Therefore, as of today, r­ole assumption behavior requires an explicit self-referential permission in the role trust policy, and the actions and conditions within that policy must also be satisfied, regardless of the permissions expressed in the role’s identity-based policy. (If permissions in the identity-based policy are present, they must also be satisfied.)

Requiring self-reference in the trust policy makes role trust policy evaluation consistent regardless of which role is seeking to assume the role. Improved consistency makes role permissions easier to understand and manage, whether through human inspection or security tooling. This change also eliminates the possibility of continuing the lifetime of an otherwise temporary credential without explicit, trackable grants of permission in trust policies. It also means that trust policy constraints and conditions are enforced consistently, regardless of which principal is assuming the role. Finally, as previously noted, this change allows customers to create and understand role assumption permissions in a single place (the role trust policy) rather than two places (the role trust policy and the role identity policy). It increases the simplicity of role trust permission management: “what you see [in the trust policy] is what you get.”

Continuing with the preceding example, if you need to allow a role to assume itself, you now must update the role trust policy to explicitly allow both RoleB and RoleA. The RoleA trust policy now looks like the following:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": [
                    "arn:aws:iam::123456789012:role/RoleB",
                    "arn:aws:iam::123456789012:role/RoleA"
                ]
            },
            "Action": ["sts:AssumeRole", "sts:TagSession"],
            "Condition": {
                "StringEquals": {
					"aws:PrincipalTag/project": "BlueSkyProject"
				}
            }
        }
    ]
}

Without this new principal grant, the role can no longer assume itself. The trust policy conditions are also applied, even if the role still has unconditioned access to itself in its identity-based policy.

Conclusion

In this blog post we’ve reviewed the old and new behavior of role assumption in the case where a role seeks to assume itself. We’ve seen that, according to our analysis of service metadata, the vast majority of role self-assumption behavior that relies solely on identity-based privileges is totally unnecessary, because the code (or human) who calls sts:AssumeRole is already, without realizing it, using the role’s credentials to call the AWS STS API. Eliminating that mistake will improve performance and decrease resource consumption. We’ve also explained in more depth the reasons for the old behavior and the reasons for making the change, and provided Athena and CloudTrail Lake queries that you can use to examine past or (in the case of allow-listed roles) current self-assumption behavior in your own environments. You can reach out to AWS Support or your customer account team if you need help in this effort.

If you currently use the old behavior and need to continue to do so, your primary option is to create an explicit allow for the role in its own trust policy. If that option doesn’t work due to operational constraints, you can reach out to AWS Support to seek an accommodation of your use case for new roles or new accounts. You can also ask AWS Support to remove roles from the allow-list if you want their behavior to follow the new model.

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

AWS would like to thank several customers and partners who highlighted this behavior as something they found surprising and unhelpful, and asked us to consider making this change. We would also like to thank independent security researcher Ryan Gerstenkorn who engaged with AWS on this topic and worked with us prior to this update.

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Mark Ryland

Mark Ryland

Mark is the director of the Office of the CISO for AWS. He has over 30 years of experience in the technology industry and has served in leadership roles in cybersecurity, software engineering, distributed systems, technology standardization and public policy. Previously, he served as the Director of Solution Architecture and Professional Services for the AWS World Public Sector team.

Stephen Whinston

Stephen Whinston

Stephen is a Senior Product Manager with the AWS Identity and Access Management organization. Prior to Amazon, Stephen worked in product management for cloud service and identity management providers. Stephen holds degrees in computer science and an MBA from the University of Colorado Leeds School of Business. Outside of work, Stephen enjoys his family time and the Pacific Northwest.

Sign Amazon SNS messages with SHA256 hashing for HTTP subscriptions

Post Syndicated from Daniel Caminhas original https://aws.amazon.com/blogs/security/sign-amazon-sns-messages-with-sha256-hashing-for-http-subscriptions/

Amazon Simple Notification Service (Amazon SNS) now supports message signatures based on Secure Hash Algorithm 256 (SHA256) hashing. Amazon SNS signs the messages that are delivered from your Amazon SNS topic so that subscribed HTTP endpoints can verify the authenticity of the messages. In this blog post, we will show you how to enable message signatures based on SHA256 for your Amazon SNS topics.

About message signing verification

To verify the authenticity of a message sent to your HTTP endpoint by Amazon SNS, you can verify the message signature. There are two cases where we recommend verifying the authenticity of the message. The first is when Amazon SNS sends a message to an HTTP endpoint that you subscribed to a topic. The second is when Amazon SNS sends a confirmation message to your HTTP endpoint after the Subscribe or the Unsubscribe API actions. For more information, see Verifying the signatures of Amazon SNS messages in the Amazon SNS Developer Guide.

Amazon SNS now supports two message signature versions:

  • Signature version 1 – Amazon SNS creates the signature based on the SHA1 hash of the message.
  • Signature version 2 – Amazon SNS creates the signature based on the SHA256 hash of the message.

Amazon SNS adds the SignatureVersion property to the JSON payload of messages delivered to HTTP endpoints, as shown in the following code snippet. For more information on the JSON payload format, see Parsing message formats in the Amazon SNS Developer Guide.

{
  "Type" : "Notification",
  "MessageId" : "22b80b92-fdea-4c2c-8f9d-bdfb0c7bf324",
  "TopicArn" : "arn:aws:sns:us-west-2:123456789012:MyTopic",
  "Subject" : "My First Message",
  "Message" : "Hello world!",
  "Timestamp" : "2022-08-02T00:54:06.655Z",
  "SignatureVersion" : "2",
  "Signature" : "EXAMPLEw6JRN...",
  "SigningCertURL" : "https://sns.us-west-2.amazonaws.com/SimpleNotificationService-f3ecfb7224c7233fe7bb5f59f96de52f.pem",
  "UnsubscribeURL" : "https://sns.us-west-2.amazonaws.com/?Action=Unsubscribe&SubscriptionArn=arn:aws:sns:us-west-2:123456789012:MyTopic:c9135db0-26c4-47ec-8998-413945fb5a96"
}

What to consider before you enable message signatures based on SHA256 for your Amazon SNS topic

As an Amazon SNS topic owner, before you enable SHA256 support for your topic, we recommend communicating with the owners of the HTTP endpoints that are subscribed to your topic. They might need to update their message signature verification logic to accommodate the new signature version. If the endpoint owners are using the AWS SDK feature for verifying the Amazon SNS message signatures, they need to make sure that they are using one of the following versions of the AWS SDK: Java 1.12.285, JavaScript 0.3.5, Ruby 1.54.0, PHP 1.8.0 or .NET 3.7.3.96.

How to enable message signatures based on SHA256 for your Amazon SNS topic

By default, Amazon SNS topics use SHA1 for hashing the message signature. You can enable SHA256 support for your topic by setting the topic attribute SignatureVersion to 2 using the AWS Software Development Kit (AWS SDK), or AWS Command Line Interface (AWS CLI).

The following code example shows how to set the topic attribute SignatureVersion by using the AWS CLI.

aws sns set-topic-attributes \
    --topic-arn arn:aws:sns:us-west-2:123456789012:MyTopic \
    --attribute-name SignatureVersion \
    --attribute-value 2

The following code example shows how to set the SignatureVersion attribute by using the AWS SDK for Java.

public static void enableSHA256Support(SnsClient snsClient, String topicArn) {

        try {

            SetTopicAttributesRequest request = SetTopicAttributesRequest.builder()
                .attributeName("SignatureVersion")
                .attributeValue("2")
                .topicArn(topicArn)
                .build();

            SetTopicAttributesResponse result = snsClient.setTopicAttributes(request);
            System.out.println("\n\nStatus was " + result.sdkHttpResponse().statusCode() + "\n\nTopic " + request.topicArn()
                + " updated " + request.attributeName() + " to " + request.attributeValue());

        } catch (SnsException e) {
            System.err.println(e.awsErrorDetails().errorMessage());
        }
    }

Conclusion

Amazon SNS topic owners can now enable message signatures based on SHA256 hashing. In this post, you learned how to choose the hashing algorithm, either SHA256 or SHA1, for your SNS topic. For more information, see Verifying the signatures of Amazon SNS messages in the Amazon SNS Developer Guide, and SetTopicAttributes in the Amazon SNS API Reference.

For more serverless learning resources, visit Serverless Land.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

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Author

Daniel Caminhas

Daniel is a software development engineer for Amazon SNS.

Author

Ahmed Abouzeid

Ahmed is a software development manager for Amazon SNS.

10 reasons to import a certificate into AWS Certificate Manager (ACM)

Post Syndicated from Nicholas Doropoulos original https://aws.amazon.com/blogs/security/10-reasons-to-import-a-certificate-into-aws-certificate-manager-acm/

AWS Certificate Manager (ACM) is a service that lets you efficiently provision, manage, and deploy public and private SSL/TLS certificates for use with AWS services and your internal connected resources. The certificates issued by ACM can then be used to secure network communications and establish the identity of websites on the internet or resources on private networks.

So why might you want to import a certificate into ACM, rather than using a certificate issued by ACM? According to the AWS Certificate Manager User Guide topic Importing certificates into AWS Certificate Manager, “you might do this because you already have a certificate from a third-party certificate authority (CA), or because you have application-specific requirements that are not met by ACM issued certificates.”

In this blog post, I’ll list 10 reasons why you might want to import a certificate into ACM, including what specific requirements you might have, and why you might want to use a certificate signed by a third-party CA in the first place.

1. To use an ECDSA certificate for faster TLS connections

Imported Elliptic Curve Digital Signature Algorithm (ECDSA) certificates use smaller keys than ACM issued public RSA certificates, allowing for TLS connections to be established faster. For this reason, ECDSA certificates are particularly useful for systems with limited processing resources, such as Internet of Things (IoT) devices. ACM supports imported certificates with ECDSA in 256, 384, and 521 bit variations. If you want to use an ECDSA certificate for your public-facing web application, you need to get a third-party certificate and then import it into ACM. For more information about supported cryptographic algorithms for imported certificates, see Prerequisites for importing certificates in the AWS Certificate Manager User Guide.

2. To control your certificate’s renewal cycle

When you import a certificate into ACM, you have greater control over its renewal cycle simply because you can re-import it as frequently as you want. You also have control over how often your imported certificate’s private key can be rotated. As a best practice, you should rotate your certificate’s private key based on your certificate’s usage frequency.

Note: When you re-import your certificate, to maintain the existing associations during renewal, ensure that you specify the existing certificate’s Amazon Resource Name (ARN). For more information and step-by-step instructions, see Reimporting a certificate in the AWS Certificate Manager User Guide.

3. To use certificate pinning

You might have an application that requires certificate pinning, which is the practice of bypassing the typical hierarchical model of trust that is governed by certificate authorities. With certificate pinning, a host’s identity is trusted based on a specific certificate or public key. As a certificate pinning best practice, AWS recommends that public certificates issued by ACM should not be pinned because ACM will generate a new public/private key pair at the next renewal phase, which essentially replaces the pinned certificate with a new one, causing service disruption along the process. If you want to use certificate pinning, you can pin an imported certificate because imported certificates are not subject to managed renewal, thereby reducing the risk of production impact.

4. To use a higher-assurance certificate

You might want to use a higher-assurance certificate, such as an organization validation (OV) or extended validation (EV) certificate. Certificates issued by ACM currently only support domain validation (DV). If the domain you want to protect is an application that requires OV or EV, you can import OV or EV certificates into ACM by using a third-party certificate of either type. You can use the ACM API action ImportCertificate to import OV or EV certificates into ACM.

5. To use a self-signed certificate

For internal testing environments where your developers want speed and flexibility, self-signed certificates are issued faster and effortlessly. However, it’s important to know that self-signed certificates are not trusted by default, which means that self-signed certificates need to be installed inside the trust stores of the intended clients, to avoid the risk of your users getting into the habit of ignoring browser warnings. For more information, see the additional requirements for self-signed certificates in Prerequisites for importing certificates in the AWS Certificate Manager User Guide.

6. To use an IP address for the certificate’s subject

By design, the subject field of an ACM certificate can only identify a fully qualified domain name (FQDN). If you want to use an IP address for the certificate’s subject, then you can create the certificate and import it to ACM.

7. To exceed the number of domains allowed by the ACM quotas

Certificates issued by ACM are subject to the ACM service quotas. The default quota for ACM is 10 domain names for each ACM certificate, and you can request an increase to the quota up to a maximum of 100 domain names for each certificate. However, if you import certificates, they are not subject to the quotas, and you can use a public certificate with more than 100 FQDNs in its domain scope without having to go through the process of requesting any limit increases.

8. To use a private certificate issued by ACM Private CA with the IssueCertificate API action

Certificates provisioned with the IssueCertificate API action have a private status and cannot be associated directly with an AWS integrated service, such as an internal Application Load Balancer. Instead, a private certificate issued by AWS Certificate Manager Private Certificate Authority (ACM Private CA) with the IssueCertificate API action needs to be exported and then imported into ACM before the association can be made. The same is true for certificate templates as well, which are configuration templates that can be passed as parameters to the IssueCertificate API action as a means to have greater control over the private certificate’s extensions.

9. To use a private certificate issued by your on-premises CA

You might want to use a private certificate issued by your on-premises CA instead of using ACM Private CA. To administer your internal public key infrastructure (PKI), AWS generally recommends that you use ACM Private CA. However, you might still come across scenarios where a certificate signed by your on-premises CA is better suited for your specific needs. For example, you might want to have a common root of trust, for consistency and interoperability purposes across a hybrid PKI solution. Furthermore, using an external parent CA with ACM Private CA also allows you to enforce CA name constraints. For more information, see Signing private CA certificates with an external CA in the AWS Certificate Manager Private Certificate Authority User Guide.

10. To use a certificate for something other than securing a public website

In addition to securing a public website, you can use certificates for other purposes. For example, you can import client and server certificates as part of an OpenVPN setup. For more information about this example, see How can I generate server and client certificates and their respective keys on a Windows server and upload them to AWS Certificate Manager (ACM)? In addition, you can import a code-signing certificate for use with AWS IoT Device Management. For more information about how to import a code-signing certificate, see (For IoT only) Obtain and import a code-signing certificate in the AWS Signer Developer Guide.

Conclusion

In this blog post, you learned about some of the reasons you might want to import a certificate into AWS Certificate Manager (ACM). For more information about importing certificates into ACM and step-by-step instructions, see Importing certificates into AWS Certificate Manager in the AWS Certificate Manager User Guide. For the latest pricing information, see the AWS Certificate Manager Pricing page on the AWS website. You can also use the AWS pricing calculator to estimate costs.

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 the AWS Certificate Manager forum or contact AWS Support.

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Nicholas Doropoulos

Nicholas Doropoulos

Nicholas is a Cloud Security Engineer II, Bestselling Udemy Instructor, AWS Shield, GuardDuty and Certificate Manager SME. In his spare time, he enjoys creating tools, practising his OSINT skills by participating in Search Party CTFs for missing people and registering Google Dorks in Offensive Security’s Google Hacking Database.

Hazard analysis and Chaos engineering at Vanguard Group

Post Syndicated from Jason Barto original https://aws.amazon.com/blogs/devops/hazard-analysis-and-chaos-engineering-at-vanguard-group/

Anticipating events that can cause a disruption to your system’s service is critical to building highly available, reliable systems.  Hazard analysis gives you a method to identify such events.  Chaos engineering gives you a method to confirm that a system behaves as expected in adverse conditions.  By combining these methods, Vanguard is building reliability into their systems.

Vanguard engineering teams perform hazard analysis on their systems and capture the identified events as failure scenarios.  They use the identified failure scenarios to create hypotheses to support chaos engineering experiments.  These hypotheses predict how the system will respond to failures and each hypothesis is then confirmed through experimentation to increase the team’s confidence in the system’s reliability.

In this article we will walk you through how Vanguard uses hazard analysis and chaos engineering.  We will also provide guidance on how you can employ these techniques on your applications.

Failure Mode & Effects Analysis

A hazard analysis can be performed using different methods.  At Vanguard, they have adapted the failure mode & effects analysis (FMEA) method to support their important services.

FMEA is a bottom-up approach to analyse an architecture and focus on the impact to system functions when one or more components of the system are disrupted. Members of the engineering team and architects responsible for designing and building a system brainstorm possible failure scenarios or failure modes, and document the impact of these failures on the system. Combined with a quantitative method for ranking the failure modes, the analysis process produces a prioritised list of failure modes which describes how the system would respond to individual or combined failures in its component parts or dependencies.

For each failure mode the team conducting the analysis will highlight what protections exist within the system to guard against the failure mode.  Sometimes, fault isolation boundaries have been put in place to prevent client impact in failure scenarios. In other scenarios, for one reason or another, there are hard dependencies in place for which the engineering team has decided not to build in fault tolerance. For example, a team responsible for a less-critical function may have architected its system to operate across multiple availability zones, but could decide not to implement other mitigations to prioritize cost over increased resilience.

The FMEA method has been in use by engineers in the automotive, aeronautical, healthcare, and military industries for more than 60 years.  Over that time, FMEA has been modified to best suit the organization and the field in which it was applied.  In many variations the FMEA measures each failure mode with a risk priority number (RPN), which is intended to quantitatively rank the failure mode based upon:

  1. The failure mode’s impact to the system as a whole
  2. The probability of the failure mode’s occurrence
  3. How easily the failure mode can be detected

Vanguard have adapted the FMEA process to serve their own specific requirements and processes.  Vanguard have decided not to adopt the RPN element of the FMEA process, as teams found they spent a lot of time debating the impact, probability, and detectability of individual failure modes.  To perform an FMEA more quickly, teams instead focus on the failure modes and system impact only, documenting a mental model of system performance which can be experimented through chaos engineering.

An excerpt of a Vanguard FMEA output is provided as an example in the following table:

The “Process Step” in the table above refers to a business function of the system being analyzed, for example “Request to retrieve stored data”. As part of the analysis, the team identifies the system components needed to perform the Process Step and considers the interactions of those components Focusing on a Process Step makes it easier to anticipate the failure scenarios that would affect the system in performing this particular business function. Also, the Process Step will imply an importance or criticality which can be a factor when prioritizing mitigations.

After selecting a Process Step, you walk through the system components involved and identify how component failures or disruptions will affect the wider system. Such component failures may involve individual components or a combination of components and are captured as “Failure Mode”. This identifies the component or components that are disrupted and their behaviour; for example, “Microservice is unavailable or returns an error”.

“Expected Behaviour” describes the effect of the failure mode on the wider system, in the context of the Process Step. This captures what other system components are affected by the Failure Mode and why, and how this impacts the Process Step as a whole.

Lastly, the “Hypothesis” column forms the basis for the chaos experiments that will follow from the FMEA to confirm that the system performs as expected.

At Vanguard, all mission-critical product teams are conducting FMEAs for their production applications. The outputs of these sessions are maintained over time and serve multiple purposes:

  1. When onboarding new team members, it is helpful to provide the FMEA document alongside an architecture diagram and narrative. It will paint a more robust picture of how the system is intended to operate in both “happy path” and “unhappy path” scenarios.
  2. When troubleshooting incidents, an FMEA document can help on-call engineers – especially those less experienced with debugging – to match up the documented expectations to the observed system behavior.
  3. Site Reliability Engineers (SREs) looking for opportunities to improve the resilience of a system might look to FMEA documentation to understand the existing fault isolation boundaries and introduce additional resilience mechanisms through automation and system changes.
  4. Finally, when selecting scenarios for experimentation with Chaos Engineering, the FMEA document provides a list of conjectures that have been mapped to hypotheses, ready to be validated through experimentation. This input into the Chaos Engineering workflow is the primary use of FMEA documents for Vanguard product teams.

