Tag Archives: AWS CloudTrail

Now Use AWS IAM to Delete a Service-Linked Role When You No Longer Require an AWS Service to Perform Actions on Your Behalf

Post Syndicated from Ujjwal Pugalia original https://aws.amazon.com/blogs/security/now-use-aws-iam-to-delete-a-service-linked-role-when-you-no-longer-require-an-aws-service-to-perform-actions-on-your-behalf/

Earlier this year, AWS Identity and Access Management (IAM) introduced service-linked roles, which provide you an easy and secure way to delegate permissions to AWS services. Each service-linked role delegates permissions to an AWS service, which is called its linked service. Service-linked roles help with monitoring and auditing requirements by providing a transparent way to understand all actions performed on your behalf because AWS CloudTrail logs all actions performed by the linked service using service-linked roles. For information about which services support service-linked roles, see AWS Services That Work with IAM. Over time, more AWS services will support service-linked roles.

Today, IAM added support for the deletion of service-linked roles through the IAM console and the IAM API/CLI. This means you now can revoke permissions from the linked service to create and manage AWS resources in your account. When you delete a service-linked role, the linked service no longer has the permissions to perform actions on your behalf. To ensure your AWS services continue to function as expected when you delete a service-linked role, IAM validates that you no longer have resources that require the service-linked role to function properly. This prevents you from inadvertently revoking permissions required by an AWS service to manage your existing AWS resources and helps you maintain your resources in a consistent state. If there are any resources in your account that require the service-linked role, you will receive an error when you attempt to delete the service-linked role, and the service-linked role will remain in your account. If you do not have any resources that require the service-linked role, you can delete the service-linked role and IAM will remove the service-linked role from your account.

In this blog post, I show how to delete a service-linked role by using the IAM console. To learn more about how to delete service-linked roles by using the IAM API/CLI, see the DeleteServiceLinkedRole API documentation.

Note: The IAM console does not currently support service-linked role deletion for Amazon Lex, but you can delete your service-linked role by using the Amazon Lex console. To learn more, see Service Permissions.

How to delete a service-linked role by using the IAM console

If you no longer need to use an AWS service that uses a service-linked role, you can remove permissions from that service by deleting the service-linked role through the IAM console. To delete a service-linked role, you must have permissions for the iam:DeleteServiceLinkedRole action. For example, the following IAM policy grants the permission to delete service-linked roles used by Amazon Redshift. To learn more about working with IAM policies, see Working with Policies.

{ 
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "AllowDeletionOfServiceLinkedRolesForRedshift",
            "Effect": "Allow",
            "Action": ["iam:DeleteServiceLinkedRole"],
            "Resource": ["arn:aws:iam::*:role/aws-service-role/redshift.amazonaws.com/AWSServiceRoleForRedshift*"]
	 }
    ]
}

To delete a service-linked role by using the IAM console:

  1. Navigate to the IAM console and choose Roles from the navigation pane.

Screenshot of the Roles page in the IAM console

  1. Choose the service-linked role you want to delete and then choose Delete role. In this example, I choose the  AWSServiceRoleForRedshift service-linked role.

Screenshot of the AWSServiceRoleForRedshift service-linked role

  1. A dialog box asks you to confirm that you want to delete the service-linked role you have chosen. In the Last activity column, you can see when the AWS service last used the service-linked role, which tells you when the linked service last used the service-linked role to perform an action on your behalf. If you want to continue to delete the service-linked role, choose Yes, delete to delete the service-linked role.

Screenshot of the "Delete role" window

  1. IAM then checks whether you have any resources that require the service-linked role you are trying to delete. While IAM checks, you will see the status message, Deletion in progress, below the role name. Screenshot showing "Deletion in progress"
  1. If no resources require the service-linked role, IAM deletes the role from your account and displays a success message on the console.

Screenshot of the success message

  1. If there are AWS resources that require the service-linked role you are trying to delete, you will see the status message, Deletion failed, below the role name.

Screenshot showing the "Deletion failed"

  1. If you choose View details, you will see a message that explains the deletion failed because there are resources that use the service-linked role.
    Screenshot showing details about why the role deletion failed
  2. Choose View Resources to view the Amazon Resource Names (ARNs) of the first five resources that require the service-linked role. You can delete the service-linked role only after you delete all resources that require the service-linked role. In this example, only one resource requires the service-linked role.

Conclusion

Service-linked roles make it easier for you to delegate permissions to AWS services to create and manage AWS resources on your behalf and to understand all actions the service will perform on your behalf. If you no longer need to use an AWS service that uses a service-linked role, you can remove permissions from that service by deleting the service-linked role through the IAM console. However, before you delete a service-linked role, you must delete all the resources associated with that role to ensure that your resources remain in a consistent state.