There are many resources available online to learn more about how FMEA is used and applied in other organisations. In Failure Modes and Continuous Resilience, Adrian Cockcroft introduces FMEA as a method for anticipating failure scenarios. The NASA Software Engineering Handbook details how FMEAs are conducted as part of their engineering process. The Automotive Industry Group has also formally documented the use of FMEA in the Automotive Industry Action Group FMEA Handbook.

Chaos Engineering

After failure modes have been identified and mitigated through system design, it’s time to understand how resilient the system’s implementation is to those failure modes. Chaos engineering can be used to explore a system and validate that a system’s implementation meets business resiliency objectives.

Chaos engineering helps to improve a team’s mental model about the system under experimentation and provides insights into how a complex system behaves under adverse conditions. It also enables an engineer to find the unknown unknowns and the known unknowns through experiments that are built on top of the hypothesis. These experiments should simulate real world events, such as network degradation and increased client requests, and the outcome of the experiment should not be known. In other words, an experiment is not an experiment if it’s known that the conditions will cause the system to fail.

Prerequisites to Chaos Experiments at Vanguard

At Vanguard, there are some necessary prerequisites to running a chaos experiment. Firstly, the system under experiment must be set up with some basic observability tooling that will allow teams to monitor the state of the application during the failure injection. This could be as simple as an Amazon CloudWatch dashboard and some associated alarms, or as elaborate as a dedicated dashboard set up in a vendor tool.

Secondly, teams must be able to drive load to the application during the experiment; depending on the experiment type, the level and type of load may vary. The load generator can be as simple as a script on someone’s machine, or a fully automated load test depending on the requirements of the hypothesis.

Finally, teams need to have a good understanding of what the application’s “steady state” looks like. I Ideally, this takes the form of some metrics such as expected error rate, expected latency, and/or a service level objective (SLO) that can be monitored throughout the duration of the experiment. For example, a service level objective for a RESTful API might be that 90% of requests should receive a response within 100 milliseconds.

With the prerequisites met and a completed FMEA, teams can then experiment with their hypothesis using various experiment templates defined by Vanguard’s Climate of Chaos tooling.

Vanguard’s Climate of Chaos

At Vanguard, ensuring its software systems are resilient to adverse events is a critical part of its ongoing mission to provide world-class service to their clients. Vanguard believes that in order to develop high quality software, one must plan for the inevitable “stormy weather” events that occur in a distributed system.

Over the past 2 years, as a response to this need, Vanguard has developed in-house tooling called “The Climate of Chaos” to give teams easy access to common experiment templates, along with a friendly UI interface. The Climate of Chaos helps developers experiment on their systems and validate the hypotheses generated from FMEAs. It also provides the tooling for them to simulate the most common failure scenarios on Vanguard’s most commonly utilized AWS infrastructure, including Amazon Elastic Container Service (Amazon ECS), AWS Fargate, Amazon DynamoDB, Amazon Relational Database Service (Amazon RDS), AWS Lambda, and others.

The Climate of Chaos was created prior to Amazon’s release of the AWS Fault Injection Simulator (FIS), and today there is a lot of overlap with the experiment capabilities available in FIS. The Climate of Chaos has also been enhanced with company-specific features and integrations that make it easier for Vanguard developers to run chaos experiments in a controlled and predictable manner.

The Climate of Chaos includes important safety features such as an “emergency stop” function. This feature enables teams to terminate the experiment immediately if unintended side effects are encountered, rolling back the events simulated to resume steady state operation. The Climate of Chaos has been coupled with other systems like an in-house load testing tooling and added features like the ability to monitor CloudWatch alarms. Vanguard also offers teams the ability to schedule experiments to run at their convenience. Soon, Vanguard hopes to make running chaos experiments even smarter, introducing tools that will help teams run bulk experiments that systematically inject failures on a group of related applications to help pinpoint more complex failure modes.

Next Steps

Failure modes and effects analysis is a hazard analysis method which can help you identify single and combined points of failure in your system so you can prioritize the failure modes. To learn more about the FMEA process, you can read the NASA Software Engineering Handbook which outlines how they perform FMEA on their software-based systems. The AWS Whitepaper Building Mission-Critical Financial Services Applications on AWS provides example forms and suggestions for severity, probability, and detectability rankings. Appendix F in the whitepaper suggests a 1 to 10 ranking for each Risk Priority Number input, and the example spreadsheets recommend performing FMEAs for the application, platform, infrastructure, and operation layers of the system. Using these examples, you can perform an analysis of your own systems and generate hypotheses.

To experiment on your systems and validate your own hypotheses, you can use the AWS Fault Injection Simulator (FIS) mentioned earlier in this article. FIS provides you with a framework for performing controlled chaos experiments on your AWS workloads. It helps you to safely manage your experiments by providing tooling to monitor, rollback, and orchestrate chaos experiments. FIS provides the fault injection mechanisms that you will need to experiment upon your system’s implementation and resilience to identified failure modes. You can start by running experiments in pre-production environments, and then step up to running them as part of your CI/CD workflow and ultimately in your production environment. To learn more about FIS, you can read the FIS User Guide and FIS tutorials.

By using FMEA to anticipate the failures and experimenting on your systems with chaos engineering, you will gain confidence in the reliability of your system.

The content and opinions in this post are those of The Vanguard Group and AWS is not responsible for the content or accuracy of this post.

About the authors:

Tory Benya

Tory works as a Chaos Engineering Tech Lead at Vanguard.  She is passionate about automation, data, and making software work for people.  She likes to automate, integrate, and improve processes and technology.  Tory makes data-driven decisions to make a difference as part of her team at Vanguard.

Christina Yakomin

Christina works as a Senior Site Reliability Engineering Specialist in Vanguard’s Chief Technology Office. Throughout her career, she has developed an expansive skill set in front- and back-end web development, as well as cloud infrastructure and automation, with a specialization in Site Reliability Engineering. She has earned several Amazon Web Services certifications, including the Solutions Architect – Professional. Christina has also worked closely with the Women’s Initiative for Leadership Success at Vanguard, both internally at the company and externally in the local community, to further the career advancement of women and girls – in particular within the tech industry.

Jason Barto

Jason works as a Principal Solutions Architect at AWS where he works with customers to design resilient system architectures and develop chaos engineering practices. Prior to joining AWS Jason was designing and building distributed systems for complex event processing and real-time telemetry analytics.

John Formento

John is a Solutions Architect at AWS. He helps large enterprises achieve their goals by architecting secure and scalable solutions on the AWS Cloud. John holds 7 AWS certifications including AWS Certified Solutions Architect – Professional and DevOps Engineer – Professional.

Amazon introduces dynamic intermediate certificate authorities

Post Syndicated from Adina Lozada original https://aws.amazon.com/blogs/security/amazon-introduces-dynamic-intermediate-certificate-authorities/

AWS Certificate Manager (ACM) is a managed service that lets you provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with Amazon Web Services (AWS) and your internal connected resources. Starting October 11, 2022, at 9:00 AM Pacific Time, public certificates obtained through ACM will be issued from one of the multiple intermediate certificate authorities (CAs) that Amazon manages. In this blog post, we share important details about this change and how you can prepare.

What is changing and why?

Public certificates that you request through ACM are obtained from Amazon Trust Services, which is a public certificate authority (CA) that Amazon manages. Like other public CAs, Amazon Trust Services CAs have a structured trust hierarchy. The public certificate issued to you, also known as the leaf certificate, can chain to one or more intermediate CAs and then to the Amazon Trust Services root CA. The Amazon Trust Services root CA is trusted by default by most and operating systems. This is why Amazon can issue public certificates that are trusted by these systems.

Starting October 11, 2022 at 9:00 AM Pacific Time, public certificates obtained through ACM will be issued from one of the multiple intermediate CAs that Amazon manages. These intermediate CAs chain to an existing Amazon Trust Services root CA. With this change, leaf certificates issued to you will be signed by different intermediate CAs. Before this change, Amazon maintained a limited number of intermediate CAs and issued and renewed certificates from the same intermediate CAs.

Amazon is making this change to create a more resilient and agile certificate infrastructure that will help us respond more quickly to future requirements. This change also presents an opportunity to correct a known issue related to delayed revocation of a subordinate CA and help minimize the scope of impact for new risks that might emerge in the future.

What can I do to prepare?

Most customers won’t experience an impact from this change. Browsers and most applications will continue to work just as they do now, because these services trust the Amazon Trust Services root CA and not a specific intermediate CA. If you’re using one of the standard operating systems and web browsers that are listed in the next section of this post, you don’t need to take any action.

If you use intermediate CA information through certificate pinning, you will need to make changes and pin to an Amazon Trust Services root CA instead of an intermediate CA or leaf certificate. Certificate pinning is a process in which your application that initiates the TLS connection only trusts a specific public certificate through one or more certificate variables that you define. If the pinned certificate is replaced, your application won’t initiate the connection. AWS recommends that you don’t use certificate pinning because it introduces an availability risk. However, if your use case requires certificate pinning, AWS recommends that you pin to an Amazon Trust Services root CA instead of an intermediate CA or leaf certificate. When you pin to an Amazon Trust Services root CA, you should pin to all of the root CAs shown in the following table.

Amazon Trust Services root CA certificates

Distinguished name SHA-256 hash of subject public key information Test URL
CN=Amazon Root CA
1,O=Amazon,C=US
fbe3018031f9586bcbf41727e417b7d1c45c2f47f93be372a17b96b50757d5a2 Test URL
CN=Amazon Root CA
2,O=Amazon,C=US
7f4296fc5b6a4e3b35d3c369623e364ab1af381d8fa7121533c9d6c633ea2461 Test URL
CN=Amazon Root CA
3,O=Amazon,C=US
36abc32656acfc645c61b71613c4bf21c787f5cabbee48348d58597803d7abc9 Test URL
CN=Amazon Root CA
4,O=Amazon,C=US
f7ecded5c66047d28ed6466b543c40e0743abe81d109254dcf845d4c2c7853c5 Test URL

To test that your trust store contains the Amazon Trust Services root CA, see the preceding table, which lists the Amazon Trust Services root CA certificates, and choose each test URL in the table. If the test URL works, you should see a message that says Expected Status: Good, along with the certificate chain. If the test URL doesn’t work, you will receive an error message that indicates the connection has failed.

What should I do if the Amazon Trust Services CAs are not in my trust store?

If your application is using a custom trust store, you must add the Amazon Trust Services root CAs to your application’s trust store. The instructions for doing this vary based on the application or service. Refer to the documentation for the application or service that you’re using.

If your tests of any of the test URLs failed, you must update your trust store. The simplest way to update your trust store is to upgrade the operating system or browser that you’re using.

The following operating systems use the Amazon Trust Services CAs:

  • Amazon Linux (all versions)
  • Microsoft Windows versions, with updates installed, from January 2005, Windows Vista, Windows 7, Windows Server 2008, and newer versions
  • Mac OS X 10.4 with Java for Mac OS X 10.4 Release 5, Mac OS X 10.5, and newer versions
  • Red Hat Enterprise Linux 5 (March 2007 release), Linux 6, and Linux 7 and CentOS 5, CentOS 6, and CentOS 7
  • Ubuntu 8.10
  • Debian 5.0
  • Java 1.4.2_12, Java 5 update 2, and all newer versions, including Java 6, Java 7, and Java 8

Modern browsers trust Amazon Trust Services CAs. To update the certificate bundle in your browser, update your browser. For instructions on how to update your browser, see the update page for your browser:

Where can I get help?

If you have questions, contact AWS Support or your technical account manager (TAM), or start a new thread on the AWS re:Post ACM Forum. If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security news? Follow us on Twitter.

Adina Lozada

Adina Lozada

Adina is a Principal Technical Program Manager on the Amazon Certificate Manager (ACM) team with over 18 years of professional experience as a multi-disciplined, security careerist in both public and private sector. She works with AWS services to help make complex, cross-functional program delivery faster for our customers.

Chandan Kundapur

Chandan Kundapur

Chandan is a, Sr. Technical Product Manager on the Amazon Certificate Manager (ACM) team. With over 15 years of cyber security experience, he has a passion for driving our product strategy to help AWS customers identify and secure their resources and endpoints with public and private certificates.

DevOps with serverless Jenkins and AWS Cloud Development Kit (AWS CDK)

Post Syndicated from sangusah original https://aws.amazon.com/blogs/devops/devops-with-serverless-jenkins-and-aws-cloud-development-kit-aws-cdk/

The objective of this post is to walk you through how to set up a completely serverless Jenkins environment on AWS Fargate using AWS Cloud Development Kit (AWS CDK).

Jenkins is a popular open-source automation server that provides hundreds of plugins to support building, testing, deploying, and automation. Jenkins uses a controller-agent architecture in which the controller is responsible for serving the web UI, stores the configurations and related data on disk, and delegates the jobs to the worker agents that run these jobs as their primary responsibility.

Amazon Elastic Container Service (Amazon ECS)  using Fargate is a fully-managed container orchestration service that helps you easily deploy, manage, and scale containerized applications. It deeply integrates with the rest of the AWS platform to provide a secure and easy-to-use solution for running container workloads in the cloud and now on your infrastructure. Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. Fargate is compatible with both Amazon ECS and Amazon Elastic Kubernetes Service (Amazon EKS).

Solution overview

The following diagram illustrates the solution architecture. The dashed lines indicate the AWS CDK deployment.

Figure 1 This diagram shows AWS CDK and how it deploys using AWS CloudFormation to create the Elastic Load Balancer, AWS Fargate, and Amazon EFS

Figure 1 This diagram shows AWS CDK and how it deploys using AWS CloudFormation to create the Elastic Load Balancer, AWS Fargate, and Amazon EFS

You’ll be using the following:

  1. The Jenkins controller URL backed by an Application Load Balancer (ALB).
  2. You’ll be using your default Amazon Virtual Private Cloud (Amazon VPC) for this example.
  3. The Jenkins controller runs as a service in Amazon ECS using Fargate as the launch type. You’ll use Amazon Elastic File System (Amazon EFS) as the persistent backing store for the Jenkins controller task. The Jenkins controller and Amazon EFS are launched in private subnets.

Prerequisites

For this post, you’ll utilize AWS CDK using TypeScript.

Follow the guide on Getting Started for AWS CDK to:

  • Get your local environment setup
  • Bootstrap your development account

Code

Let’s review the code used to define the Jenkins environment in AWS using the AWS CDK.

Setup your imports

import { Duration, IResource, RemovalPolicy, Stack, Tags } from 'aws-cdk-lib';
import { Construct } from 'constructs';

import * as cdk from 'aws-cdk-lib';

import * as ecs from 'aws-cdk-lib/aws-ecs';
import * as efs from 'aws-cdk-lib/aws-efs';
import { Port } from 'aws-cdk-lib/aws-ec2';
import * as elbv2 from 'aws-cdk-lib/aws-elasticloadbalancingv2';

Setup your Amazon ECS, which is a logical grouping of tasks or services and set vpc

export class AppStack extends Stack {
  constructor(scope: Construct, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    const jenkinsHomeDir: string = 'jenkins-home';
    const appName: string = 'jenkins-cdk';

    const cluster = new ecs.Cluster(this, `${appName}-cluster`, {
      clusterName: appName,
    });

    const vpc = cluster.vpc;

Setup Amazon EFS to store the data

    const fileSystem = new efs.FileSystem(this, `${appName}-efs`, {
      vpc: vpc,
      fileSystemName: appName,
      removalPolicy: RemovalPolicy.DESTROY,
    });

Setup Access Point, which are application-specific entry points into an Amazon EFS file system that makes it easier to manage application access to shared datasets

const accessPoint = fileSystem.addAccessPoint(`${appName}-ap`, {
      path: `/${jenkinsHomeDir}`,
      posixUser: {
        uid: '1000',
        gid: '1000',
      },
      createAcl: {
        ownerGid: '1000',
        ownerUid: '1000',
        permissions: '755',
      },
    });

Setup Task Definition to run Docker containers in Amazon ECS

const taskDefinition = new ecs.FargateTaskDefinition(
      this,
      `${appName}-task`,
      {
        family: appName,
        cpu: 1024,
        memoryLimitMiB: 2048,
      }
    );

Setup a Volume mapping the Amazon EFS from above to the Task Definition

taskDefinition.addVolume({
      name: jenkinsHomeDir,
      efsVolumeConfiguration: {
        fileSystemId: fileSystem.fileSystemId,
        transitEncryption: 'ENABLED',
        authorizationConfig: {
          accessPointId: accessPoint.accessPointId,
          iam: 'ENABLED',
        },
      },
    });

Setup the Container using the Task Definition and the Jenkins image from the registry

const containerDefinition = taskDefinition.addContainer(appName, {
      image: ecs.ContainerImage.fromRegistry('jenkins/jenkins:lts'),
      logging: ecs.LogDrivers.awsLogs({ streamPrefix: 'jenkins' }),
      portMappings: [{ containerPort: 8080 }],
    });

Setup Mount Points to bind ephemeral storage to the container

containerDefinition.addMountPoints({
      containerPath: '/var/jenkins_home',
      sourceVolume: jenkinsHomeDir,
      readOnly: false,
    });

Setup Fargate Service to run the container serverless

    const fargateService = new ecs.FargateService(this, `${appName}-service`, {
      serviceName: appName,
      cluster: cluster,
      taskDefinition: taskDefinition,
      desiredCount: 1,
      maxHealthyPercent: 100,
      minHealthyPercent: 0,
      healthCheckGracePeriod: Duration.minutes(5),
    });
    fargateService.connections.allowTo(fileSystem, Port.tcp(2049));

Setup ALB and add listener to checks for connection requests, using the protocol and port that you configure.

    const loadBalancer = new elbv2.ApplicationLoadBalancer(
      this,
      `${appName}-elb`,
      {
        loadBalancerName: appName,
        vpc: vpc,
        internetFacing: true,
      }
    );
    const lbListener = loadBalancer.addListener(`${appName}-listener`, {
      port: 80,
    });

Setup Target to route requests to Jenkins running on Amazon ECS using Fargate

const loadBalancerTarget = lbListener.addTargets(`${appName}-target`, {
      port: 8080,
      targets: [fargateService],
      deregistrationDelay: Duration.seconds(10),
      healthCheck: { path: '/login' },
    });
  }
}

Jenkins Deployment

Now that you have all the code, let’s deploy the AWS CDK definition:

  1. Make sure that you have done the Prerequisite steps from earlier.
  2. Install packages by running the following command in your IDE CLI:
npm i
  1. Now you’ll deploy your AWS CDK definition to your dev account:
cdk deploy

Let’s now login to Jenkins

  1. In your browser, use the DNS Name from the deployed Load Balancer
  2. In Amazon CloudWatch, there will be a Log group that will be created that is associated to Cluster Service.
    1. Go into that log and you’ll see it output the Password to login to Jenkins
  1. In Jenkins, follow the wizard to continue the setup

Cleaning up

To avoid incurring future charges, delete the resources.

Let’s destroy our deploy solution

  1. In your IDE CLI:
cdk destroy

Conclusion

With this overview we were able to cover the following:

  • Build an Elastic Load Balancer
  • Use AWS Fargate with a Jenkins AMI
  • All resources running serverlessly
  • All build using the AWS CDK

About the author:

Josh Thornes

Josh Thornes is a Sr. Technical Account Manager at AWS. He works with AWS Partners at any stage of their software-as-a-service (SaaS) journey in order to help build new products, migrate existing applications, or optimize SaaS solutions on AWS. His areas of interest include builder experience (e.g., developer tools, DevOps culture, CI/CD, Front-end, Mobile, Microservices), security, IoT, analytics.

Use AWS Network Firewall to filter outbound HTTPS traffic from applications hosted on Amazon EKS and collect hostnames provided by SNI

Post Syndicated from Kirankumar Chandrashekar original https://aws.amazon.com/blogs/security/use-aws-network-firewall-to-filter-outbound-https-traffic-from-applications-hosted-on-amazon-eks/

This blog post shows how to set up an Amazon Elastic Kubernetes Service (Amazon EKS) cluster such that the applications hosted on the cluster can have their outbound internet access restricted to a set of hostnames provided by the Server Name Indication (SNI) in the allow list in the AWS Network Firewall rules. For encrypted web traffic, SNI can be used for blocking access to specific sites in the network firewall. SNI is an extension to TLS that remains unencrypted in the traffic flow and indicates the destination hostname a client is attempting to access over HTTPS.

This post also shows you how to use Network Firewall to collect hostnames of the specific sites that are being accessed by your application. Securing outbound traffic to specific hostnames is called egress filtering. In computer networking, egress filtering is the practice of monitoring and potentially restricting the flow of information outbound from one network to another. Securing outbound traffic is usually done by means of a firewall that blocks packets that fail to meet certain security requirements. One such firewall is AWS Network Firewall, a managed service that you can use to deploy essential network protections for all of your VPCs that you create with Amazon Virtual Private Cloud (Amazon VPC).

Example scenario

You have the option to scan your application traffic by the identifier of the requested SSL certificate, which makes you independent from the relationship of the IP address to the certificate. The certificate could be served from any IP address. Traditional stateful packet filters are not able to follow the changing IP address of the endpoints. Therefore, the host name information that you get from the SNI becomes important in making security decisions. Amazon EKS has gained popularity for running containerized workloads in the AWS Cloud, and you can restrict outbound traffic to only the known hostnames provided by SNI. This post will walk you through the process of setting up the EKS cluster in two different subnets so that your software can use the additional traffic routing in the VPC and traffic filtering through Network Firewall.

Solution architecture

The architecture illustrated in Figure 1 shows a VPC with three subnets in Availability Zone A, and three subnets in Availability Zone B. There are two public subnets where Network Firewall endpoints are deployed, two private subnets where the worker nodes for the EKS cluster are deployed, and two protected subnets where NAT gateways are deployed.

Figure 1: Outbound internet access through Network Firewall from Amazon EKS worker nodes

Figure 1: Outbound internet access through Network Firewall from Amazon EKS worker nodes

The workflow in the architecture for outbound access to a third-party service is as follows:

  1. The outbound request originates from the application running in the private subnet (for example, to https://aws.amazon.com) and is passed to the NAT gateway in the protected subnet.
  2. The HTTPS traffic received in the protected subnet is routed to the AWS Network Firewall endpoint in the public subnet.
  3. The network firewall computes the rules, and either accepts or declines the request to pass to the internet gateway.
  4. If the request is passed, the application-requested URL (provided by SNI in the non-encrypted HTTPS header) is allowed in the network firewall, and successfully reaches the third-party server for access.

The VPC settings for this blog post follow the recommendation for using public and private subnets described in Creating a VPC for your Amazon EKS cluster in the Amazon EKS User Guide, but with additional subnets called protected subnets. Instead of placing the NAT gateway in a public subnet, it will be placed in the protected subnet, and the Network Firewall endpoints in the public subnet will filter the egress traffic that flows through the NAT gateway. This design pattern adds further checks and could be a recommendation for your VPC setup.

As suggested in Creating a VPC for your Amazon EKS cluster, using the Public and private subnets option allows you to deploy your worker nodes to private subnets, and allows Kubernetes to deploy load balancers to the public subnets. This arrangement can load-balance traffic to pods that are running on nodes in the private subnets. As shown in Figure 1, the solution uses an additional subnet named the protected subnet, apart from the public and private subnets. The protected subnet is a VPC subnet deployed between the public subnet and private subnet. The outbound internet traffic that is routed through the protected subnet is rerouted to the Network Firewall endpoint hosted within the public subnet. You can use the same strategy mentioned in Creating a VPC for your Amazon EKS cluster to place different AWS resources within private subnets and public subnets. The main difference in this solution is that you place the NAT gateway in a separate protected subnet, between private subnets, and place Network Firewall endpoints in the public subnets to filter traffic in the network firewall. The NAT gateway’s IP address is still preserved, and could still be used for adding to the allow list of third-party entities that need connectivity for the applications running on the EKS worker nodes.

To see a practical example of how the outbound traffic is filtered based on the hosted names provided by SNI, follow the steps in the following Deploy a sample section. You will deploy an AWS CloudFormation template that deploys the solution architecture, consisting of the VPC components, EKS cluster components, and the Network Firewall components. When that’s complete, you can deploy a sample app running on Amazon EKS to test egress traffic filtering through AWS Network Firewall.

Deploy a sample to test the network firewall

Follow the steps in this section to perform a sample app deployment to test the use case of securing outbound traffic through AWS Network Firewall.

Prerequisites

The prerequisite actions required for the sample deployment are as follows:

  1. Make sure you have the AWS CLI installed, and configure access to your AWS account.
  2. Install and set up the eksctl tool to create an Amazon EKS cluster.
  3. Copy the necessary CloudFormation templates and the sample eksctl config files from the blog’s Amazon S3 bucket to your local file system. You can do this by using the following AWS CLI S3 cp command.
    aws s3 cp s3://awsiammedia/public/sample/803-network-firewall-to-filter-outbound-traffic/config.yaml .
    aws s3 cp s3://awsiammedia/public/sample/803-network-firewall-to-filter-outbound-traffic/lambda_function.py .
    aws s3 cp s3://awsiammedia/public/sample/803-network-firewall-to-filter-outbound-traffic/network-firewall-eks-collect-all.yaml .
    aws s3 cp s3://awsiammedia/public/sample/803-network-firewall-to-filter-outbound-traffic/network-firewall-eks.yaml .

    Important: This command will download the S3 bucket contents to the current directory on your terminal, so the “.” (dot) in the command is very important.

  4. Once this is complete, you should be able to see the list of files shown in Figure 2. (The list includes config.yaml, lambda_function.py, network-firewall-eks-collect-all.yaml, and network-firewall-eks.yaml.)
    Figure 2: Files downloaded from the S3 bucket

    Figure 2: Files downloaded from the S3 bucket

Deploy the VPC architecture with AWS Network Firewall

In this procedure, you’ll deploy the VPC architecture by using a CloudFormation template.

To deploy the VPC architecture (AWS CLI)

  1. Deploy the CloudFormation template network-firewall-eks.yaml, which you previously downloaded to your local file system from the Amazon S3 bucket.

    You can do this through the AWS CLI by using the create-stack command, as follows.

    aws cloudformation create-stack --stack-name AWS-Network-Firewall-Multi-AZ \
    --template-body file://network-firewall-eks.yaml \
    --parameters ParameterKey=NetworkFirewallAllowedWebsites,ParameterValue=".amazonaws.com\,.docker.io\,.docker.com" \
    --capabilities CAPABILITY_NAMED_IAM

    Note: The initially allowed hostnames for egress filtering are passed to the network firewall by using the parameter key NetworkFirewallAllowedWebsites in the CloudFormation stack. In this example, the allowed hostnames are .amazonaws.com, .docker.io, and docker.com.

  2. Make a note of the subnet IDs from the stack outputs of the CloudFormation stack after the status goes to Create_Complete.
     
    aws cloudformation describe-stacks \
    --stack-name AWS-Network-Firewall-Multi-AZ

    Note: For simplicity, the CloudFormation stack name is AWS-Network-Firewall-Multi-AZ, but you can change this name to according to your needs and follow the same naming throughout this post.

To deploy the VPC architecture (console)

In your account, launch the AWS CloudFormation template by choosing the following Launch Stack button. It will take approximately 10 minutes for the CloudFormation stack to complete.

Select this image to open a link that starts building the CloudFormation stack

Note: The stack will launch in the N. Virginia (us-east-1) Region. To deploy this solution into other AWS Regions, download the solution’s CloudFormation template, modify it, and deploy it to the selected Region.

Deploy and set up access to the EKS cluster

In this step, you’ll use the eksctl CLI tool to create an EKS cluster.

To deploy an EKS cluster by using the eksctl tool

There are two methods for creating an EKS cluster. Method A uses the eksctl create cluster command without a configuration (config) file. Method B uses a config file.

Note: Before you start, make sure you have the VPC subnet details available from the previous procedure.

Method A: No config file

You can create an EKS cluster without a config file by using the eksctl create cluster command.

  1. From the CLI, enter the following commands.
    eksctl create cluster \
    --vpc-private-subnets=<private-subnet-A>,<private-subnet-B> \
    --vpc-public-subnets=<public-subnet-A>,<public-subnet-B>
  2. Make sure that the subnets passed to the --vpc-public-subnets parameter are protected subnets taken from the VPC architecture CloudFormation stack output. You can verify the subnet IDs by looking at step 2 in the To deploy the VPC architecture section.

Method B: With config file

Another way to create an EKS cluster is by using the following config file, with more options with the name (cluster.yaml in this example).

  1. Create a file named cluster.yaml by adding the following contents to it.
    apiVersion: eksctl.io/v1alpha5
    kind: ClusterConfig
    metadata:
      name: filter-egress-traffic-test
      region: us-east-1
      version: "1.19"
    availabilityZones: ["us-east-1a", "us-east-1b"]
    vpc:
      id: 
      subnets:
        public:
          us-east-1a: { id: <public-subnet-A> }
          us-east-1b: { id: <public-subnet-B> }
        private:
          us-east-1a: { id: <private-subnet-A> }
          us-east-1b: { id: <private-subnet-B> }
    
    managedNodeGroups:
    - name: nodegroup
      desiredCapacity: 3
      ssh:
        allow: true
        publicKeyName: main
      iam:
        attachPolicyARNs:
        - arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore
        - arn:aws:iam::aws:policy/AmazonEKSWorkerNodePolicy
        - arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly
        - arn:aws:iam::aws:policy/service-role/AmazonEC2RoleforSSM
        - arn:aws:iam::aws:policy/AmazonEKSServicePolicy
        - arn:aws:iam::aws:policy/AmazonEKSClusterPolicy
        - arn:aws:iam::aws:policy/AmazonEKS_CNI_Policy
      preBootstrapCommands:
        - yum install -y https://s3.amazonaws.com/ec2-downloads-windows/SSMAgent/latest/linux_amd64/amazon-ssm-agent.rpm
        - sudo systemctl enable amazon-ssm-agent
        - sudo systemctl start amazon-ssm-agent

  2. Run the following command to create an EKS cluster using the eksctl tool and the cluster.yaml config file.
    eksctl create cluster -f cluster.yaml

To set up access to the EKS cluster

  1. Before you deploy a sample Kubernetes Pod, make sure you have the kubeconfig file set up for the EKS cluster that you created in step 2 of To deploy an EKS cluster by using the eksctl tool. For more information, see Create a kubeconfig for Amazon EKS. You can use eksctl to do this, as follows.

    eksctl utils write-kubeconfig —cluster filter-egress-traffic-test

  2. Set the kubectl context to the EKS cluster you just created, by using the following command.

    kubectl config get-contexts

    Figure 3 shows an example of the output from this command.

    Figure 3: kubectl config get-contexts command output

    Figure 3: kubectl config get-contexts command output

  3. Copy the context name from the command output and set the context by using the following command.

    kubectl config use-context <NAME-OF-CONTEXT>

To deploy a sample Pod on the EKS cluster

  1. Next, deploy a sample Kubernetes Pod in the  EKS cluster.

    kubectl run -i --tty amazon-linux —image=public.ecr.aws/amazonlinux/amazonlinux:latest sh

    If you already have a Pod, you can use the following command to get a shell to a running container.

    kubectl attach amazon-linux -c alpine -i -t

  2. Now you can test access to a non-allowed website in the AWS Network Firewall stateful rules, using these steps.
    1. First, install the cURL tool on the sample Pod you created previously. cURL is a command-line tool for getting or sending data, including files, using URL syntax. Because cURL uses the libcurl library, it supports every protocol libcurl supports. On the Pod where you have obtained a shell to a running container, run the following command to install cURL.
      apk install curl
    2. Access a website using cURL.
      curl -I https://aws.amazon.com

      This gives a timeout error similar to the following.

      curl -I https://aws.amazon.com
      curl: (28) Operation timed out after 300476 milliseconds with 0 out of 0 bytes received

    3. Navigate to the AWS CloudWatch console and check the alert logs for Network Firewall. You will see a log entry like the following sample, indicating that the access to https://aws.amazon.com was blocked.
      {
          "firewall_name": "AWS-Network-Firewall-Multi-AZ-firewall",
          "availability_zone": "us-east-1a",
          "event_timestamp": "1623651293",
          "event": {
              "timestamp": "2021-06-14T06:14:53.483069+0000",
              "flow_id": 649458981081302,
              "event_type": "alert",
              "src_ip": "xxx.xxx.xxx.xxx",
              "src_port": xxxxx,
              "dest_ip": "xxx.xxx.xxx.xxx",
              "dest_port": 443,
              "proto": "TCP",
              "alert": {
                  "action": "blocked",
                  "signature_id": 4,
                  "rev": 1,
                  "signature": "not matching any TLS allowlisted FQDNs",
                  "category": "",
                  "severity": 1
              },
              "tls": {
                  "sni": "aws.amazon.com",
                  "version": "UNDETERMINED",
                  "ja3": {},
                  "ja3s": {}
              },
              "app_proto": "tls"
          }
      }

      The error shown here occurred because the hostname www.amazon.com was not added to the Network Firewall stateful rules allow list.

      When you deployed the network firewall in step 1 of the To deploy the VPC architecture procedure, the values provided for the CloudFormation parameter NetworkFirewallAllowedWebsites were just .amazonaws.com, .docker.io, .docker.com and not aws.amazon.com.

Update the Network Firewall stateful rules

In this procedure, you’ll update the Network Firewall stateful rules to allow the aws.amazon.com domain name.

To update the Network Firewall stateful rules (console)

  1. In the AWS CloudFormation console, locate the stack you used to create the network firewall earlier in the To deploy the VPC architecture procedure.
  2. Select the stack you want to update, and choose Update. In the Parameters section, update the stack by adding the hostname aws.amazon.com to the parameter NetworkFirewallAllowedWebsites as a comma-separated value. See Updating stacks directly in the AWS CloudFormation User Guide for more information on stack updates.

Re-test from the sample pod

In this step, you’ll test the outbound access once again from the sample Pod you created earlier in the To deploy a sample Pod on the EKS cluster procedure.

To test the outbound access to the aws.amazon.com hostname

  1. Get a shell to a running container in the sample Pod that you deployed earlier, by using the following command.
    kubectl attach amazon-linux -c alpine -i -t
  2. On the terminal where you got a shell to a running container in the sample Pod, run the following cURL command.
    curl -I https://aws.amazon.com
  3. The response should be a success HTTP 200 OK message similar to this one.
    curl -Ik https://aws.amazon.com
    HTTP/2 200
    content-type: text/html;charset=UTF-8
    server: Server

If the VPC subnets are organized according to the architecture suggested in this solution, outbound traffic from the EKS cluster can be sent to the network firewall and then filtered based on hostnames provided by SNI.

Collecting hostnames provided by the SNI

In this step, you’ll see how to configure the network firewall to collect all the hostnames provided by SNI that are accessed by an already running application—without blocking any access—by making use of CloudWatch and alert logs.

To configure the network firewall (console)

  1. In the AWS CloudFormation console, locate the stack that created the network firewall earlier in the To deploy the VPC architecture procedure.
  2. Select the stack to update, and then choose Update.
  3. Choose Replace current template and upload the template network-firewall-eks-collect-all.yaml. (This template should be available from the files that you downloaded earlier from the S3 bucket in the Prerequisites section.) Choose Next. See Updating stacks directly for more information.

To configure the network firewall (AWS CLI)

  1. Update the CloudFormation stack by using the network-firewall-eks-collect-all.yaml template file that you previously downloaded from the S3 bucket in the Prerequisites section, using the update-stack command as follows.
    aws cloudformation update-stack --stack-name AWS-Network-Firewall-Multi-AZ \
    --template-body file://network-firewall-eks-collect-all.yaml \
    --capabilities CAPABILITY_NAMED_IAM

To check the rules in the AWS Management Console

  1. In the AWS Management Console, navigate to the Amazon VPC console and locate the AWS Network Firewall tab.
  2. Select the network firewall that you created earlier, and then select the stateful rule with the name log-all-tls.
  3. The rule group should appear as shown in Figure 4, indicating that the logs are captured and sent to the Alert logs.
    Figure 4: Network Firewall rule groups

    Figure 4: Network Firewall rule groups

To test based on stateful rule

  1. On the terminal, get the shell for the running container in the Pod you created earlier. If this Pod is not available, follow the instructions in the To deploy a sample Pod on the EKS cluster procedure to create a new sample Pod.
  2. Run the cURL command to aws.amazon.com. It should return HTTP 200 OK, as follows.
    curl -Ik https://aws.amazon.com/
    HTTP/2 200
    content-type: text/html;charset=UTF-8
    server: Server
    date:
    ------
    ----------
    --------------
  3. Navigate to the AWS CloudWatch Logs console and look up the Alert logs log group with the name /AWS-Network-Firewall-Multi-AZ/anfw/alert.

    You can see the hostnames provided by SNI within the TLS protocol passing through the network firewall. The CloudWatch Alert logs for allowed hostnames in the SNI looks like the following example.

    {
        "firewall_name": "AWS-Network-Firewall-Multi-AZ-firewall",
        "availability_zone": "us-east-1b",
        "event_timestamp": "1627283521",
        "event": {
            "timestamp": "2021-07-26T07:12:01.304222+0000",
            "flow_id": 1977082435410607,
            "event_type": "alert",
            "src_ip": "xxx.xxx.xxx.xxx",
            "src_port": xxxxx,
            "dest_ip": "xxx.xxx.xxx.xxx",
            "dest_port": 443,
            "proto": "TCP",
            "alert": {
                "action": "allowed",
                "signature_id": 2,
                "rev": 0,
                "signature": "",
                "category": "",
                "severity": 3
            },
            "tls": {
                "subject": "CN=aws.amazon.com",
                "issuerdn": "C=US, O=Amazon, OU=Server CA 1B, CN=Amazon",
                "serial": "08:13:34:34:48:07:64:27:4D:BC:CB:14:4D:AF:F2:11",
                "fingerprint": "f7:53:97:5e:76:1e:fb:f6:70:72:02:95:d5:9f:2f:05:52:79:5d:ae",
                "sni": "aws.amazon.com",
                "version": "TLS 1.2",
                "notbefore": "2020-09-30T00:00:00",
                "notafter": "2021-09-23T12:00:00",
                "ja3": {},
                "ja3s": {}
            },
            "app_proto": "tls"
        }
    }

Optionally, you can also create an AWS Lambda function to collect the hostnames that are passed through the network firewall.