If you have any questions, submit a comment in the “Comments” section below. If you need help working with service-linked roles, start a new thread on the IAM forum or contact AWS Support.

– Ujjwal

Greater Transparency into Actions AWS Services Perform on Your Behalf by Using AWS CloudTrail

Post Syndicated from Ujjwal Pugalia original https://aws.amazon.com/blogs/security/get-greater-transparency-into-actions-aws-services-perform-on-your-behalf-by-using-aws-cloudtrail/

To make managing your AWS account easier, some AWS services perform actions on your behalf, including the creation and management of AWS resources. For example, AWS Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring. To make these AWS actions more transparent, AWS adds an AWS Identity and Access Management (IAM) service-linked roles to your account for each linked service you use. Service-linked roles let you view all actions an AWS service performs on your behalf by using AWS CloudTrail logs. This helps you monitor and audit the actions AWS services perform on your behalf. No additional actions are required from you and you can continue using AWS services the way you do today.

To learn more about which AWS services use service-linked roles and log actions on your behalf to CloudTrail, see AWS Services That Work with IAM. Over time, more AWS services will support service-linked roles. For more information about service-linked roles, see Role Terms and Concepts.

In this blog post, I demonstrate how to view CloudTrail logs so that you can more easily monitor and audit AWS services performing actions on your behalf. First, I show how AWS creates a service-linked role in your account automatically when you configure an AWS service that supports service-linked roles. Next, I show how you can view the policies of a service-linked role that grants an AWS service permission to perform actions on your behalf. Finally, I  use the configured AWS service to perform an action and show you how the action appears in your CloudTrail logs.

How AWS creates a service-linked role in your account automatically

I will use Amazon Lex as the AWS service that performs actions on your behalf for this post. You can use Amazon Lex to create chatbots that allow for highly engaging conversational experiences through voice and text. You also can use chatbots on mobile devices, web browsers, and popular chat platform channels such as Slack. Amazon Lex uses Amazon Polly on your behalf to synthesize speech that sounds like a human voice.

Amazon Lex uses two IAM service-linked roles:

  • AWSServiceRoleForLexBots — Amazon Lex uses this service-linked role to invoke Amazon Polly to synthesize speech responses for your chatbot.
  • AWSServiceRoleForLexChannels — Amazon Lex uses this service-linked role to post text to your chatbot when managing channels such as Slack.

You don’t need to create either of these roles manually. When you create your first chatbot using the Amazon Lex console, Amazon Lex creates the AWSServiceRoleForLexBots role for you. When you first associate a chatbot with a messaging channel, Amazon Lex creates the AWSServiceRoleForLexChannels role in your account.

1. Start configuring the AWS service that supports service-linked roles

Navigate to the Amazon Lex console, and choose Get Started to navigate to the Create your Lex bot page. For this example, I choose a sample chatbot called OrderFlowers. To learn how to create a custom chatbot, see Create a Custom Amazon Lex Bot.

Screenshot of making the choice to create an OrderFlowers chatbot

2. Complete the configuration for the AWS service

When you scroll down, you will see the settings for the OrderFlowers chatbot. Notice the field for the IAM role with the value, AWSServiceRoleForLexBots. This service-linked role is “Automatically created on your behalf.” After you have entered all details, choose Create to build your sample chatbot.

Screenshot of the automatically created service-linked role

AWS has created the AWSServiceRoleForLexBots service-linked role in your account. I will return to using the chatbot later in this post when I discuss how Amazon Lex performs actions on your behalf and how CloudTrail logs these actions. First, I will show how you can view the permissions for the AWSServiceRoleForLexBots service-linked role by using the IAM console.

How to view actions in the IAM console that AWS services perform on your behalf

When you configure an AWS service that supports service-linked roles, AWS creates a service-linked role in your account automatically. You can view the service-linked role by using the IAM console.

1. View the AWSServiceRoleForLexBots service-linked role on the IAM console

Go to the IAM console, and choose AWSServiceRoleForLexBots on the Roles page. You can confirm that this role is a service-linked role by viewing the Trusted entities column.

Screenshot of the service-linked role

2.View the trusted entities that can assume the AWSServiceRoleForLexBots service-linked role

Choose the Trust relationships tab on the AWSServiceRoleForLexBots role page. You can view the trusted entities that can assume the AWSServiceRoleForLexBots service-linked role to perform actions on your behalf. In this example, the trusted entity is lex.amazonaws.com.