To create a Lambda function to collect hostnames provided by SNI (optional)

Sample Lambda code

The sample Lambda code from Figure 5 is shown following, and is written in Python 3. The sample collects the hostnames that are provided by SNI and captured in Network Firewall. Network Firewall logs the hostnames provided by SNI in the CloudWatch Alert logs. Then, by creating a CloudWatch logs subscription filter, you can send logs to the Lambda function for further processing, for example to invoke SNS notifications.

import json
import gzip
import base64
import boto3
import sys
import traceback
sns_client = boto3.client('sns')
def lambda_handler(event, context):
    try:
        decoded_event = json.loads(gzip.decompress(base64.b64decode(event['awslogs']['data'])))
        body = '''
        {filtermatch}
        '''.format(
            loggroup=decoded_event['logGroup'],
            logstream=decoded_event['logStream'],
            filtermatch=decoded_event['logEvents'][0]['message'],
        )
        # print(body)# uncomment this for debugging
        filterMatch = json.loads(body)
        data = []
        if 'http' in filterMatch['event']:
            data.append(filterMatch['event']['http']['hostname'])
        elif 'tls' in filterMatch['event']:
            data.append(filterMatch['event']['tls']['sni'])
        result = 'Trying to reach ' + 1*' ' + (data[0]) + 1*' ' 'via Network Firewall' + 1*' '  + (filterMatch['firewall_name'])
        # print(result)# uncomment this for debugging
        message = {'HostName': result}
        send_to_sns = sns_client.publish(
            TargetArn='<SNS-topic-ARN>', #Replace with the SNS topic ARN
            Message=json.dumps({'default': json.dumps(message),
                            'sms': json.dumps(message),
                            'email': json.dumps(message)}),
            Subject='Trying to reach the hostname through the Network Firewall',
            MessageStructure='json')
    except Exception as e:
        print('Function failed due to exception.')
        e = sys.exc_info()[0]
        print(e)
        traceback.print_exc()
        Status="Failure"
        Message=("Error occured while executing this. The error is %s" %e)

Clean up

In this step, you’ll clean up the infrastructure that was created as part of this solution.

To delete the Kubernetes workloads

  1. On the terminal, using the kubectl CLI tool, run the following command to delete the sample Pod that you created earlier.
    kubectl delete pods amazon-linux

    Note: Clean up all the Kubernetes workloads running on the EKS cluster. For example, if the Kubernetes service of type LoadBalancer is deployed, and if the EKS cluster where it exists is deleted, the LoadBalancer will not be deleted. The best practice is to clean up all the deployed workloads.

  2. On the terminal, using the eksctl CLI tool, delete the created EKS cluster by using the following command.
    eksctl delete cluster --name filter-egress-traffic-test

To delete the CloudFormation stack and AWS Network Firewall

  1. Navigate to the AWS CloudFormation console and choose the stack with the name AWS-Network-Firewall-Multi-AZ.
  2. Choose Delete, and then at the prompt choose Delete Stack. For more information, see Deleting a stack on the AWS CloudFormation console.

Conclusion

By following the VPC architecture explained in this blog post, you can protect the applications running on an Amazon EKS cluster by filtering the outbound traffic based on the approved hostnames that are provided by SNI in the Network Firewall Allow list.

Additionally, with a simple Lambda function, CloudWatch Logs, and an SNS topic, you can get readable hostnames provided by the SNI. Using these hostnames, you can learn about the traffic pattern for the applications that are running within the EKS cluster, and later create a strict list to allow only the required outbound traffic. To learn more about Network Firewall stateful rules, see Working with stateful rule groups in AWS Network Firewall in the AWS Network Firewall Developer Guide.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

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Kirankumar Chandrashekar

Kirankumar is a Sr. Solutions Architect for Strategic Accounts at AWS. He focuses on leading customers in architecting DevOps, containers and container technologies to name a few. Kirankumar is passionate about DevOps, Infrastructure as Code, and solving complex customer issues. He enjoys music, as well as cooking and traveling.

New additions to line charts in Amazon QuickSight

Post Syndicated from Bhupinder Chadha original https://aws.amazon.com/blogs/big-data/new-additions-to-line-charts-in-amazon-quicksight/

Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to create and deliver insights to everyone in your organization or even with your customers and partners. You can make your data come to life with rich interactive charts and create beautiful dashboards to be shared with thousands of users, either directly within the QuickSight application, or embedded in web apps and portals.

Line charts are ubiquitous to the world of data visualization and are used to visualize change in data over a dimension. They are a great way to analyze trends and patterns where data points are connected with a straight line to visualize the overall trend. In this post, we look at some of the new improvements to our line charts:

  • Support for missing data points for line and area charts
  • Improved performance and increased data limit to 10,000 data points

Missing data points

Line charts in QuickSight expect you to have data for each X axis item. If data is missing for any X axis item, it can lead to broken lines (default behavior) because there is no line drawn connecting the missing data points.

Drawing lines with points of missing data could be misleading because it would represent incorrect data, and there are valid use cases to do so. For example, imagine a scenario of a retail sales report for a given time period where data is recorded during days of operation (Monday through Saturday). In such cases, instead of displaying a broken line chart that skips Sunday, you may want to show a continuous trend by directly connecting Saturday to Monday, hiding the fact that Sunday isn’t operational. Alternatively, you may want to view store traffic for Sunday as 0 instead of displaying a broken line.

Previously, line charts only supported treating missing data for date/time fields. Now, we have added support for categorical data for both line and area charts. The following are the different line treatment options:

  1. Continuous line – Display continuous lines by directly connecting the line to the next available data point in the series
  2. Show as zero – Interpolate the missing values with zero and display a continuous line
  3. Broken line – Retain the default experience to display disjointed lines over missing values

The following diagram illustrates a line chart using each option.

This new feature applies for both categorical and time series data on area charts as well, as shown in the following graphs.

Authors can also configure different data treatments for the left and right Y axis for dual axis charts, as shown in the following example.

Increased data limit for line charts

With the recent update, we have improved line chart performance to support a maximum of 10,000 data points instead of the previous 2,500 data point limit. This also increases the limit for more line series created by the Color by field, which is also bound by the total data limit. For example, if the line chart has 1,000 data points for each series, you could display up to 10 unique colored series.

This update enables use cases where authors want to show a higher number of data points, such as hourly trends or daily trends for a year (365 data points) for multiple groups. This update doesn’t change the default limits of the Color by field (25) and X axis data point limit (100) that exist today to be compatible with existing dashboards and analysis, until authors choose to customize the limits.

Summary

In this post, we looked at how to treat missing data for line charts, where instead of viewing broken lines, you can display continuous lines. This helps you customize how you want to visualize overall trends and variations depending on the business context. Additionally, we looked at the new data handling limit for line charts, which supports 10,000 data points—four times more data than before. To learn more refer customizing missing data control.

Try out the new feature and share your feedback and questions in the comments section.


About the author

Bhupinder Chadha is a senior product manager for Amazon QuickSight focused on visualization and front end experiences. He is passionate about BI, data visualization and low-code/no-code experiences. Prior to QuickSight he was the lead product manager for Inforiver, responsible for building a enterprise BI product from ground up. Bhupinder started his career in presales, followed by a small gig in consulting and then PM for xViz, an add on visualization product.

How to automate updates for your domain list in Route 53 Resolver DNS Firewall

Post Syndicated from Guillaume Neau original https://aws.amazon.com/blogs/security/how-to-automate-updates-for-your-domain-list-in-route-53-resolver-dns-firewall/

Note: This post includes links to third-party websites. AWS is not responsible for the content on those websites.


Following the release of Amazon Route 53 Resolver DNS Firewall, Amazon Web Services (AWS) published several blog posts to help you protect your Amazon Virtual Private Cloud (Amazon VPC) DNS resolution, including How to Get Started with Amazon Route 53 Resolver DNS Firewall for Amazon VPC and Secure your Amazon VPC DNS resolution with Amazon Route 53 Resolver DNS Firewall. Route 53 Resolver DNS Firewall provides managed domain lists that are fully maintained and kept up-to-date by AWS and that directly benefit from the threat intelligence that we gather, but you might want to create or import your own list to have full control over the DNS filtering.

In this blog post, you will find a solution to automate the management of your domain list by using AWS Lambda, Amazon EventBridge, and Amazon Simple Storage Service (Amazon S3). The solution in this post uses, as an example, the URLhaus open Response Policy Zone (RPZ) list, which generates a new file every five minutes.

Architecture overview

The solution is made of the following four components, as shown in Figure 1.

  1. An EventBridge scheduled rule to invoke the Lambda function on a schedule.
  2. A Lambda function that uses the AWS SDK to perform the automation logic.
  3. An S3 bucket to temporarily store the list of domains retrieved.
  4. Amazon Route 53 Resolver DNS Firewall.
    Figure 1: Architecture overview

    Figure 1: Architecture overview

After the solution is deployed, it works as follows:

  1. The scheduled rule invokes the Lambda function every 5 minutes to fetch the latest domain list available.
  2. The Lambda function fetches the list from URLhaus, parses the data retrieved, formats the data, uploads the list of domains into the S3 bucket, and invokes the Route 53 Resolver DNS Firewall importFirewallDomains API action.
  3. The domain list is then updated.

Implementation steps

As a first step, create your own domain list on the Route 53 Resolver DNS Firewall. Having your own domain list allows you to have full control of the list of domains to which you want to apply actions, as defined within rule groups.

To create your own domain list

  1. In the Route 53 console, in the left menu, choose Domain lists in the DNS firewall section.
  2. Choose the Add domain list button, enter a name for your owned domain list, and then enter a placeholder domain to initialize the domain list.
  3. Choose Add domain list to finalize the creation of the domain list.
    Figure 2: Expected view of the console

    Figure 2: Expected view of the console

The list from URLhaus contains more than a thousand records. You will use the ImportFirewallDomains endpoint to upload this list to DNS Firewall. The use of the ImportFirewallDomains endpoint requires that you first upload the list of domains and make the list available in an S3 bucket that is located in the same AWS Region as the owned domain list that you just created.

To create the S3 bucket

  1. In the S3 console, choose Create bucket.
  2. Under General configuration, configure the AWS Region option to be the same as the Region in which you created your domain list.
  3. Finalize the configuration of your S3 bucket, and then choose Create bucket.

Because a new file is created every five minutes, we recommend setting a lifecycle rule to automatically expire and delete files after 24 hours to optimize for cost and only save the most recent lists.

To create the Lambda function

  1. Follow the steps in the topic Creating an execution role in the IAM console to create an execution role. After step 4, when you configure permissions, choose Create Policy, and then create and add an IAM policy similar to the following example. This policy needs to:
    • Allow the Lambda function to put logs in Amazon CloudWatch.
    • Allow the Lambda function to have read and write access to objects placed in the created S3 bucket.
    • Allow the Lambda function to update the firewall domain list.
    • {
          "Version": "2012-10-17",
          "Statement": [
              {
                  "Action": [
                      "logs:CreateLogGroup",
                      "logs:CreateLogStream",
                      "logs:PutLogEvents"
                  ],
                  "Resource": "arn:aws:logs:<region>:<accountId>:*",
                  "Effect": "Allow"
              },
              {
                  "Action": [
                      "s3:PutObject",
                      "s3:GetObject"
                  ],
                  "Resource": "arn:aws:s3:::<DNSFW-BUCKET-NAME>/*",
                  "Effect": "Allow"
              },
              {
                  "Action": [
                      "route53resolver:ImportFirewallDomains"
                  ],
                  "Resource": "arn:aws:route53resolver:<region>:<accountId>:firewall-domain-list/<domain-list-id>",
                  "Effect": "Allow"
              }
          ]
      }

  2. (Optional) If you decide to use the example provided by AWS:
    • After cloning the repository: Build the layer following the instruction included in the readme.md and the provided script.
    • Zip the lambda.
    • In the left menu, select Layers then Create Layer. Enter a name for the layer, then select Upload a .zip file. Choose to upload the layer (node-axios-layer.zip).
    • As a compatible runtime, select: Node.js 16.x.
    • Select Create
  3. In the Lambda console, in the same Region as your domain list, choose Create function, and then do the following:
    • Choose your desired runtime and architecture.
    • (Optional) To use the code provided by AWS: Select Node.js 16.x as the runtime.
    • Choose Change the default execution role.
    • Choose Use an existing role, and then pick the role that you just created.
  4. After the Lambda function is created, in the left menu of the Lambda console, choose Functions, and then select the function you created.
    • For Code source, you can either enter the code of the Lambda function or choose the Upload from button and then choose the source for the code. AWS provides an example of functioning code on GitHub under a MIT-0 license.

    (optional) To use the code provided by AWS:

    • Choose the Upload from button and upload the zipped code example.
    • After the code is uploaded, edit the default Runtime settings: Choose the Edit button and set the handler to be equal to: LambdaRpz.handler
    • Edit the default Layers configuration, choose the Add a layer button, select Specify an ARN and enter the ARN of the layer created during the optional step 2.
    • Edit the environment variables of the function: Select the Edit button and define the three following variables:
      1. Key : FirewallDomainListId | Value : <domain-list-id>
      2. Key : region | Value : <region>
      3. Key : s3Prefix | Value : <DNSFW-BUCKET-NAME>

The code that you place in the function will be able to fetch the list from URLhaus, upload the list as a file to S3, and start the import of domains.

For the Lambda function to be invoked every 5 minutes, next you will create a scheduled rule with Amazon EventBridge.

To automate the invoking of the Lambda function

  1. In the EventBridge console, in the same AWS Region as your domain list, choose Create rule.
  2. For Rule type, choose Schedule.
  3. For Schedule pattern, select the option A schedule that runs at a regular rate, such as every 10 minutes, and under Rate expression set a rate of 5 minutes.
    Figure 3: Console view when configuring a schedule

    Figure 3: Console view when configuring a schedule

  4. To select the target, choose AWS service, choose Lambda function, and then select the function that you previously created.

After the solution is deployed, your domain list will be updated every 5 minutes and look like the view in Figure 4.

Figure 4: Console view of the created domain list after it has been updated by the Lambda function

Figure 4: Console view of the created domain list after it has been updated by the Lambda function

Code samples

You can use the samples in the amazon-route-53-resolver-firewall-automation-examples-2 GitHub repository to ease the automation of your domain list, and the associated updates. The repository contains script files to help you with the deployment process of the AWS CloudFormation template. Note that you need to have the AWS Command Line Interface (AWS CLI) installed and properly configured in order to use the files.

To deploy the CloudFormation stack

  1. If you haven’t done so already, create an S3 bucket to store the artifacts in the Region where you wish to deploy. This name of this bucket will then be referenced as ParamS3ArtifactBucket with a value of <DOC-EXAMPLE-BUCKET-ARTIFACT>
  2. Clone the repository locally.
    git clone https://github.com/aws-samples/amazon-route-53-resolver-firewall-automation-examples-2
  3. Build the Lambda function layer. From the /layer folder, use the provided script.
    . ./build-layer.sh
  4. Zip and upload the artifact to the bucket created in step 1. From the root folder, use the provided script.
    . ./zipupload.sh <ParamS3ArtifactBucket>
  5. Deploy the AWS CloudFormation stack by using either the AWS CLI or the CloudFormation console.
    • To deploy by using the AWS CLI, from the root folder, type the following command, making sure to replace <region>, <DOC-EXAMPLE-BUCKET-ARTIFACT>, <DNSFW-BUCKET-NAME>, and <DomainListName>with your own values.
      aws --region <region> cloudformation create-stack --stack-name DNSFWStack --capabilities CAPABILITY_NAMED_IAM --template-body file://./DNSFWStack.cfn.yaml --parameters ParameterKey=ParamS3ArtifactBucket,ParameterValue=<DOC-EXAMPLE-BUCKET-ARTIFACT> ParameterKey=ParamS3RpzBucket,ParameterValue=<DNSFW-BUCKET-NAME> ParameterKey=ParamFirewallDomainListName,ParameterValue=<DomainListName>

    • To deploy by using the console, do the following:
      1. In the CloudFormation console, choose Create stack, and then choose With new resources (standard).
      2. On the creation screen, choose Template is ready, and upload the provided DNSFWStack.cfn.yaml file.
      3. Enter a stack name and configure the requested parameters with your desired configuration and outcomes. These parameters include the following:
        • The name of your firewall domain list.
        • The name of the S3 bucket that contains Lambda artifacts.
        • The name of the S3 bucket that will be created to contain the files with the domain information from URLhaus.
      4. Acknowledge that the template requires IAM permission because it will create the role for the Lambda function and manage its IAM policy, and then choose Create stack.

After a few minutes, all the resources should be created and the CloudFormation stack is now deployed. After 5 minutes, your domain list should be updated, as shown in Figure 5.

Figure 5: Console view of CloudFormation after the stack has been deployed

Figure 5: Console view of CloudFormation after the stack has been deployed

Conclusions and cost

In this blog post, you learned about creating and automating the update of a domain list that you fully control. To go further, you can extend and replicate the architecture pattern to fetch domain names from other sources by editing the source code of the Lambda function.

After the solution is in place, in order for the filtering to be effective, you need to create a rule group referencing the domain list and associate the rule group with some of your VPCs.

For cost information, see the AWS Pricing Calculator. This solution will be invoked 60 (minutes) * 24 (hours) * 30 (days) / 5 (minutes) = 8,640 times per month, invoking the Lambda function that will run for an average of 400 minutes, storing an average of 0.5 GB in Amazon S3, and creating a domain list that averages 1,500 domains. According to our public pricing, and without factoring in the AWS Free Tier, this will incur the estimated total cost of $1.43 per month for the filtering of 1 million DNS requests.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

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Guillaume Neau

Guillaume Neau

Guillaume is a solutions architect of France with an expertise in information security that focus on building solutions that improve the life of citizens.

Announcing new AWS IAM Identity Center APIs to manage users and groups at scale

Post Syndicated from Sharanya Ramakrishnan original https://aws.amazon.com/blogs/security/announcing-new-aws-iam-identity-center-apis-to-manage-users-and-groups-at-scale/

If you use AWS IAM Identity Center (successor to AWS Single Sign-On) as your identity source, you create and manage your users and groups manually in the IAM Identity Center console. However, you may prefer to automate this process to save time, spend less administrative effort, and to scale effectively as your organization grows. If you use IAM Identity Center with a supported identity provider (IdP) or Microsoft Active Directory (AD), you may want to check if the right users and groups have synced into IAM Identity Center. You can do this manually, but now you can use the new APIs to make the process easier by setting up automated checks that query this information from IAM Identity Center and notify you only when you need to intervene.