Screenshot of the trusted entities that can assume the service-linked role

3. View the policy attached to the AWSServiceRoleForLexBots service-linked role

Choose AmazonLexBotPolicy on the Permissions tab to view the policy attached to the AWSServiceRoleForLexBots service-linked role. You can view the policy summary to see that AmazonLexBotPolicy grants permission to Amazon Lex to use Amazon Polly.

Screenshot showing that AmazonLexBotPolicy grants permission to Amazon Lex to use Amazon Polly

4. View the actions that the service-linked role grants permissions to use

Choose Polly to view the action, SynthesizeSpeech, that the AmazonLexBotPolicy grants permission to Amazon Lex to perform on your behalf. Amazon Lex uses this permission to synthesize speech responses for your chatbot. I show later in this post how you can monitor this SynthesizeSpeech action in your CloudTrail logs.

Screenshot showing the the action, SynthesizeSpeech, that the AmazonLexBotPolicy grants permission to Amazon Lex to perform on your behalf

Now that I know the trusted entity and the policy attached to the service-linked role, let’s go back to the chatbot I created earlier and see how CloudTrail logs the actions that Amazon Lex performs on my behalf.

How to use CloudTrail to view actions that AWS services perform on your behalf

As discussed already, I created an OrderFlowers chatbot on the Amazon Lex console. I will use the chatbot and display how the AWSServiceRoleForLexBots service-linked role helps me track actions in CloudTrail. First, though, I must have an active CloudTrail trail created that stores the logs in an Amazon S3 bucket. I will use a trail called TestTrail and an S3 bucket called account-ids-slr.

1. Use the Amazon Lex chatbot via the Amazon Lex console

In Step 2 in the first section of this post, when I chose Create, Amazon Lex built the OrderFlowers chatbot. After the chatbot was built, the right pane showed that a Test Bot was created. Now, I choose the microphone symbol in the right pane and provide voice input to test the OrderFlowers chatbot. In this example, I tell the chatbot, “I would like to order some flowers.” The bot replies to me by asking, “What type of flowers would you like to order?”

Screenshot of voice input to test the OrderFlowers chatbot

When the chatbot replies using voice, Amazon Lex uses Amazon Polly to synthesize speech from text to voice. Amazon Lex assumes the AWSServiceRoleForLexBots service-linked role to perform the SynthesizeSpeech action.

2. Check CloudTrail to view actions performed on your behalf

Now that I have created the chatbot, let’s see which actions were logged in CloudTrail. Choose CloudTrail from the Services drop-down menu to reach the CloudTrail console. Choose Trails and choose the S3 bucket in which you are storing your CloudTrail logs.

Screenshot of the TestTrail trail

In the S3 bucket, you will find log entries for the SynthesizeSpeech event. This means that CloudTrail logged the action when Amazon Lex assumed the AWSServiceRoleForLexBots service-linked role to invoke Amazon Polly to synthesize speech responses for your chatbot. You can monitor and audit this invocation, and it provides you with transparency into Amazon Polly’s SynthesizeSpeech action that Amazon Lex invoked on your behalf. The applicable CloudTrail log section follows and I have emphasized the key lines.

{  
         "eventVersion":"1.05",
         "userIdentity":{  
           "type":"AssumedRole",
            "principalId":"{principal-id}:OrderFlowers",
            "arn":"arn:aws:sts::{account-id}:assumed-role/AWSServiceRoleForLexBots/OrderFlowers",
            "accountId":"{account-id}",
            "accessKeyId":"{access-key-id}",
            "sessionContext":{  
               "attributes":{  
                  "mfaAuthenticated":"false",
                  "creationDate":"2017-09-17T17:30:05Z"
               },
               "sessionIssuer":{  
                  "type":"Role",
                  "principalId":"{principal-id}",
                  "arn":"arn:aws:iam:: {account-id}:role/aws-service-role/lex.amazonaws.com/AWSServiceRoleForLexBots",
                  "accountId":"{account-id",
                  "userName":"AWSServiceRoleForLexBots"
               }
            },
            "invokedBy":"lex.amazonaws.com"
         },
         "eventTime":"2017-09-17T17:30:05Z",
         "eventSource":"polly.amazonaws.com",
         "eventName":"SynthesizeSpeech",
         "awsRegion":"us-east-1",
         "sourceIPAddress":"lex.amazonaws.com",
         "userAgent":"lex.amazonaws.com",
         "requestParameters":{  
            "outputFormat":"mp3",
            "textType":"text",
            "voiceId":"Salli",
            "text":"**********"
         },
         "responseElements":{  
            "requestCharacters":45,
            "contentType":"audio/mpeg"
         },
         "requestID":"{request-id}",
         "eventID":"{event-id}",
         "eventType":"AwsApiCall",
         "recipientAccountId":"{account-id}"
      }

Conclusion

Service-linked roles make it easier for you to track and view actions that linked AWS services perform on your behalf by using CloudTrail. When an AWS service supports service-linked roles to enable this additional logging, you will see a service-linked role added to your account.

If you have comments about this post, submit a comment in the “Comments” section below. If you have questions about working with service-linked roles, start a new thread on the IAM forum or contact AWS Support.

– Ujjwal

How to Query Personally Identifiable Information with Amazon Macie

Post Syndicated from Chad Woolf original https://aws.amazon.com/blogs/security/how-to-query-personally-identifiable-information-with-amazon-macie/

Amazon Macie logo

In August 2017 at the AWS Summit New York, AWS launched a new security and compliance service called Amazon Macie. Macie uses machine learning to automatically discover, classify, and protect sensitive data in AWS. In this blog post, I demonstrate how you can use Macie to help enable compliance with applicable regulations, starting with data retention.

How to query retained PII with Macie

Data retention and mandatory data deletion are common topics across compliance frameworks, so knowing what is stored and how long it has been or needs to be stored is of critical importance. For example, you can use Macie for Payment Card Industry Data Security Standard (PCI DSS) 3.2, requirement 3, “Protect stored cardholder data,” which mandates a “quarterly process for identifying and securely deleting stored cardholder data that exceeds defined retention.” You also can use Macie for ISO 27017 requirement 12.3.1, which calls for “retention periods for backup data.” In each of these cases, you can use Macie’s built-in queries to identify the age of data in your Amazon S3 buckets and to help meet your compliance needs.

To get started with Macie and run your first queries of personally identifiable information (PII) and sensitive data, follow the initial setup as described in the launch post on the AWS Blog. After you have set up Macie, walk through the following steps to start running queries. Start by focusing on the S3 buckets that you want to inventory and capture important compliance related activity and data.

To start running Macie queries:

  1. In the AWS Management Console, launch the Macie console (you can type macie to find the console).
  2. Click Dashboard in the navigation pane. This shows you an overview of the risk level and data classification type of all inventoried S3 buckets, categorized by date and type.
    Screenshot of "Dashboard" in the navigation pane
  3. Choose S3 objects by PII priority. This dashboard lets you sort by PII priority and PII types.
    Screenshot of "S3 objects by PII priority"
  4. In this case, I want to find information about credit card numbers. I choose the magnifying glass for the type cc_number (note that PII types can be used for custom queries). This view shows the events where PII classified data has been uploaded to S3. When I scroll down, I see the individual files that have been identified.
    Screenshot showing the events where PII classified data has been uploaded to S3
  5. Before looking at the files, I want to continue to build the query by only showing items with high priority. To do so, I choose the row called Object PII Priority and then the magnifying glass icon next to High.
    Screenshot of refining the query for high priority events
  6. To view the results matching these queries, I scroll down and choose any file listed. This shows vital information such as creation date, location, and object access control list (ACL).
  7. The piece I am most interested in this case is the Object PII details line to understand more about what was found in the file. In this case, I see name and credit card information, which is what caused the high priority. Scrolling up again, I also see that the query fields have updated as I interacted with the UI.
    Screenshot showing "Object PII details"

Let’s say that I want to get an alert every time Macie finds new data matching this query. This alert can be used to automate response actions by using AWS Lambda and Amazon CloudWatch Events.

  1. I choose the left green icon called Save query as alert.
    Screenshot of "Save query as alert" button
  2. I can customize the alert and change things like category or severity to fit my needs based on the alert data.
  3. Another way to find the information I am looking for is to run custom queries. To start using custom queries, I choose Research in the navigation pane.
    1. To learn more about custom Macie queries and what you can do on the Research tab, see Using the Macie Research Tab.
  4. I change the type of query I want to run from CloudTrail data to S3 objects in the drop-down list menu.
    Screenshot of choosing "S3 objects" from the drop-down list menu
  5. Because I want PII data, I start typing in the query box, which has an autocomplete feature. I choose the pii_types: query. I can now type the data I want to look for. In this case, I want to see all files matching the credit card filter so I type cc_number and press Enter. The query box now says, pii_types:cc_number. I press Enter again to enable autocomplete, and then I type AND pii_types:email to require both a credit card number and email address in a single object.
    The query looks for all files matching the credit card filter ("cc_number")
  6. I choose the magnifying glass to search and Macie shows me all S3 objects that are tagged as PII of type Credit Cards. I can further specify that I only want to see PII of type Credit Card that are classified as High priority by adding AND and pii_impact:high to the query.
    Screenshot showing narrowing the query results furtherAs before, I can save this new query as an alert by clicking Save query as alert, which will be triggered by data matching the query going forward.