This post explains how you can use IAM Identity Center APIs to automate managing users and groups in a scalable manner and gain better visibility into users and groups in the Identity Center directory. These automations can help you save time and reduce administrative effort. We will provide some background on how IAM Identity Center works and how you can use the new APIs to help simplify your workflows.

Background

IAM Identity Center allows you to centrally manage access to all of your AWS accounts within AWS Organizations or your applications. Using IAM Identity Center is the AWS recommendation for managing the workforce identities of the human users in your organization who access AWS resources. It provides you with the flexibility to create and manage users and groups in the Identity Center directory, or bring in your users and groups from a different identity source such as Active Directory or an external identity provider (IdP). After IAM Identity Center is configured, you can look up users or groups to grant them single sign-on access to AWS accounts, applications, or both. By signing-in once in the IAM Identity Center portal, your users can access their assigned AWS accounts, as well as Identity Center enabled applications such as Amazon SageMaker Studio or Amazon EMR Studio, as well as cloud applications such as Jira, Salesforce, and Tableau.

While IAM Identity Center simplifies user access, you may prefer to manage these users and groups at scale, and audit their access regularly to meet your security requirements. You may also want to automate the process of giving users access to AWS and the resources they need to do their job. Previously, you could only manage identities in the Identity Center directory manually by using the IAM Identity Center console. Now, you can use the new Identity Center APIs to build automation that manages the Identity Center directory users and groups for you.

With these Identity Center APIs, you can build automated workflows to do the following tasks:

  • Provision and de-provision users and groups.
  • Add new members to a group or remove them from a group.
  • Query information about users and groups in the Identity Center directory.
  • Update information about users and groups.
  • Find out which users are members of which groups.

You can create automated workflows using the APIs to define who has access to AWS accounts or applications through IAM Identity Center, and provide them with the right resources to do their job. Automating workflows can save you time and can reduce your administrative effort. With the new APIs, you can auto-generate reports about users and their IAM Identity Center access configurations. These automated reports can provide you with greater visibility to evaluate your security posture.

Provision, manage, and de-provision users and groups in IAM Identity Center

As your enterprise grows, you may want to automate your administrative tasks to reduce manual effort, save time, and scale efficiently. If you’re a cloud administrator or IT administrator who manages which employees in your organization need access to AWS as part of their job role, or what AWS resources they need so they can develop applications, now you can set up automated workflows that manage this for you.

Consider the following scenario. Your organization uses the Identity Center directory as the source for user information, and a new data scientist joins your company. You want them to be granted access to log in to AWS automatically based on their job role. After they log in, you want them to have access to the AWS resources and applications that you have approved for their job role, including Amazon SageMaker, AWS Managed Grafana, and multiple S3 buckets. Previously, you had to use the AWS Management Console to manually create a new user object and then add the new data scientist to the AWS_Data_Science group. With the new APIs, you can set up an automated workflow that creates a new user and adds the user to relevant groups in the Identity Center directory, as soon as the new data scientist is added to your human resources (HR) system.

A sample AWS Identity Store operations python script called identitystore_operations.py is available in the iam-identitycenter-identitystoreapi-operations GitHub repository. This sample program shows you how you can automate Identity Store operations to create a new user, add the user to a group, list group memberships, and update the user’s group memberships operations. This sample program requires the AWS SDK for Python (Boto3). For instructions to install the AWS SDK for Python, see the Boto3 Quickstart.

The following is an example to see all supported operations available in the sample script.

python identitystore_operations.py —h

The following is example output:

usage: identitystore_operations.py [-h]
{create_user,create_group,adduser_to_group,delete_group,list_members,list_membership}
...
positional arguments:
{create_user,create_group,adduser_to_group,delete_group,list_members,list_membership}

options:
-h, --help show this help message and exit

Next is an example of how you can create the new user John Doe in the Identity Center directory and add the user to an existing AWS_Data_Science group.

python identitystore_operations.py create_user --identitystoreid d-123456a7890 --username johndoe --givenname John --familyname Doe --groupname AWS_Data_Science

The following is example output:

User:johndoe with UserId:12345678-9012-3456-789a-bcdef021345a created successfully
User:johndoe added to Group:AWS_Data_Science successfully

To continue with this example, consider a scenario where the data scientist transitions to a role as an applied scientist, and needs access to additional AWS applications and resources. Rather than using the IAM Identity Center console to manually update the user’s information and add them to the AWS_Applied_Scientists group, you can now use automation to update the user and provide them with the access they need.

The following is an example of how the previously-created user johndoe can be added to the AWS_Applied_Scientists group.

python identitystore_operations.py adduser_to_group --identitystoreid d-123456a7890 --groupname AWS_Applied_Scientists --username johndoe

The following is example output:

User:johndoe added to Group:AWS_Applied_Scientists successfully

Finally for this scenario, consider that this employee leaves your company. Rather than using the IAM Identity Center console to manually delete their user object, now your automation can delete the user as soon as they are removed from your HR system.

Evaluate your security posture in IAM Identity Center

To maintain visibility across AWS, it is a best practice to regularly audit and evaluate the security controls for any service that you use. It’s also a best practice to identify the AWS accounts or applications that an employee can access. Having access to this information helps you maintain and improve your company’s security posture. As a cloud administrator, you may need to submit periodic reports to auditors enumerating the employees who have access to AWS. If you or your IT team manage users and groups in a different source system, you also need to track whether the right users and groups are synced into AWS.

The following is an example of how you can find the members of the AWS_Applied_Scientists group.

python identitystore_operations.py list_members --identitystoreid d-123456a7890 --groupname AWS_Applied_Scientists

The following is example output:

UserName:johndoe,Display Name: John Doe

For example, consider a scenario in which you use Active Directory as your identity source. You want to confirm that the right set of users and groups have been synced into the Identity Center directory. After the users and groups are confirmed, you are required to submit this list of users and groups with access to AWS to auditors every quarter. Rather than manually verifying which employees have access to AWS and manually creating a list for the auditors, now you can use the new APIs to create a workflow that automatically queries the users and groups in the Identity Center directory, compares it to your list of intended Active Directory users and groups who should have AWS access, and provides you the information about whether there are any users or groups who have access that was not intended. Additionally, you can set up a script to generate reports every quarter for the auditors.

The following is an example of how you can find the group memberships of the specific user johndoe.

python identitystore_operations.py list_membership --identitystoreid d-123456a7890 --username johndoe

The following is example output:

User :johndoe is a member of the following groups
AWS_Data_Science
AWS_Applied_Scientists

Conclusion

In this post, you learned how to use IAM Identity Center APIs to automate managing users and groups in a scalable manner and gain better visibility into users and groups in the Identity Center directory.

The IAM Identity Center APIs for user and group management expand the capabilities of existing Identity Store APIs, helping you build scalable workflows. They help your IT and cloud teams save time and reduce administrative effort through automation.

To learn more about using IAM Identity Center or the user and group management APIs, see the AWS IAM Identity Center User Guide or the Identity Store API Reference Guide.

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 IAM Identity Center re:Post or contact AWS Support.

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Author

Sharanya Ramakrishnan

Sharanya is a Senior Technical Product Manager in the AWS Identity team. She enjoys solving customer problems through meaningful products, particularly in the dynamic security and identity space. Outside of work, Sharanya likes to travel and enjoys hiking and reading.

Author

Siva Rajamani

Siva is a Boston-based Enterprise Solutions Architect. He enjoys working closely with customers and supporting their digital transformation and AWS adoption journey. His core areas of focus are Serverless, Application Integration, and Security.

Bala KP

Bala KP

Bala KP is a Sr Partner Solutions Architect at Amazon Web Services. He helps global system integrator partners and customers in the financial services and insurance domain to move their most sensitive workloads to AWS.

Amazon DevOps Guru increases Operational Efficiency for 605

Post Syndicated from Mohit Gadkari original https://aws.amazon.com/blogs/devops/amazon-devops-guru-increases-operational-efficiency-for-605/

605 is an independent TV measurement firm that offers advertising and content measurement, full-funnel attribution, media planning, optimization, and analytical solutions, all on top of their multi-source viewership data set covering over 21 million U.S. households. 605 has built their technology solutions on AWS with dozens of accounts and tens of thousands of resources to monitor.

As 605 continues to innovate and build new solutions, the size and complexity of their AWS deployment has also grown proportionally. Over time, managing their deployment has become an operational challenge for their current team. 605 has deployed different application performance monitoring (APM) tools and notification systems to help their observability staff scale and support their growing cloud environment. However, 605 realized that their continued growth on the cloud would necessitate either increasing their observability staff or assuming some risk of potential application performance issues or even outages.

Amazon DevOps Guru allowed 605 to find a third path forward. Rather than accepting the trade-off of hiring more staff or assuming more risk, 605 discovered that DevOps Guru provides an increase in operational efficiency using their existing staff resources by applying artificial intelligence (AI) to supplement their existing APM and notification platform. Layering DevOps Guru into their DevOps environment , 605 realized a 4-fold decrease in the number of alerts and notifications that proved to be false positives. In fact, 605 went from an environment where 76.2% of their alerts and notifications were false positives, to one with only 18.9% false positives simply by adding Amazon DevOps Guru. In the end, 605 can more effectively and efficiently manage their environment with existing resources and actually freeing-up DevOps brainpower to work on more strategically important initiatives than application management.

“Amazon DevOps Guru has provided insights that help us focus our infrastructure roadmap. Our current SIEM tools require building out alerting ahead of time, while DevOps Guru is constantly evolving, which prevents becoming stagnant in our monitoring. Reducing the risk of false positive alerts has saved countless engineering hours.”

Jared Williams, VP of Infrastructure and Architecture, 605

605 without DevOps Guru had their Amazon CloudWatch and Amazon Elastic Container Service for Kubernetes ( Amazon EKS) configured with different application performance monitoring and notification systems. They saw only 23.8 % legitimate alerts and notifications, where as with the integration with DevOps Guru the legitimate alerts and notifications went up to 81% for a 6-month time period.
605 are monitoring over 13+ AWS Accounts, 20+ Amazon EKS Clusters, 500+ Pods ,15000+ EC2 Instances, 500+ S3 Buckets and 55+ Application Load Balancers with DevOps Guru

605 without DevOps Guru had their Amazon CloudWatch and Amazon Elastic Container Service for Kubernetes ( Amazon EKS) configured with different application performance monitoring and notification systems. They saw only 23.8 % legitimate alerts and notifications, where as with the integration with DevOps Guru the legitimate alerts and notifications went up to 81% for a 6-month time period.

Figure 1. 605 are monitoring over 13+ AWS Accounts, 20+ Amazon EKS Clusters, 500+ Pods ,15000+ EC2 Instances, 500+ S3 Buckets and 55+ Application Load Balancers with DevOps Guru.

Amazon DevOps Guru is a service powered by applying artificial intelligence (AI) that’s designed to make it easy to improve an application’s operational performance and availability. DevOps Guru helps detect behaviors that deviate from normal operating patterns so that you can identify operational issues long before they impact your applications. DevOps Guru utilizes ML models informed by years of Amazon.com and AWS operational excellence to identify anomalous application behavior (for example, increased latency, error rates, resource constraints, and others). Furthermore, it helps surface critical issues that could cause potential outages or service disruptions. When DevOps Guru identifies a critical issue, it automatically sends an alert and provides a summary of related anomalies, the likely root cause, and context for when and where the issue occurred. When possible, DevOps Guru also helps provide recommendations regarding how to remediate the issue. DevOps Guru ingests operational data from your AWS applications and provides a single dashboard to visualize issues in your operational data. DevOps Guru can be enabled for all of the resources in your AWS account, resources in your AWS CloudFormation Stacks, or resources grouped together by AWS Tags, with no manual setup or ML expertise required.

The value of DevOps Guru for 605 goes beyond providing operational efficiency and avoiding the choice of adding DevOps resources or assuming more risk. DevOps Guru also discovered issues with application performance that their existing solutions weren’t trained to inspect.

This new data allowed 605 to avoid a potential problem that they didn’t otherwise know would occur. As DevOps Guru doesn’t require any set-up beyond enabling the service and choosing resources to monitor (it’s a managed service), the service can surface issues without any prior configuration.

In the end, the value of DevOps Guru for 605 surfaces in three ways. First, it increases operational efficiency by allowing their existing DevOps team to more effectively manage its AWS applications and resources, as well as the room to grow along with their business needs. Second, DevOps Guru reduces operational fatigue and allows their DevOps teams to focus on more strategic issues by significantly reducing false positives. Lastly, DevOps Guru can find operational issues to which existing APM tools may not be configured or able to detect.

Start monitoring your AWS applications with AWS DevOps Guru today using this link

About the authors:

Mohit Gadkari

Mohit Gadkari is a Solutions Architect at Amazon Web Services (AWS) supporting SMB customers. He has been professionally using AWS since 2015 specializing in DevOps and Cloud Security and currently he is using this experience to help customers navigate the cloud.

Pauly Longani

Pauly is an Enterprise Support Lead at AWS, USA. He is a customer advocate and supports his customers in their cloud journey. He is passionate about the cloud and how it can be leveraged to overcome challenges across industry verticals.

Jared Williams

Jared, VP of Infrastructure and Architecture at 605, is in his 15th year managing or working on teams with DevOps type focuses. He has been involved with AWS since 2009. He manages the multi-team DevOps department at 605 where he has been for more than three years. Jared also co-founded a 24,000+ person DevOps community.

How to subscribe to the new Security Hub Announcements topic for Amazon SNS

Post Syndicated from Mike Saintcross original https://aws.amazon.com/blogs/security/how-to-subscribe-to-the-new-security-hub-announcements-topic-for-amazon-sns/

With AWS Security Hub you are able to manage your security posture in AWS, perform security best practice checks, aggregate alerts, and automate remediation. Now you are able to use Amazon Simple Notification Service (Amazon SNS) to subscribe to the new Security Hub Announcements topic to receive updates about new Security Hub services and features, newly supported standards and controls, and other Security Hub changes.

Introducing the Security Hub Announcements topic

Amazon SNS follows the publish/subscribe (pub/sub) messaging model, in which notifications are delivered to you by using a push mechanism that eliminates the need for you to periodically check or poll for new information and updates. You can now use this push mechanism to receive notifications about Security Hub by subscribing to the dedicated Security Hub Announcements topic.

The Security Hub Announcements topic publishes the following types of notifications:

  • General notifications
  • Upcoming standards and controls
  • New AWS Regions supported
  • New standards and controls
  • Updated standards and controls
  • Retired standards and controls
  • Updates to the AWS Security Finding Format (ASFF)
  • New integrations
  • New features
  • Changes to existing features

How to use the Security Hub Announcements topic

You can subscribe to the SNS topic for Security Hub Announcements to receive notification messages about newly released finding types, updates to the existing finding types, and other functionality changes. By subscribing to the SNS topic, you will receive Security Hub Announcements messages as soon as they are published. The notifications are available in all protocols that Amazon SNS supports, such as email and SMS. For more information about supported protocols in Amazon SNS, see Subscribing to an Amazon SNS topic.

The Security Hub Announcements topic is available in all AWS Regions in the aws and aws-cn partitions, but is not yet available in the AWS GovCloud (US) Regions (the aws-us-gov partition). Later in this post, we’ll show you how to subscribe to the Security Hub Announcements topic in a specific AWS Region by using the topic Amazon Resource Name (ARN) for that Region. The SNS topic messages are the same across Regions in a partition, so you can choose to subscribe to only one Region in a partition to avoid receiving duplicate information.

However, if you want to invoke an AWS Lambda function in reaction to a Security Hub Announcements message, you must subscribe to the topic ARN that is in the same Region as the Lambda function. The Lambda function can receive the SNS topic message payload as an input parameter and manipulate the information in the message, publish the message to other SNS topics, or send the message to other AWS services. For more information, see Subscribing a function to a topic in the Amazon SNS Developer Guide.

The same is true if you want to subscribe an Amazon Simple Queue Service (Amazon SQS) queue to the Security Hub Announcements topic, you must use a topic ARN that is in the same Region as the SQS queue. The SQS queue can be used to persist announcement SNS topic messages in the queue for other applications to process at a later time. For more information, see Subscribing an Amazon SQS queue to an Amazon SNS topic in the Amazon SQS Developer Guide.

IAM permissions

Your user account must have sns::subscribe AWS Identity and Access Management (IAM) permissions to subscribe to an SNS topic. For more information on IAM permissions for Amazon SNS, see Using identity-based policies with Amazon SNS.

Subscribe to the Security Hub Announcements topic

The following is the list of Security Hub Announcements topic ARNs for each currently supported Region. The examples in this post use the US West (Oregon) Region (us-west-2), but you can update the procedures with one of the following ARNs to use a different supported Region.

Security Hub Announcements topic ARNs by Region

arn:aws:sns:us-east-1:088139225913:SecurityHubAnnouncements
arn:aws:sns:us-east-2:291342846459:SecurityHubAnnouncements
arn:aws:sns:us-west-1:137690824926:SecurityHubAnnouncements
arn:aws:sns:us-west-2:393883065485:SecurityHubAnnouncements
arn:aws:sns:eu-central-1:871975303681:SecurityHubAnnouncements
arn:aws:sns:eu-north-1:191971010772:SecurityHubAnnouncements
arn:aws:sns:eu-south-1:151363035580:SecurityHubAnnouncements
arn:aws:sns:eu-west-1:705756202095:SecurityHubAnnouncements
arn:aws:sns:eu-west-2:883600840440:SecurityHubAnnouncements
arn:aws:sns:eu-west-3:313420042571:SecurityHubAnnouncements
arn:aws:sns:ca-central-1:137749997395:SecurityHubAnnouncements
arn:aws:sns:sa-east-1:359811883282:SecurityHubAnnouncements
arn:aws:sns:me-south-1:585146626860:SecurityHubAnnouncements
arn:aws:sns:af-south-1:463142546776:SecurityHubAnnouncements
arn:aws:sns:ap-northeast-1:592469075483:SecurityHubAnnouncements
arn:aws:sns:ap-northeast-2:374299265323:SecurityHubAnnouncements
arn:aws:sns:ap-northeast-3:633550238216:SecurityHubAnnouncements
arn:aws:sns:ap-southeast-1:512267288502:SecurityHubAnnouncements
arn:aws:sns:ap-southeast-2:475730049140:SecurityHubAnnouncements
arn:aws:sns:ap-southeast-3:627843640627:SecurityHubAnnouncements
arn:aws:sns:ap-east-1:464812404305:SecurityHubAnnouncements
arn:aws:sns:ap-south-1:707356269775:SecurityHubAnnouncements
arn:aws-cn:sns:cn-north-1:672341567257:SecurityHubAnnouncements
arn:aws-cn:sns:cn-northwest-1:672534482217:SecurityHubAnnouncements

The two procedures that follow show you how to subscribe an email address to the Security Hub Announcements topic by using the AWS Management Console and the AWS CLI.

To subscribe an email address to the Security Hub Announcements topic (console)

  1. Sign in to the Amazon SNS console.
  2. In the Region list, choose the same Region as the topic ARN to which you want to subscribe. This example uses the us-west-2 Region.
  3. In the left navigation pane, choose Subscriptions, then choose Create subscription.
  4. In the Create subscription dialog box, do the following:
    • For Topic ARN, paste the following topic ARN for the us-west-2 Region, or use one of the ARNs listed above for a different supported Region:

      arn:aws:sns:us-west-2:393883065485:SecurityHubAnnouncements

    • For Protocol, choose Email.
    • For Endpoint, enter an email address that you can use to receive the notification.
  5. Choose Create subscription.
  6. In your email application, open the message from AWS Notifications and open the link to confirm your subscription. Your web browser displays a confirmation response from Amazon SNS, similar to that shown in Figure 1.

    Figure 1: SNS notification subscription confirmation

    Figure 1: SNS notification subscription confirmation

The following steps show you how to subscribe an email address to the Security Hub Announcements topic by using the AWS Command Line Interface (AWS CLI).

To subscribe an email address to the Security Hub Announcements topic (AWS CLI)

  1. Run the following command in the AWS CLI, replacing <[email protected]> with your email address, and optionally replacing the ARN and reference to us-west-2 if you want to use a different Region:
    aws sns --region us-west-2 subscribe --topic-arn arn:aws:sns:us-west-2:393883065485:SecurityHubAnnouncements --protocol email --notification-endpoint <[email protected]>
  2. In your email application, open the message from AWS Notifications and open the link to confirm your subscription.
  3. Your web browser displays a confirmation response from Amazon SNS, similar to that shown in Figure 1.

Example subscription responses

The following sections contain examples of a message announcing new standard controls supported by Security Hub in email and sqs protocol types.

Example message from an email subscription (protocol type: email)

{"AnnouncementType":"NEW_STANDARDS_CONTROLS", “Title”:”[New Controls] 36 new Security Hub controls added to the AWS Foundational Security Best Practices standard”, "Description":"We have added 36 new controls to the AWS Foundational Security Best Practices standard. These include controls for Amazon Auto Scaling (AutoScaling.3, AutoScaling.4, AutoScaling.6), AWS CloudFormation (CloudFormation.1), Amazon CloudFront (CloudFront.10), Amazon Elastic Compute Cloud (Amazon EC2) (EC2.23, EC2.24, EC2.27), Amazon Elastic Container Registry (Amazon ECR) (ECR.1, ECR.2), Amazon Elastic Container Service (Amazon ECS) (ECS.3, ECS.4, ECS.5, ECS.8, ECS.10, ECS.12), Amazon Elastic File System (Amazon EFS) (EFS.3, EFS.4), Amazon Elastic Kubernetes Service (Amazon EKS) (EKS.2), Elastic Load Balancing (ELB.12, ELB.13, ELB.14), Amazon Kinesis (Kinesis.1), AWS Network Firewall (NetworkFirewall.3, NetworkFirewall.4, NetworkFirewall.5), Amazon OpenSearch Service (Opensearch.7), Amazon Redshift (Redshift.9), Amazon Simple Storage Service (Amazon S3) (S3.13), Amazon Simple Notification Service (SNS.2), AWF WAF (WAF.2, WAF.3, WAF.4, WAF.6, WAF.7, WAF.8). If you enabled the AWS Foundational Security Best Practices standard in an account and configured Security Hub to automatically enable new controls, these controls are enabled by default. Availability of controls can vary by Region."}

Example message from an SQS queue subscription (protocol type: sqs)

The following message shows the additional metadata included with an SQS subscription to the Security Hub Announcements topic. For more information about the metadata included in an SNS topic message delivered to an SQS queue, see Fanout to Amazon SQS Queues.

{
  "Type" : "Notification",
  "MessageId" : "c9c03e46-69df-5c3c-84e9-6520708ac394",
  "TopicArn" : "arn:aws:sns:us-west-2:393883065485:SecurityHubAnnouncements",
  "Message" : "{\"AnnouncementType\":\"NEW_STANDARDS_CONTROLS\",\"Title\":\"[New Controls] 36 new Security Hub controls added to the AWS Foundational Security Best Practices standard\",\"Description\":\"We have added 36 new controls to the AWS Foundational Security Best Practices standard. These include controls for Amazon Auto Scaling (AutoScaling.3, AutoScaling.4, AutoScaling.6), AWS CloudFormation (CloudFormation.1), Amazon CloudFront (CloudFront.10), Amazon Elastic Compute Cloud (Amazon EC2) (EC2.23, EC2.24, EC2.27), Amazon Elastic Container Registry (Amazon ECR) (ECR.1, ECR.2), Amazon Elastic Container Service (Amazon ECS) (ECS.3, ECS.4, ECS.5, ECS.8, ECS.10, ECS.12), Amazon Elastic File System (Amazon EFS) (EFS.3, EFS.4), Amazon Elastic Kubernetes Service (Amazon EKS) (EKS.2), Elastic Load Balancing (ELB.12, ELB.13, ELB.14), Amazon Kinesis (Kinesis.1), AWS Network Firewall (NetworkFirewall.3, NetworkFirewall.4, NetworkFirewall.5), Amazon OpenSearch Service (Opensearch.7), Amazon Redshift (Redshift.9), Amazon Simple Storage Service (Amazon S3) (S3.13), Amazon Simple Notification Service (SNS.2), AWF WAF (WAF.2, WAF.3, WAF.4, WAF.6, WAF.7, WAF.8). If you enabled the AWS Foundational Security Best Practices standard in an account and configured Security Hub to automatically enable new controls, these controls are enabled by default. Availability of controls can vary by Region. \"}",
  "Timestamp" : "2022-08-04T18:59:33.319Z",
  "SignatureVersion" : "1",
  "Signature" : "GdKokPEUexpKZn5da5u/p5eZF1cE3JUyL0uPVKmPnDzd3orkk5jJ211VsOflUFi6V9lSXF/V6RBpQN/9f3+JBFBprng7BRQwT9I4jSa1xOn1L3xKXEVGvWI6nl1oDqBl21Pj3owV+NZ+Exd2W0dpgg8B1LG4bYq5T73MjHjWGtelcBa15TpIz/+rynqanXCKCvc/50V/XZLjA5M7gU6Dzs9CULIjkdEpCsw5FvSxbtkEd6Ktx4LH7Zq6FlPKNli3EaEHRKh9uYPo6sR/yvF4RWg3E9O4dVsK7A8uTdR+pwVCU1M601KMRxO1OWF8VIdvyPINJND8Nu/70GRA2L+MRA==",
  "SigningCertURL" : "https://sns.us-west-2.amazonaws.com/SimpleNotificationService-56e67fcb41f6fec09b0196692625d385.pem",
  "UnsubscribeURL" : "https://sns.us-west-2.amazonaws.com/?Action=Unsubscribe&SubscriptionArn=arn:aws:sns:us-west-2:393883065485:SecurityHubAnnouncements:1eb29a83-8726-4366-891c-293ad5e35a53"
}

Note: You need to set up the SQS access policy in order for SNS to push message to the SNS queue. For more information, see Basic examples of Amazon SQS policies.

Available now

The SNS topic for Security Hub Announcements is available today in the Regions described in this post. Subscribe now to stay informed of Security Hub updates. With Amazon SNS, there is no minimum fee, and you pay only for what you use. For more information, see the Amazon SNS pricing page.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support. You can also start a new thread on AWS Security Hub re:Post to get answers from the community.

Want more AWS Security news? Follow us on Twitter.

Mike Saintcross

Mike Saintcross

Mike Saintcross is a Security Consultant at AWS helping enterprise customers achieve their cloud security goals in an ever-changing threat landscape. His background is in Security Engineering with a focus on deep packet inspection, incident response, security orchestration, and automation.

Neha Joshi

Neha Joshi

Neha Joshi is a Senior Solutions Architect at AWS. She loves to brainstorm and develop solutions to help customers be successful on AWS. Outside of work, she enjoys hiking and audio books.

AWS announces migration plans for NIST 800-53 Revision 5

Post Syndicated from James Mueller original https://aws.amazon.com/blogs/security/aws-announces-migration-plans-for-nist-800-53-revision-5/

Amazon Web Services (AWS) is excited to begin migration plans for National Institute of Standards and Technology (NIST) 800-53 Revision 5.

The NIST 800-53 framework is a regulatory standard that defines the minimum baseline of security controls for U.S. federal information systems. In 2020, NIST released Revision 5 of the framework to improve security standards for industry partners and government agencies. The set of NIST 800-53 controls provides a foundation for additional laws and regulations within the U.S. government.

The Federal Information Security Modernization Act (FISMA) of 2014 is a law that requires federal agencies and contractors to meet information security standards. The Federal Risk and Authorization Management Program (FedRAMP) is a federal government program that provides a standardized approach to security assessment, authorization, and continuous monitoring of cloud services. Both FISMA and FedRAMP rely on the NIST 800-53 framework.

NIST 800-53 Revision 5

AWS meets the NIST 800-53 Revision 4 regulatory standards mandated by government authorities. NIST added numerous security enhancements, such as privacy and supply chain management, to Revision 5 to keep abreast of emerging threats to federal information systems.

In preparation for federal regulators to accept NIST 800-53 Revision 5 as the new requirement standard, AWS has begun efforts to adapt to the new security controls, processes, and procedures. AWS security compliance teams have analyzed the new requirements and launched a project to implement the updates. Although AWS is not required to migrate to the new Revision 5 standard until NIST announces the official regulatory compliance deadline, we are already taking steps to meet the deadline.

To learn more about AWS compliance programs, see the AWS Compliance Programs page.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

James Mueller

James Mueller

James is a Security Assurance Manager for AWS. For over 20 years, he has served customers in the private, public, and non-profit sectors delivering innovative information technology solutions. He currently leads security compliance efforts to drive adoption of AWS services.

How to deploy AWS Network Firewall by using AWS Firewall Manager

Post Syndicated from Harith Gaddamanugu original https://aws.amazon.com/blogs/security/how-to-deploy-aws-network-firewall-by-using-aws-firewall-manager/

AWS Network Firewall helps make it easier for you to secure virtual networks at scale inside Amazon Web Services (AWS). Without having to worry about availability, scalability, or network performance, you can now deploy Network Firewall with the AWS Firewall Manager service. Firewall Manager allows administrators in your organization to apply network firewalls across accounts. This post will take you through different deployment models and demonstrate with step-by-step instructions how this can be achieved.

Here’s a quick overview of the services used in this blog post:

  • Amazon Virtual Private Cloud (Amazon VPC) is a logically isolated virtual network. It has inbuilt network security controls and routing between VPC subnets by design. An internet gateway is a horizontally scaled, redundant, and highly available VPC component that allows communication between your VPC and the internet.
  • AWS Transit Gateway is a service that connects your VPCs to each other, to on-premises networks, to virtual private networks (VPNs), and to the internet through a central hub.
  • AWS Network Firewall is a service that secures network traffic at the organization and account levels. AWS Network Firewall policies govern the monitoring and protection behavior of these firewalls. The specifics of these policies are defined in rule groups. A rule group consists of rules that define reusable criteria for inspecting and processing network traffic. Network Firewall can support thousands of rules that can be based on a domain, port, protocol, IP address, or pattern matching.
  • AWS Firewall Manager is a security management service that acts as a central place for you to configure and deploy firewall rules across AWS Regions, accounts, and resources in AWS Organizations. Firewall Manager helps you to ensure that all firewall rules are consistently enforced, even as new accounts and resources are created. Firewall Manager integrates with AWS Network Firewall, Amazon Route 53 Resolver DNS Firewall, AWS WAF, AWS Shield Advanced, and Amazon VPC security groups.

Deployment models overview

When it comes to securing multiple AWS accounts, security teams categorize firewall deployment into centralized or distributed deployment models. Firewall Manager supports Network Firewall deployment in both modes. There are multiple additional deployment models available with Network Firewall. For more information about these models, see the blog post Deployment models for AWS Network Firewall.

Centralized deployment model

Network Firewall can be centrally deployed as an Amazon VPC attachment to a transit gateway that you set up with AWS Transit Gateway. Transit Gateway acts as a network hub and simplifies the connectivity between VPCs as well as on-premises networks. Transit Gateway also provides inter-Region peering capabilities to other transit gateways to establish a global network by using the AWS backbone. In a centralized transit gateway model, Firewall Manager can create one or more firewall endpoints for each Availability Zone within an inspection VPC. Network Firewall deployed in a centralized model covers the following use cases:

  • Filtering and inspecting traffic within a VPC or in transit between VPCs, also known as east-west traffic.
  • Filtering and inspecting ingress and egress traffic to and from the internet or on-premises networks, also known as north-south traffic.

Distributed deployment model

With the distributed deployment model, Firewall Manager creates endpoints into each VPC that requires protection. Each VPC is protected individually and VPC traffic isolation is retained. You can either customize the endpoint location by specifying which Availability Zones to create firewall endpoints in, or Firewall Manager can automatically create endpoints in those Availability Zones that have public subnets. Each VPC does not require connectivity to any other VPC or transit gateway. Network Firewall configured in a distributed model addresses the following use cases:

  • Protect traffic between a workload in a public subnet (for example, an EC2 instance) and the internet. Note that the only recommended workloads that should have a network interface in a public subnet are third-party firewalls, load balancers, and so on.
  • Protect and filter traffic between an AWS resource (for example Application Load Balancers or Network Load Balancers) in a public subnet and the internet.

Deploying Network Firewall in a centralized model with Firewall Manager

The following steps provide a high-level overview of how to configure Network Firewall with Firewall Manager in a centralized model, as shown in Figure 1.

Overview of how to configure a centralized model

  1. Complete the steps described in the AWS Firewall Manager prerequisites.
  2. Create an Inspection VPC in each Firewall Manager member account. Firewall Manager will use these VPCs to create firewalls. Follow the steps to create a VPC.
  3. Create the stateless and stateful rule groups that you want to centrally deploy as an administrator. For more information, see Rule groups in AWS Network Firewall.
  4. Build and deploy Firewall Manager policies for Network Firewall, based on the rule groups you defined previously. Firewall Manager will now create firewalls across these accounts.
  5. Finish deployment by updating the related VPC route tables in the member account, so that traffic gets routed through the firewall for inspection.
    Figure 1: Network Firewall centralized deployment model

    Figure 1: Network Firewall centralized deployment model

The following steps provide a detailed description of how to configure Network Firewall with Firewall Manager in a centralized model.

To deploy network firewall policy centrally with Firewall Manager (console)

  1. Sign in to your Firewall Manager delegated administrator account and open the Firewall Manager console under AWS WAF and Shield services.
  2. In the navigation pane, under AWS Firewall Manager, choose Security policies.
  3. On the Filter menu, select the AWS Region where your application is hosted, and choose Create policy. In this example, we choose US East (N. Virginia).
  4. As shown in Figure 2, under Policy details, choose the following:
    1. For AWS services, choose AWS Network Firewall.
    2. For Deployment model, choose Centralized.
      Figure 2: Network Firewall Manager policy type and Region for centralized deployment

      Figure 2: Network Firewall Manager policy type and Region for centralized deployment

  5. Choose Next.
  6. Enter a policy name.
  7. In the AWS Network Firewall policy configuration pane, you can choose to configure both stateless and stateful rule groups along with their logging configurations. In this example, we are not creating any rule groups and keep the default configurations, as shown in Figure 3. If you would like to add a rule group, you can create rule groups here and add them to the policy.
    Figure 3: AWS Network Firewall policy configuration

    Figure 3: AWS Network Firewall policy configuration

  8. Choose Next.
  9. For Inspection VPC configuration, select the account and add the VPC ID of the inspection VPC in each of the member accounts that you previously created, as shown in Figure 4. In the centralized model, you can only select one VPC under a specific account as the inspection VPC.
    Figure 4: Inspection VPC configuration

    Figure 4: Inspection VPC configuration

  10. For Availability Zones, select the Availability Zones in which you want to create the Network Firewall endpoint(s), as shown in Figure 5. You can select by Availability Zone name or Availability Zone ID. Optionally, if you want to specify the CIDR for each Availability Zone, or specify the subnets for firewall subnets, then you can add the CIDR blocks. If you don’t provide CIDR blocks, Firewall Manager queries your VPCs for available IP addresses to use. If you provide a list of CIDR blocks, Firewall Manager searches for new subnets only in the CIDR blocks that you provide.
    Figure 5: Network Firewall endpoint Availability Zones configuration

    Figure 5: Network Firewall endpoint Availability Zones configuration

  11. Choose Next.
  12. For Policy scope, choose VPC, as shown in Figure 6.
    Figure 6: Firewall Manager policy scope configuration

    Figure 6: Firewall Manager policy scope configuration

  13. For Resource cleanup, choose Automatically remove protections from resources that leave the policy scope. When you select this option, Firewall Manager will automatically remove Firewall Manager managed protections from your resources when a member account or a resource leaves the policy scope. Choose Next.
  14. For Policy tags, you don’t need to add any tags. Choose Next.
  15. Review the security policy, and then choose Create policy.
  16. To route traffic for inspection, you manually update the route configuration in the member accounts. Exactly how you do this depends on your architecture and the traffic that you want to filter. For more information, see Route table configurations for AWS Network Firewall.

Note: In current versions of Firewall Manager, centralized policy only supports one inspection VPC per account. If you want to have multiple inspection VPCs in an account to inspect multiple firewalls, you cannot deploy all of them through Firewall Manager centralized policy. You have to manually deploy to the network firewalls in each inspection VPC.

Deploying Network Firewall in a distributed model with Firewall Manager

The following steps provide a high-level overview of how to configure Network Firewall with Firewall Manager in a distributed model, as shown in Figure 7.

Overview of how to configure a distributed model

  1. Complete the steps described in the AWS Firewall Manager prerequisites.
  2. Create a new VPC with a desired tag in each Firewall Manager member account. Firewall Manager uses these VPC tags to create network firewalls in tagged VPCs. Follow these steps to create a VPC.
  3. Create the stateless and stateful rule groups that you want to centrally deploy as an administrator. For more information, see Rule groups in AWS Network Firewall.
  4. Build and deploy Firewall Manager policy for network firewalls into tagged VPCs based on the rule groups that you defined in the previous step.
  5. Finish deployment by updating the related VPC route tables in the member accounts to begin routing traffic through the firewall for inspection.
    Figure 7: Network Firewall distributed deployment model

    Figure 7: Network Firewall distributed deployment model

The following steps provide a detailed description how to configure Network Firewall with Firewall Manager in a distributed model.