Advanced tip

Try the following advanced queries using Lucene query syntax and save the queries as alerts in Macie.

  • Use a regular-expression based query to search for a minimum of 10 credit card numbers and 10 email addresses in a single object:
    • pii_explain.cc_number:/([1-9][0-9]|[0-9]{3,}) distinct Credit Card Numbers.*/ AND pii_explain.email:/([1-9][0-9]|[0-9]{3,}) distinct Email Addresses.*/
  • Search for objects containing at least one credit card, name, and email address that have an object policy enabling global access (searching for S3 AllUsers or AuthenticatedUsers permissions):
    • (object_acl.Grants.Grantee.URI:”http\://acs.amazonaws.com/groups/global/AllUsers” OR  object_acl.Grants.Grantee.URI:”http\://acs.amazonaws.com/groups/global/AllUsers”) AND (pii_types.cc_number AND pii_types.email AND pii_types.name)

These are two ways to identify and be alerted about PII by using Macie. In a similar way, you can create custom alerts for various AWS CloudTrail events by choosing a different data set on which to run the queries again. In the examples in this post, I identified credit cards stored in plain text (all data in this post is example data only), determined how long they had been stored in S3 by viewing the result details, and set up alerts to notify or trigger actions on new sensitive data being stored. With queries like these, you can build a reliable data validation program.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about how to use Macie, start a new thread on the Macie forum or contact AWS Support.

-Chad

Automate Your IT Operations Using AWS Step Functions and Amazon CloudWatch Events

Post Syndicated from Andy Katz original https://aws.amazon.com/blogs/compute/automate-your-it-operations-using-aws-step-functions-and-amazon-cloudwatch-events/


Rob Percival, Associate Solutions Architect

Are you interested in reducing the operational overhead of your AWS Cloud infrastructure? One way to achieve this is to automate the response to operational events for resources in your AWS account.

Amazon CloudWatch Events provides a near real-time stream of system events that describe the changes and notifications for your AWS resources. From this stream, you can create rules to route specific events to AWS Step Functions, AWS Lambda, and other AWS services for further processing and automated actions.

In this post, learn how you can use Step Functions to orchestrate serverless IT automation workflows in response to CloudWatch events sourced from AWS Health, a service that monitors and generates events for your AWS resources. As a real-world example, I show automating the response to a scenario where an IAM user access key has been exposed.

Serverless workflows with Step Functions and Lambda

Step Functions makes it easy to develop and orchestrate components of operational response automation using visual workflows. Building automation workflows from individual Lambda functions that perform discrete tasks lets you develop, test, and modify the components of your workflow quickly and seamlessly. As serverless services, Step Functions and Lambda also provide the benefits of more productive development, reduced operational overhead, and no costs incurred outside of when the workflows are actively executing.

Example workflow

As an example, this post focuses on automating the response to an event generated by AWS Health when an IAM access key has been publicly exposed on GitHub. This is a diagram of the automation workflow:

AWS proactively monitors popular code repository sites for IAM access keys that have been publicly exposed. Upon detection of an exposed IAM access key, AWS Health generates an AWS_RISK_CREDENTIALS_EXPOSED event in the AWS account related to the exposed key. A configured CloudWatch Events rule detects this event and invokes a Step Functions state machine. The state machine then orchestrates the automated workflow that deletes the exposed IAM access key, summarizes the recent API activity for the exposed key, and sends the summary message to an Amazon SNS topic to notify the subscribers―in that order.

The corresponding Step Functions state machine diagram of this automation workflow can be seen below:

While this particular example focuses on IT automation workflows in response to the AWS_RISK_CREDENTIALS_EXPOSEDevent sourced from AWS Health, it can be generalized to integrate with other events from these services, other event-generating AWS services, and even run on a time-based schedule.

Walkthrough

To follow along, use the code and resources found in the aws-health-tools GitHub repo. The code and resources include an AWS CloudFormation template, in addition to instructions on how to use it.

Launch Stack into N. Virginia with CloudFormation

The Step Functions state machine execution starts with the exposed keys event details in JSON, a sanitized example of which is provided below:

{
    "version": "0",
    "id": "121345678-1234-1234-1234-123456789012",
    "detail-type": "AWS Health Event",
    "source": "aws.health",
    "account": "123456789012",
    "time": "2016-06-05T06:27:57Z",
    "region": "us-east-1",
    "resources": [],
    "detail": {
        "eventArn": "arn:aws:health:us-east-1::event/AWS_RISK_CREDENTIALS_EXPOSED_XXXXXXXXXXXXXXXXX",
        "service": "RISK",
        "eventTypeCode": "AWS_RISK_CREDENTIALS_EXPOSED",
        "eventTypeCategory": "issue",
        "startTime": "Sat, 05 Jun 2016 15:10:09 GMT",
        "eventDescription": [
            {
                "language": "en_US",
                "latestDescription": "A description of the event is provided here"
            }
        ],
        "affectedEntities": [
            {
                "entityValue": "ACCESS_KEY_ID_HERE"
            }
        ]
    }
}

After it’s invoked, the state machine execution proceeds as follows.

Step 1: Delete the exposed IAM access key pair

The first thing you want to do when you determine that an IAM access key has been exposed is to delete the key pair so that it can no longer be used to make API calls. This Step Functions task state deletes the exposed access key pair detailed in the incoming event, and retrieves the IAM user associated with the key to look up API activity for the user in the next step. The user name, access key, and other details about the event are passed to the next step as JSON.

This state contains a powerful error-handling feature offered by Step Functions task states called a catch configuration. Catch configurations allow you to reroute and continue state machine invocation at new states depending on potential errors that occur in your task function. In this case, the catch configuration skips to Step 3. It immediately notifies your security team that errors were raised in the task function of this step (Step 1), when attempting to look up the corresponding IAM user for a key or delete the user’s access key.

Note: Step Functions also offers a retry configuration for when you would rather retry a task function that failed due to error, with the option to specify an increasing time interval between attempts and a maximum number of attempts.

Step 2: Summarize recent API activity for key

After you have deleted the access key pair, you’ll want to have some immediate insight into whether it was used for malicious activity in your account. Another task state, this step uses AWS CloudTrail to look up and summarize the most recent API activity for the IAM user associated with the exposed key. The summary is in the form of counts for each API call made and resource type and name affected. This summary information is then passed to the next step as JSON. This step requires information that you obtained in Step 1. Step Functions ensures the successful completion of Step 1 before moving to Step 2.

Step 3: Notify security

The summary information gathered in the last step can provide immediate insight into any malicious activity on your account made by the exposed key. To determine this and further secure your account if necessary, you must notify your security team with the gathered summary information.

This final task state generates an email message providing in-depth detail about the event using the API activity summary, and publishes the message to an SNS topic subscribed to by the members of your security team.

If the catch configuration of the task state in Step 1 was triggered, then the security notification email instead directs your security team to log in to the console and navigate to the Personal Health Dashboard to view more details on the incident.

Lessons learned

When implementing this use case with Step Functions and Lambda, consider the following:

  • One of the most important parts of implementing automation in response to operational events is to ensure visibility into the response and resolution actions is retained. Step Functions and Lambda enable you to orchestrate your granular response and resolution actions that provides direct visibility into the state of the automation workflow.
  • This basic workflow currently executes these steps serially with a catch configuration for error handling. More sophisticated workflows can leverage the parallel execution, branching logic, and time delay functionality provided by Step Functions.
  • Catch and retry configurations for task states allow for orchestrating reliable workflows while maintaining the granularity of each Lambda function. Without leveraging a catch configuration in Step 1, you would have had to duplicate code from the function in Step 3 to ensure that your security team was notified on failure to delete the access key.
  • Step Functions and Lambda are serverless services, so there is no cost for these services when they are not running. Because this IT automation workflow only runs when an IAM access key is exposed for this account (which is hopefully rare!), the total monthly cost for this workflow is essentially $0.

Conclusion

Automating the response to operational events for resources in your AWS account can free up the valuable time of your engineers. Step Functions and Lambda enable granular IT automation workflows to achieve this result while gaining direct visibility into the orchestration and state of the automation.

For more examples of how to use Step Functions to automate the operations of your AWS resources, or if you’d like to see how Step Functions can be used to build and orchestrate serverless applications, visit Getting Started on the Step Functions website.