To deploy Network Firewall policy distributed with Firewall Manager (console)

  1. Create new VPCs in member accounts and tag them. In this example, you launch VPCs in the US East (N. Virginia) Region. Create a new VPC in a member account by using the VPC wizard, as follows.
    1. Choose VPC with a Single Public Subnet. For this example, select a subnet in the us-east-1a Availability Zone.
    2. Add a desired tag to this VPC. For this example, use the key Network Firewall and the value yes. Make note of this tag key and value, because you will need this tag to configure the policy in the Policy scope step.
  2. Sign in to your Firewall Manager delegated administrator account and open the Firewall Manager console under AWS WAF and Shield services.
  3. In the navigation pane, under AWS Firewall Manager, choose Security policies.
  4. On the Filter menu, select the AWS Region where you created VPCs previously and choose Create policy. In this example, you choose US East (N. Virginia).
    1. For AWS services, choose AWS Network Firewall.
    2. For Deployment model, choose Distributed, and then choose Next.
      Figure 8: Network Firewall Manager policy type and Region for distributed deployment

      Figure 8: Network Firewall Manager policy type and Region for distributed deployment

  5. Enter a policy name.
  6. On the AWS Network Firewall policy configuration page, you can configure both stateless and stateful rule groups, along with their logging configurations. In this example you are not creating any rule groups, so you choose the default configurations, as shown in Figure 9. If you would like to add a rule group, you can create rule groups here and add them to the policy.
    Figure 9: Network Firewall policy configuration

    Figure 9: Network Firewall policy configuration

  7. Choose Next.
  8. In the Configure AWS Network Firewall Endpoint section, as shown in Figure 10, you can choose Custom endpoint configuration or Automatic endpoint configuration. In this example, you choose Custom endpoint configuration and select the us-east-1a Availability Zone. Optionally, if you want to specify the CIDR for each Availability Zone or specify the subnets for firewall subnets, then you can add the CIDR blocks. If you don’t provide CIDR blocks, Firewall Manager queries your VPCs for available IP addresses to use. If you provide a list of CIDR blocks, Firewall Manager searches for new subnets only in the CIDR blocks that you provide.
    Figure 10: Network Firewall endpoint Availability Zones configuration

    Figure 10: Network Firewall endpoint Availability Zones configuration

  9. Choose Next.
  10. For AWS Network Firewall route configuration, choose the following options, as shown in Figure 11. This will monitor the route configuration using the administrator account, to help ensure that traffic is routed as expected through the network firewalls.
    1. For Route management, choose Monitor.
    2. Under Traffic type, for Internet gateway, choose Add to firewall policy.
    3. Select the checkbox for Allow required cross-AZ traffic, and then choose Next.
      Figure 11: Network Firewall route management configuration

      Figure 11: Network Firewall route management configuration

  11. For Policy scope, select the following options to create network firewalls in previously tagged VPCs, as shown in Figure 12.
    1. For AWS accounts this policy applies to, choose All accounts under my AWS organization.
    2. For Resource type, choose VPC.
    3. For Resources, choose Include only resources that have the specified tags.
    4. For Key, enter Network Firewall. For Value, Enter Yes. The tag you are using here is the same tag defined in step 1.
      Figure 12: AWS Firewall Manager policy scope configuration

      Figure 12: AWS Firewall Manager policy scope configuration

      Important: Be careful when defining the policy scope. Each policy creates Network Firewall endpoints in all the VPCs and their Availability Zones that are within the policy scope. If you select an inappropriate scope, it could result in the creation of a large number of network firewalls and incur significant charges for AWS Network Firewall.

  12. For Resource cleanup, select the Automatically remove protections from resources that leave the policy scope check box, and then choose Next.
    Figure 13: Firewall Manager Resource cleanup configuration

    Figure 13: Firewall Manager Resource cleanup configuration

  13. For Policy tags, you don’t need to add any tags. Choose Next.
  14. Review the security policy, and then choose Create policy.
  15. To route traffic for inspection, you need to manually update the route configuration in the member accounts. Exactly how you do this depends on your architecture and the traffic that you want to filter. For more information, see Route table configurations for AWS Network Firewall.

Clean up

To avoid incurring future charges, delete the resources you created for this solution.

To delete Firewall Manager policy (console)

  1. Sign in to your Firewall Manager delegated administrator account and open the Firewall Manager console under AWS WAF and Shield services
  2. In the navigation pane, choose Security policies.
  3. Choose the option next to the policy that you want to delete.
  4. Choose Delete all policy resources, and then choose Delete. If you do not select Delete all policy resources, then only the firewall policy on the administrator account will be deleted, not network firewalls deployed in the other accounts in AWS Organizations.

To delete the VPCs you created as prerequisites

Conclusion

In this blog post, you learned how you can use either a centralized or a distributed deployment model for Network Firewall, so developers in your organization can build firewall rules, create security policies, and enforce them in a consistent, hierarchical manner across your entire infrastructure. As new applications are created, Firewall Manager makes it easier to bring new applications and resources into a consistent state by enforcing a common set of security rules.

For information about pricing, see the pages for AWS Firewall Manager pricing and AWS Network Firewall pricing. For more information, see the other AWS Network Firewall posts on the AWS Security Blog. Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.

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 the AWS Firewall Manager re:Post or contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Harith Gaddamanugu

Harith Gaddamanugu

Harith works at AWS as a Sr. Edge Specialist Solutions Architect. He stays motivated by solving problems for customers across AWS Perimeter Protection and Edge services. When he is not working, he enjoys spending time outdoors with friends and family.

Yang Liu

Yang Liu

Yang works as cloud support engineer II with AWS. On a daily basis, he provides solutions for customers’ cloud architecture questions related to networking infrastructure and the security domain. Outside of work, Yang loves traveling with his family and two Corgis, Cookie and Cache.

How to export AWS Security Hub findings to CSV format

Post Syndicated from Andy Robinson original https://aws.amazon.com/blogs/security/how-to-export-aws-security-hub-findings-to-csv-format/

AWS Security Hub is a central dashboard for security, risk management, and compliance findings from AWS Audit Manager, AWS Firewall Manager, Amazon GuardDuty, IAM Access Analyzer, Amazon Inspector, and many other AWS and third-party services. You can use the insights from Security Hub to get an understanding of your compliance posture across multiple AWS accounts. It is not unusual for a single AWS account to have more than a thousand Security Hub findings. Multi-account and multi-Region environments may have tens or hundreds of thousands of findings. With so many findings, it is important for you to get a summary of the most important ones. Navigating through duplicate findings, false positives, and benign positives can take time.

In this post, we demonstrate how to export those findings to comma separated values (CSV) formatted files in an Amazon Simple Storage Service (Amazon S3) bucket. You can analyze those files by using a spreadsheet, database applications, or other tools. You can use the CSV formatted files to change a set of status and workflow values to align with your organizational requirements, and update many or all findings at once in Security Hub.

The solution described in this post, called CSV Manager for Security Hub, uses an AWS Lambda function to export findings to a CSV object in an S3 bucket, and another Lambda function to update Security Hub findings by modifying selected values in the downloaded CSV file from an S3 bucket. You use an Amazon EventBridge scheduled rule to perform periodic exports (for example, once a week). CSV Manager for Security Hub also has an update function that allows you to update the workflow, customer-specific notation, and other customer-updatable values for many or all findings at once. If you’ve set up a Region aggregator in Security Hub, you should configure the primary CSV Manager for Security Hub stack to export findings only from the aggregator Region. However, you may configure other CSV Manager for Security Hub stacks that export findings from specific Regions or from all applicable Regions in specific accounts. This allows application and account owners to view their own Security Hub findings without having access to other findings for the organization.

How it works

CSV Manager for Security Hub has two main features:

  • Export Security Hub findings to a CSV object in an S3 bucket
  • Update Security Hub findings from a CSV object in an S3 bucket

Overview of the export function

The overview of the export function CsvExporter is shown in Figure 1.

Figure 1: Architecture diagram of the export function

Figure 1: Architecture diagram of the export function

Figure 1 shows the following numbered steps:

  1. In the AWS Management Console, you invoke the CsvExporter Lambda function with a test event.
  2. The export function calls the Security Hub GetFindings API action and gets a list of findings to export from Security Hub.
  3. The export function converts the most important fields to identify and sort findings to a 37-column CSV format (which includes 12 updatable columns) and writes to an S3 bucket.

Overview of the update function

To update existing Security Hub findings that you previously exported, you can use the update function CsvUpdater to modify the respective rows and columns of the CSV file you exported, as shown in Figure 2. There are 12 modifiable columns out of 37 (any changes to other columns are ignored), which are described in more detail in Step 3: View or update findings in the CSV file later in this post.

Figure 2: Architecture diagram of the update function

Figure 2 shows the following numbered steps:

Figure 2 shows the following numbered steps:

  1. You download the CSV file that the CsvExporter function generated from the S3 bucket and update as needed.
  2. You upload the CSV file that contains your updates to the S3 bucket.
  3. In the AWS Management Console, you invoke the CsvUpdater Lambda function with a test event containing the URI of the CSV file.
  4. CsvUpdater reads the updated CSV file from the S3 bucket.
  5. CsvUpdater identifies the minimum set of updates and invokes the Security Hub BatchUpdateFindings API action.

Step 1: Use the CloudFormation template to deploy the solution

You can set up and use CSV Manager for Security Hub by using either AWS CloudFormation or the AWS Cloud Development Kit (AWS CDK).

To deploy the solution (AWS CDK)

You can find the latest code in the aws-security-hub-csv-manager GitHub repository, where you can also contribute to the sample code. The following commands show how to deploy the solution by using the AWS CDK. First, the AWS CDK initializes your environment and uploads the AWS Lambda assets to an S3 bucket. Then, you deploy the solution to your account by using the following commands. Replace <INSERT_AWS_ACCOUNT> with your account number, and replace <INSERT_REGION> with the AWS Region that you want the solution deployed to, for example us-east-1.

cdk bootstrap aws://<INSERT_AWS_ACCOUNT>/<INSERT_REGION>
cdk deploy

To deploy the solution (CloudFormation)

  1. Choose the following Launch Stack button to open the AWS CloudFormation console pre-loaded with the template for this solution:

    Launch Stack

  2. In the Parameters section, as shown in Figure 3, enter your values.
    Figure 3: CloudFormation template variables

    Figure 3: CloudFormation template variables

    1. For What folder for CSV Manager for Security Hub Lambda code, leave the default Code. For What folder for CSV Manager for Security Hub exports, leave the default Findings.

      These are the folders within the S3 bucket that the CSV Manager for Security Hub CloudFormation template creates to store the Lambda code, as well as where the findings are exported by the Lambda function.

    2. For Frequency, for this solution you can leave the default value cron(0 8 ? * SUN *). This default causes automatic exports to occur every Sunday at 8:00 AM local time using an EventBridge scheduled rule. For more information about how to update this value to meet your needs, see Schedule Expressions for Rules in the Amazon CloudWatch Events User Guide.
    3. The values you enter for the Regions field depend on whether you have configured an aggregation Region in Security Hub.
      • If you have configured an aggregation Region, enter only that Region code, for example eu-north-1, as shown in Figure 3.
      • If you haven’t configured an aggregation Region, enter a comma-separated list of Regions in which you have enabled Security Hub, for example us-east-1, eu-west-1, eu-west-2.
      • If you would like to export findings from all Regions where Security Hub is enabled, leave the Regions field blank. Regions where Security Hub is not enabled will generate a message and will be skipped.
  3. Choose Next.

The CloudFormation stack deploys the necessary resources, including an EventBridge scheduling rule, AWS System Managers Automation documents, an S3 bucket, and Lambda functions for exporting and updating Security Hub findings.

After you deploy the CloudFormation stack

After you create the CSV Manager for Security Hub stack, you can do the following:

  1. Perform the export function to write some or all Security Hub findings to a CSV file by following the instructions in Step 2: Export Security Hub findings to a CSV file later in this post.
  2. Perform a bulk update of Security Hub findings by following the instructions in Step 3: View or update findings in the CSV file later in this post. You can make changes to one or more of the 12 updatable columns of the CSV file, and perform the update function to update some or all Security Hub findings.

Step 2: Export Security Hub findings to a CSV file

You can export Security Hub findings from the AWS Lambda console. To do this, you create a test event and invoke the CsvExporter Lambda function. CsvExporter exports all Security Hub findings from all applicable Regions to a single CSV file in the S3 bucket for CSV Manager for Security Hub.

To export Security Hub findings to a CSV file

  1. In the AWS Lambda console, find the CsvExporter Lambda function and select it.
  2. On the Code tab, choose the down arrow at the right of the Test button, as shown in Figure 4, and select Configure test event.
    Figure 4: The down arrow at the right of the Test button

    Figure 4: The down arrow at the right of the Test button

  3. To create an empty test event, on the Configure test event page, do the following:
    1. Choose Create a new event.
    2. Enter an event name; in this example we used testEvent.
    3. For Template, leave the default hello-world.
    4. For Event JSON, enter the JSON object {} as shown in Figure 5.
    Figure 5: Creating an empty test event

    Figure 5: Creating an empty test event

  4. Choose Save to save the empty test event.
  5. To invoke the Lambda function, choose the Test button, as shown in Figure 6.
    Figure 6: Test button to invoke the Lambda function

    Figure 6: Test button to invoke the Lambda function

  6. On the Execution Results tab, note the following details, which you will need for the next step.
    {
    "message": "Export succeeded", 
    "bucket": DOC-EXAMPLE-BUCKET,
    "exportKey”: DOC-EXAMPLE-OBJECT,
    "resultCode": 200
    }

  7. Locate the CSV object that matches the value of “exportKey” (in this example, DOC-EXAMPLE-OBJECT) in the S3 bucket that matches the value of “bucket” (in this example, DOC-EXAMPLE-BUCKET).

Now you can view or update the findings in the CSV file, as described in the next section.

Step 3: (Optional) Using filters to limit CSV results

In your test event, you can specify any filter that is accepted by the GetFindings API action. You do this by adding a filter key to your test event. The filter key can either contain the word HighActive (which is a predefined filter configured as a default for selecting active high-severity and critical findings, as shown in Figure 8), or a JSON filter object.

Figure 8 depicts an example JSON filter that performs the same filtering as the HighActive predefined filter.

To use filters to limit CSV results

  1. In the AWS Lambda console, find the CsvExporter Lambda function and select it.
  2. On the Code tab, choose the down arrow at the right of the Test button, as shown in Figure 7, and select Configure test event.
    Figure 7: The down arrow at the right of the Test button

    Figure 7: The down arrow at the right of the Test button

  3. To create a test event containing a filter, on the Configure test event page, do the following:
    1. Choose Create a new event.
    2. Enter an event name; in this example we used filterEvent.
    3. For Template, select testEvent,
    4. For Event JSON, enter the following JSON object, as shown in Figure 8.
      {
         "SeverityLabel":[
            {
               "Value":"CRITICAL",
               "Comparison":"EQUALS"
            },
            {
               "Value":"HIGH",
               "Comparison":"EQUALS"
            }
         ],
         "RecordState":[
            {
               "Comparison":"EQUALS",
               "Value":"ACTIVE"
            }
         ]
      }

      Figure 8: Test button to invoke the Lambda function

      Figure 8: Test button to invoke the Lambda function

    5. Choose Save.
  4. To invoke the Lambda function, choose the Test button as shown in Figure 9.
    Figure 9: Test button to invoke the Lambda function

    Figure 9: Test button to invoke the Lambda function

  5. On the Execution Results tab, note the following details, which you will need for the next step.
    {
    "message": "Export succeeded", 
    "bucket": DOC-EXAMPLE-BUCKET,
    "exportKey": DOC-EXAMPLE-OBJECT,
    "resultCode": 200
    }

  6. Locate the CSV object that matches the value of “exportKey” (in this example, DOC-EXAMPLE-OBJECT) in the S3 bucket that matches the value of “bucket” (in this example, DOC-EXAMPLE-BUCKET).

The results in this CSV file should be a filtered set of Security Hub findings according to the filter you specified above. You can now proceed to step 4 if you want to view or update findings.

Step 4: View or update findings in the CSV file

You can use any program that allows you to view or edit CSV files, such as Microsoft Excel. The first row in the CSV file are the column names. These column names correspond to fields in the JSON objects that are returned by the GetFindings API action.

Warning: Do not modify the first two columns, Id (column A) or ProductArn (column B). If you modify these columns, Security Hub will not be able to locate the finding to update, and any other changes to that finding will be discarded.

You can locally modify any of the columns in the CSV file, but only 12 columns out of 37 columns will actually be updated if you use CsvUpdater to update Security Hub findings. The following are the 12 columns you can update. These correspond to columns C through N in the CSV file.

Column name Spreadsheet column Description
Criticality C An integer value between 0 and 100.
Confidence D An integer value between 0 and 100.
NoteText E Any text you wish
NoteUpdatedBy F Automatically updated with your AWS principal user ID.
CustomerOwner* G Information identifying the owner of this finding (for example, email address).
CustomerIssue* H A Jira issue or another identifier tracking a specific issue.
CustomerTicket* I A ticket number or other trouble/problem tracking identification.
ProductSeverity** J A floating-point number from 0.0 to 99.9.
NormalizedSeverity** K An integer between 0 and 100.
SeverityLabel L One of the following:

  • INFORMATIONAL
  • LOW
  • MEDIUM
  • HIGH
  • HIGH
  • CRITICAL
VerificationState M One of the following:

  • UNKNOWN — Finding has not been verified yet.
  • TRUE_POSITIVE — This is a valid finding and should be treated as a risk.
  • FALSE_POSITIVE — This an incorrect finding and should be ignored or suppressed.
  • BENIGN_POSITIVE — This is a valid finding, but the risk is not applicable or has been accepted, transferred, or mitigated.
Workflow N One of the following:

  • NEW — This is a new finding that has not been reviewed.
  • NOTIFIED — The responsible party or parties have been notified of this finding.
  • RESOLVED — The finding has been resolved.
  • SUPPRESSED — A false or benign finding has been suppressed so that it does not appear as a current finding in Security Hub.

* These columns are stored inside the UserDefinedFields field of the updated findings. The column names imply a certain kind of information, but you can put any information you wish.

** These columns are stored inside the Severity field of the updated findings. These values have a fixed format and will be rejected if they do not meet that format.

Columns with fixed text values (L, M, N) in the previous table can be specified in mixed case and without underscores—they will be converted to all uppercase and underscores added in the CsvUpdater Lambda function. For example, “false positive” will be converted to “FALSE_POSITIVE”.

Step 5: Create a test event and update Security Hub by using the CSV file

If you want to update Security Hub findings, make your changes to columns C through N as described in the previous table. After you make your changes in the CSV file, you can update the findings in Security Hub by using the CSV file and the CsvUpdater Lambda function.

Use the following procedure to create a test event and run the CsvUpdater Lambda function.

To create a test event and run the CsvUpdater Lambda function

  1. In the AWS Lambda console, find the CsvUpdater Lambda function and select it.
  2. On the Code tab, choose the down arrow to the right of the Test button, as shown in Figure 10, and select Configure test event.
    Figure 10: The down arrow to the right of the Test button

    Figure 10: The down arrow to the right of the Test button

  3. To create a test event as shown in Figure 11, on the Configure test event page, do the following:
    1. Choose Create a new event.
    2. Enter an event name; in this example we used testEvent.
    3. For Template, leave the default hello-world.
    4. For Event JSON, enter the following:
      {
      "input": <s3ObjectUri>,
      "primaryRegion": <aggregationRegionName>
      }

      Replace <s3ObjectUri> with the full URI of the S3 object where the updated CSV file is located.

      Replace <aggregationRegionName> with your Security Hub aggregation Region, or the primary Region in which you initially enabled Security Hub.