Prime Day 2017 – Powered by AWS

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/prime-day-2017-powered-by-aws/

The third annual Prime Day set another round of records for global orders, topping Black Friday and Cyber Monday, making it the biggest day in Amazon retail history. Over the course of the 30 hour event, tens of millions of Prime members purchased things like Echo Dots, Fire tablets, programmable pressure cookers, espresso machines, rechargeable batteries, and much more! July 11th also set a record for the number of new Prime memberships, as people signed up in order to take advantage of hundreds of thousands of deals. Amazon customers shopped online and made heavy use of the Amazon App, with mobile orders more than doubling from last Prime Day.

Powered by AWS
Last year I told you about How AWS Powered Amazon’s Biggest Day Ever, and shared what the team had learned with regard to preparation, automation, monitoring, and thinking big. All of those lessons still apply and you can read that post to learn more. Preparation for this year’s Prime Day (which started just days after Prime Day 2016 wrapped up) started by collecting and sharing best practices and identifying areas for improvement, proceeding to implementation and stress testing as the big day approached. Two of the best practices involve auditing and GameDay:

Auditing – This is a formal way for us to track preparations, identify risks, and to track progress against our objectives. Each team must respond to a series of detailed technical and operational questions that are designed to help them determine their readiness. On the technical side, questions could revolve around time to recovery after a database failure, including the all-important check of the TTL (time to live) for the CNAME. Operational questions address schedules for on-call personnel, points of contact, and ownership of services & instances.

GameDay – This practice (which I believe originated with former Amazonian Jesse Robbins), is intended to validate all of the capacity planning & preparation and to verify that all of the necessary operational practices are in place and work as expected. It introduces simulated failures and helps to train the team to identify and quickly resolve issues, building muscle memory in the process. It also tests failover and recovery capabilities, and can expose latent defects that are lurking under the covers. GameDays help teams to understand scaling drivers (page views, orders, and so forth) and gives them an opportunity to test their scaling practices. To learn more, read Resilience Engineering: Learning to Embrace Failure or watch the video: GameDay: Creating Resiliency Through Destruction.

Prime Day 2017 Metrics
So, how did we do this year?

The AWS teams checked their dashboards and log files, and were happy to share their metrics with me. Here are a few of the most interesting ones:

Block Storage – Use of Amazon Elastic Block Store (EBS) grew by 40% year-over-year, with aggregate data transfer jumping to 52 petabytes (a 50% increase) for the day and total I/O requests rising to 835 million (a 30% increase). The team told me that they loved the elasticity of EBS, and that they were able to ramp down on capacity after Prime Day concluded instead of being stuck with it.

NoSQL Database – Amazon DynamoDB requests from Alexa, the Amazon.com sites, and the Amazon fulfillment centers totaled 3.34 trillion, peaking at 12.9 million per second. According to the team, the extreme scale, consistent performance, and high availability of DynamoDB let them meet needs of Prime Day without breaking a sweat.

Stack Creation – Nearly 31,000 AWS CloudFormation stacks were created for Prime Day in order to bring additional AWS resources on line.

API Usage – AWS CloudTrail processed over 50 billion events and tracked more than 419 billion calls to various AWS APIs, all in support of Prime Day.

Configuration TrackingAWS Config generated over 14 million Configuration items for AWS resources.

You Can Do It
Running an event that is as large, complex, and mission-critical as Prime Day takes a lot of planning. If you have an event of this type in mind, please take a look at our new Infrastructure Event Readiness white paper. Inside, you will learn how to design and provision your applications to smoothly handle planned scaling events such as product launches or seasonal traffic spikes, with sections on automation, resiliency, cost optimization, event management, and more.

Jeff;

 

AWS Summit New York – Summary of Announcements

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-summit-new-york-summary-of-announcements/

Whew – what a week! Tara, Randall, Ana, and I have been working around the clock to create blog posts for the announcements that we made at the AWS Summit in New York. Here’s a summary to help you to get started:

Amazon Macie – This new service helps you to discover, classify, and secure content at scale. Powered by machine learning and making use of Natural Language Processing (NLP), Macie looks for patterns and alerts you to suspicious behavior, and can help you with governance, compliance, and auditing. You can read Tara’s post to see how to put Macie to work; you select the buckets of interest, customize the classification settings, and review the results in the Macie Dashboard.

AWS GlueRandall’s post (with deluxe animated GIFs) introduces you to this new extract, transform, and load (ETL) service. Glue is serverless and fully managed, As you can see from the post, Glue crawls your data, infers schemas, and generates ETL scripts in Python. You define jobs that move data from place to place, with a wide selection of transforms, each expressed as code and stored in human-readable form. Glue uses Development Endpoints and notebooks to provide you with a testing environment for the scripts you build. We also announced that Amazon Athena now integrates with Amazon Glue, as does Apache Spark and Hive on Amazon EMR.