      Figure 11: Create and save a test event for the CsvUpdater Lambda function

      Figure 11: Create and save a test event for the CsvUpdater Lambda function

  4. Choose Save.
  5. Choose the Test button, as shown in Figure 12, to invoke the Lambda function.
    Figure 12: Test button to invoke the Lambda function

    Figure 12: Test button to invoke the Lambda function

  6. To verify that the Lambda function ran successfully, on the Execution Results tab, review the results for “message”: “Success”, as shown in the following example. Note that the results may be thousands of lines long.
    {
    "message": "Success",
    "details": {
    "processed": [{"Id": arn:aws:securityhub:us-east-1: 111122223333:subscription/cis-aws-foundations-benchmark/v/1.2.0/1.7/finding/6d543b22-6a3d-405c-ae7f-224469bde7d2, "ProductArn": arn:aws:securityhub:us-east-1::product/aws/securityhub}, … ],
    "unprocessed": [],
    "message": "Updated succeeded",
    "success": true
    },
    "input": s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-OBJECT,
    "resultCode": 200
    }

    The processed array lists every successfully updated finding by Id and ProductArn.

    If any of the findings were not successfully updated, their Id and ProductArn appear in the unprocessed array. In the previous example, no findings were unprocessed.

    The value s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-OBJECT is the URI of the S3 object from which your updates were read.

Cleaning up

To avoid incurring future charges, first delete the CloudFormation stack that you deployed in Step 1: Use the CloudFormation template to deploy the solution. Next, you need to manually delete the S3 bucket deployed with the stack. For instructions, see Deleting a bucket in the Amazon Simple Storage Service User Guide.

Conclusion

In this post, we showed you how you can export Security Hub findings to a CSV file in an S3 bucket and update the exported findings by using CSV Manager for Security Hub. We showed you how you can automate this process by using AWS Lambda, Amazon S3, and AWS Systems Manager. Full documentation for CSV Manager for Security Hub is available in the aws-security-hub-csv-manager GitHub repository. You can also investigate other ways to manage Security Hub findings by checking out our blog posts about Security Hub integration with Amazon OpenSearch Service, Amazon QuickSight, Slack, PagerDuty, Jira, or ServiceNow.

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 the Security Hub re:Post. To learn more or get started, visit AWS Security Hub.

Want more AWS Security news? Follow us on Twitter.

Andy Robinson

Andy Robinson

Andy wrote CSV Manager for Security Hub in response to requests from several customers. He is an AWS Professional Services Senior Security Consultant with over 30 years of security, software product management, and software design experience. Andy is also a pilot, scuba instructor, martial arts instructor, ham radio enthusiast, and photographer.

Murat Eksi

Murat Eksi

Murat is a full-stack technologist at AWS Professional Services. He has worked with various industries, including finance, sports, media, gaming, manufacturing, and automotive, to accelerate their business outcomes through application development, security, IoT, analytics, devops and infrastructure. Outside of work, he loves traveling around the world, learning new languages while setting up local events for entrepreneurs and business owners in Stockholm, or taking flight lessons.

Shikhar Mishra

Shikhar Mishra

Shikhar is a Senior Solutions Architect at Amazon Web Services. He is a cloud security enthusiast and enjoys helping customers design secure, reliable, and cost-effective solutions on AWS.

Rohan Raizada

Rohan Raizada

Rohan is a Solutions Architect for Amazon Web Services. He works with enterprises of all sizes with their cloud adoption to build scalable and secure solutions using AWS. During his free time, he likes to spend time with family and go cycling outdoors.

Jonathan Nguyen

Jonathan Nguyen

Jonathan is a Shared Delivery Team Senior Security Consultant at AWS. His background is in AWS Security with a focus on threat detection and incident response. Today, he helps enterprise customers develop a comprehensive security strategy and deploy security solutions at scale, and he trains customers on AWS Security best practices.

How to detect suspicious activity in your AWS account by using private decoy resources

Post Syndicated from Maitreya Ranganath original https://aws.amazon.com/blogs/security/how-to-detect-suspicious-activity-in-your-aws-account-by-using-private-decoy-resources/

As customers mature their security posture on Amazon Web Services (AWS), they are adopting multiple ways to detect suspicious behavior and notify response teams or workflows to take action. One example is using Amazon GuardDuty to monitor AWS accounts and workloads for malicious activity and deliver detailed security findings for visibility and remediation. Another tactic is to deploy decoys, also called honeypots, as an effective way to detect suspicious behavior.

In this blog post, we’ll show how you can create low-cost private decoy AWS resources in your AWS accounts and configure them to generate alerts when they are accessed. These decoy resources appear legitimate but don’t contain any useful or sensitive data and typically are not accessed in the normal course of business by your users and systems. Any attempt to access them is a clear signal of suspicious activity that should be investigated. You can use data sources like AWS CloudTrail, services like Amazon Detective, and your own security incident and event monitoring (SIEM) systems to investigate the activity further. This post is aimed at experienced AWS users and security professionals.

Detecting suspicious activity

Imagine that an unauthorized user has obtained credentials for your account. This could also be an insider, malicious or careless, using their valid credentials inappropriately. The unauthorized user might use these credentials to invoke AWS API calls to list resources in your account. As the next step, they might try to access resources that are commonly used to store sensitive data—such as objects in Amazon Simple Storage Service (Amazon S3) buckets, secrets in AWS Secrets Manager, or items in Amazon DynamoDB. They might also try to elevate their privileges by assuming other Identity and Access Management (IAM) roles in your account. In your role as a security professional, your task is to detect this suspicious behaviour and take actions in response. One approach is to learn the baseline of activities of the IAM users and roles in your account and flag any deviations from the learned baseline—this is the approach taken by GuardDuty when it generates findings such as Discovery:IAMUser/AnomalousBehavior.

This post focuses on another approach of creating private decoy resources in your account that are intended to look legitimate, but don’t have any useful or sensitive data and are not exposed publicly. These decoys are designed to alert you about suspicious activities that could indicate AWS credentials exposure or account compromise. You can use the decoys in conjunction with other techniques, such as creating deception environments and public and private honeypots to better detect suspicious activity in your accounts and applications.

The Fidelity-Isolation-Cost trilemma

In an ACM Queue article titled Lamboozling Attackers: A New Generation of Deception, Kelly Shortridge and Ryan Petrich introduced the Fidelity-Isolation-Cost (FIC) trilemma that “captures the most important dimensions of designing deception systems: fidelity, isolation, and cost.” Using their definition of the FIC trilemma, we see that decoy AWS resources can be well suited to designing deception systems:

  • Fidelity – Because the decoys are actual AWS resources, they behave like other legitimate resources and have high fidelity. For example, a decoy S3 bucket behaves exactly like any other S3 bucket, with the only exception being that the object data it contains is dummy and not useful. However, the unauthorized user only discovers this fact after downloading the object data and generating an automated alert to your security team.
  • Isolation – You can simply isolate the decoy AWS resources from other resources in the same account. For example, an S3 bucket is inherently isolated from other S3 buckets in the same account. An unauthorized user that can read the decoy S3 bucket does not, by doing so, get the ability to access or impact the availability of other resources in the account. The credentials obtained by the unauthorized user might have permissions to actions on other services, but the presence of the decoy S3 bucket doesn’t add to those permissions in any way.
  • Cost – You can keep the cost of deception low by choosing AWS resources that have no cost or low cost to deploy, are deployed by means of automation, and require no further operation or maintenance effort. For example, an S3 bucket with several files that are a few MB in size costs a fraction of a US cent per month for storage. The API request cost should be zero, because the bucket is designed never to be accessed in the normal course of business. Choosing similar zero or low-cost resources can make it cost-effective and feasible to create such decoy resources in multiple accounts, including in Production accounts, where it’s especially important to detect suspicious activity.

Examples of private decoy AWS resources

The following table shows examples of private decoy AWS resources that are high-fidelity, high-isolation, low-cost and are suitable to be deployed in an account that has sensitive data or applications. The table also lists the CloudTrail event fields that provide the source and name for accesses to each resource. You can use these CloudTrail events to create corresponding Amazon EventBridge rules that will generate alerts and notifications.

Private decoy resource CloudTrail event source CloudTrail event names Considerations
S3 bucket and S3 objects with dummy data s3.amazonaws.com GetObject
HeadObject
Ensure that the S3 objects do not contain any sensitive data.

S3 data events must be enabled in CloudTrail for the decoy S3 bucket

IAM role that should never be assumed sts.amazonaws.com AssumeRole Ensure that the IAM policies attached to this role allow access only to decoy resources and no other data or resources.

Ensure that the IAM role’s trust policy only trusts principals in the same account to assume the role.

Secrets Manager secret (See Note at end of table) kms.amazonaws.com Decrypt Ensure that the secret value does not contain any sensitive data.
AWS Systems Manager Parameter Store parameter (See Note at end of table) kms.amazonaws.com Decrypt Ensure that the parameter value does not contain any sensitive data.
DynamoDB table that contains items with dummy data dynamodb.amazonaws.com BatchExecuteStatement
BatchGetItem
BatchWriteItem
DeleteItem
ExecuteStatement
ExecuteTransaction
GetItem
PutItem
Query
Scan
TransactGetItems
TransactWriteItems
UpdateItem
Ensure that the item does not have any sensitive data.

DynamoDB data events must be enabled in CloudTrail for the decoy DynamoDB table.

Note: When CloudTrail Management API events are sent to EventBridge, read-only events such as Get*, List*, and Describe* are filtered out and not processed. In order to get findings for secrets and Systems Manager parameters that are being accessed, you need to alert on GetSecretValue and GetParameter API calls. Since these are not processed by EventBridge, you can instead use the fact that secrets and secure string parameters are encrypted by using AWS Key Management Service (AWS KMS), and match on the corresponding AWS KMS Decrypt API calls. This means that successful calls from an unauthorized user to GetSecretValue and GetParameter are able to be matched and alerted on.

Notifications from matching EventBridge rules can be sent to an AWS Lambda function that generates custom findings in Security Hub. These findings can then be sent to downstream systems that you might have configured in your environment, such as your SIEM system or an automated response workflow in your Security Orchestration, Automation, and Response system. Figure 1 shows this workflow.

Figure 1: Accesses to decoy resources automatically create custom Security Hub findings

Figure 1: Accesses to decoy resources automatically create custom Security Hub findings

Deploy the private decoy resources

We’ve provided an AWS CloudFormation template that you can use to deploy the solution. The template creates the following private decoy AWS resources in your account:

  • DynamoDB table
  • IAM role
  • S3 bucket with a decoy S3 object
  • Systems Manager SecureString parameter
  • Secrets Manager secret

In addition, the CloudFormation template deploys the following resources in your account to detect accesses to the decoys and send custom findings to Security Hub:

  • A CloudTrail data events trail that includes only data events from the decoy S3 bucket and DynamoDB table
  • Six EventBridge rules to match specific CloudTrail API events
  • Two Lambda functions with corresponding IAM roles:
    • The WriteData Lambda function is a CloudFormation custom resource that is used to create the decoy S3 object and the Systems Manager SecureString parameter
    • The Data Lambda function is a target for the EventBridge rules, and it sends custom findings to Security Hub when the decoy resources are accessed

Prerequisites

The prerequisites to deploying the solution are as follows:

  • Security Hub must be enabled in the AWS Regions where the private decoys will be deployed, in order to receive custom findings.
  • You must have created a CloudTrail trail to log management events for the AWS account in the Region where you deploy the private decoys. This trail can be created locally in the account or can be an organization trail. Ensure that you have enabled both read and write events, and enabled all AWS KMS events in the trail (this is the default configuration).

Deploy the solution

After you have the prerequisites set up, you can launch the CloudFormation template to deploy the private decoys.

To launch the template

  1. Choose the following Launch Stack button to launch a CloudFormation stack in your account.

    Launch Stack

    Note: The stack will launch in the N. Virginia (us-east-1) Region. To deploy this solution into other AWS Regions, download the solution’s CloudFormation template, modify it, and deploy it to the selected Region. In order to get maximum coverage for detecting suspicious activity, we recommend that you deploy the solution into your key production accounts and Regions.

  2. On the Specify stack details page, enter the stack name, then choose Next.

    The CloudFormation template will use the stack name as part of the naming of the resources that are created. We recommend that you use your organization’s existing naming conventions for stack names, and not make reference to decoy resources, because this could alert any unauthorized user to the real purpose of the resources they’re attempting to access.

    Figure 2: Specify stack details

    Figure 2: Specify stack details

  3. Configure any tags or other organization-specific stack options you need, or accept the default settings, and then choose Next.
  4. Review the CloudFormation settings and select the box acknowledging that AWS CloudFormation might create IAM resources with custom names, and then choose Create stack.
  5. After the stack has completed deployment, the CloudFormation stack output will show the Amazon Resource Names (ARNs) of the decoy resources that were created.
    Figure 3: CloudFormation stack outputs

    Figure 3: CloudFormation stack outputs

Estimated costs

This solution has been designed to keep costs as low as possible, by using services that have no associated costs (such as IAM roles or any parameters stored in Systems Manager Parameter Store), and keeping the use of paid for services (such as S3 and DynamoDB) to a minimum.

Deploying the solution as outlined in this blog post should result in a cost of less than $1 per month for a single account deployment, however please refer to the AWS Pricing Calculator where you can create a pricing estimate based on your deployment using the most up-to-date pricing information.

Test the alerts

In normal circumstances, after you configure the decoys, there will be no attempted access to these resources, and no findings will be sent to Security Hub in your account. To test that the configuration is working as expected, you can issue the following commands from a device that has programmatic access to your account where the private decoy resources have been deployed. To run each command, replace the bracketed, italicized text with your own information. You can find the details for each of the resources in the outputs section of the CloudFormation stack after it has been deployed successfully.

S3 object access

  • aws s3 cp s3://<bucket_name/object_name> /tmp
  • aws s3 cp s3://<bucket_name/object_name> s3://<any_existing_bucket>

IAM role assumption

  • aws sts assume-role –role-arn <role_name> –role-session-name BlogTestRole

Secrets Manager access

  • aws secretsmanager get-secret-value –secret-id <secret_name>

Parameter Store access

  • aws ssm get-parameters –names <ssm_parameter> –with-decryption

    DynamoDB table scan

  • aws dynamodb scan –table-name <table_name>

An example of what these test-generated findings looks like is shown in Figure 4.

Figure 4: Security Hub findings

Figure 4: Security Hub findings

Considerations

Consider the following as you deploy decoy AWS resources:

  • You should consider decoy AWS resources as enhancements to your foundational security controls. Your foundational controls should include these measures:
    • Help prevent the compromise of AWS credentials and limit the privileges of credentials by implementing strong identity management and permissions management.
    • Identify and investigate alerts generated by decoy resources by implementing detective controls.
    • Implement incident response mechanisms to respond to and mitigate the potential impact of security incidents, such as a decoy AWS resource being accessed.
  • You should ensure that your monitoring services and tools are configured to query the configuration of resources and not the data stored in resources. Otherwise, you might get a large volume of false positives because every time a resource is accessed, a custom finding is created in Security Hub. For example, consider a service like Security Hub Security Standards checks, or a cloud security posture management (CSPM) tool that monitors your S3 buckets by describing the properties of all buckets in your account. Such tools will find the decoy S3 bucket and will interrogate its configuration by making calls such as GetBucketPolicy and GetBucketLogging. However, as long as these tools don’t try to read data in the bucket through calls such as GetObject, the EventBridge rules that are configured as described in this post won’t generate a finding.
  • As a specific example of the previous point, ensure that you don’t run a sensitive data discovery job in Amazon Macie on the decoy S3 bucket, to avoid false alerts. You can configure Amazon Macie to monitor the metadata of your S3 buckets, because those actions won’t generate alerts.
  • The solution generates custom findings in Security Hub only for successful accesses of Secrets Manager secrets and Systems Manager parameters. However, both successful and unsuccessful accesses of S3 objects and DynamoDB items, and IAM role assumption, will generate custom findings in Security Hub.

Conclusion

In this post, we discussed the advantages of using private decoy AWS resources to detect suspicious activities within your account and how these decoys can complement your existing security solutions. You learned how to create private decoys, set up alerting, and ingest (and test) these alerts as custom findings into Security Hub for central visibility across your AWS environment. The solution deployment included a set of common resources as private decoys; however, the necessary code and templates can be found in our GitHub repository, and you can extend and customize these to add other resources that you want to include in your accounts.

If you would also like to learn about using CloudTrail as another method of detecting unexpected behavior within your accounts, see the blog post Using CloudTrail to identify unexpected behaviors in individual workloads for more information.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

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Author

Maitreya Ranganath

Maitreya is an AWS Security Solutions Architect. He enjoys helping customers solve security and compliance challenges and architect scalable and cost-effective solutions on AWS.

Mark Keating

Mark Keating

Mark is an AWS Security Solutions Architect based out of the U.K. who works with Global Healthcare & Life Sciences and Automotive customers to solve their security and compliance challenges and help them reduce risk. He has over 20 years of experience working with technology, within in operations, solution, and enterprise architecture roles.

New row and column interactivity options for tables and pivot tables in Amazon QuickSight – Part 1

Post Syndicated from Bhupinder Chadha original https://aws.amazon.com/blogs/big-data/part-1-new-row-and-column-interactivity-options-for-tables-and-pivot-tables-in-amazon-quicksight/

Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to create and deliver insights to everyone in your organization. You can make your data come to life with rich interactive charts and create beautiful dashboards to share with thousands of users, either directly within a QuickSight application, or embedded in web apps and portals.

In 2021, as part of table and pivot table customization, we launched the ability for authors to resize row height in tables and pivot tables. Now, we are extending this capability to readers, along with other row and column interactions, such as altering column width and header height for both tables and pivot tables using a drag handler, for consistency between row and column interactions and an improved user experience. Apart from the format pane settings, authors can make quick changes to column, row, and header width for both parent and child nodes by simply dragging the cell, column, or row to the desired setting using the drag handler.

The following are the different interactions that both readers and authors can perform, some of which were already available for tables.

Modify row height

You can modify row height using the drag handler for cells, row headers, or column headers.

The following screenshot illustrates how to alter the row height by dragging from any cell in the table or pivot table.

For row headers, you can only resize row height by dragging the last element (child node), which gets aggregated to define the row height for the parent node.

You can resize the height of the column headers at any level such that you can have different heights at each level for better aesthetics.

Modify column width

You can also use the drag handler to modify column width for row headers, column headers, or cells.

You can alter column width for any field assigned to rows, as shown in the following screenshot.

You can alter column width for column dimension members both from the parent or leaf node in the case of hierarchy, or column field or values headers in the absence of a column hierarchy.

You now have complete flexibility to resize column width from cells. Depending on which cell, the corresponding column width is adjusted.

Considerations

A few things to note about this new feature:

  • Drag handlers are available for both web and embedded use cases
  • Row height is set in common for all rows and not specific to a particular row

Summary

With the introduction of a drag handler, authors and readers can now quickly alter column width, row height, and header height for tables and pivot tables. This provides a consistent behavior and improved user interaction for both the personas and visuals. To learn more about table and pivot table formatting options, refer to Formatting tables and pivot tables in Amazon QuickSight.

Try out the new feature and share your feedback and questions in the comments section.


About the author

Bhupinder Chadha is a senior product manager for Amazon QuickSight focused on visualization and front end experiences. He is passionate about BI, data visualization and low-code/no-code experiences. Prior to QuickSight he was the lead product manager for Inforiver, responsible for building a enterprise BI product from ground up. Bhupinder started his career in presales, followed by a small gig in consulting and then PM for xViz, an add on visualization product.