AWS Migration Hub – This new service will help you to migrate your application portfolio to AWS. My post outlines the major steps and shows you how the Migration Hub accelerates, tracks,and simplifies your migration effort. You can begin with a discovery step, or you can jump right in and migrate directly. Migration Hub integrates with tools from our migration partners and builds upon the Server Migration Service and the Database Migration Service.

CloudHSM Update – We made a major upgrade to AWS CloudHSM, making the benefits of hardware-based key management available to a wider audience. The service is offered on a pay-as-you-go basis, and is fully managed. It is open and standards compliant, with support for multiple APIs, programming languages, and cryptography extensions. CloudHSM is an integral part of AWS and can be accessed from the AWS Management Console, AWS Command Line Interface (CLI), and through API calls. Read my post to learn more and to see how to set up a CloudHSM cluster.

Managed Rules to Secure S3 Buckets – We added two new rules to AWS Config that will help you to secure your S3 buckets. The s3-bucket-public-write-prohibited rule identifies buckets that have public write access and the s3-bucket-public-read-prohibited rule identifies buckets that have global read access. As I noted in my post, you can run these rules in response to configuration changes or on a schedule. The rules make use of some leading-edge constraint solving techniques, as part of a larger effort to use automated formal reasoning about AWS.

CloudTrail for All Customers – Tara’s post revealed that AWS CloudTrail is now available and enabled by default for all AWS customers. As a bonus, Tara reviewed the principal benefits of CloudTrail and showed you how to review your event history and to deep-dive on a single event. She also showed you how to create a second trail, for use with CloudWatch CloudWatch Events.

Encryption of Data at Rest for EFS – When you create a new file system, you now have the option to select a key that will be used to encrypt the contents of the files on the file system. The encryption is done using an industry-standard AES-256 algorithm. My post shows you how to select a key and to verify that it is being used.

Watch the Keynote
My colleagues Adrian Cockcroft and Matt Wood talked about these services and others on the stage, and also invited some AWS customers to share their stories. Here’s the video:

Jeff;

 

New: Server-Side Encryption for Amazon Kinesis Streams

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/new-server-side-encryption-for-amazon-kinesis-streams/

In this age of smart homes, big data, IoT devices, mobile phones, social networks, chatbots, and game consoles, streaming data scenarios are everywhere. Amazon Kinesis Streams enables you to build custom applications that can capture, process, analyze, and store terabytes of data per hour from thousands of streaming data sources. Since Amazon Kinesis Streams allows applications to process data concurrently from the same Kinesis stream, you can build parallel processing systems. For example, you can emit processed data to Amazon S3, perform complex analytics with Amazon Redshift, and even build robust, serverless streaming solutions using AWS Lambda.

Kinesis Streams enables several streaming use cases for consumers, and now we are making the service more effective for securing your data in motion by adding server-side encryption (SSE) support for Kinesis Streams. With this new Kinesis Streams feature, you can now enhance the security of your data and/or meet any regulatory and compliance requirements for any of your organization’s data streaming needs.
In fact, Kinesis Streams is now one of the AWS Services in Scope for the Payment Card Industry Data Security Standard (PCI DSS) compliance program. PCI DSS is a proprietary information security standard administered by the PCI Security Standards Council founded by key financial institutions. PCI DSS compliance applies to all entities that store, process, or transmit cardholder data and/or sensitive authentication data which includes service providers. You can request the PCI DSS Attestation of Compliance and Responsibility Summary using AWS Artifact. But the good news about compliance with Kinesis Streams doesn’t stop there. Kinesis Streams is now also FedRAMP compliant in AWS GovCloud. FedRAMP stands for Federal Risk and Authorization Management Program and is a U.S. government-wide program that delivers a standard approach to the security assessment, authorization, and continuous monitoring for cloud products and services. You can learn more about FedRAMP compliance with AWS Services here.

Now are you ready to get into the keys? Get it, instead of get into the weeds. Okay a little corny, but it was the best I could do. Coming back to discussing SSE for Kinesis Streams, let me explain the flow of server-side encryption with Kinesis.  Each data record and partition key put into a Kinesis Stream using the PutRecord or PutRecords API is encrypted using an AWS Key Management Service (KMS) master key. With the AWS Key Management Service (KMS) master key, Kinesis Streams uses the 256-bit Advanced Encryption Standard (AES-256 GCM algorithm) to add encryption to the incoming data.

In order to enable server-side encryption with Kinesis Streams for new or existing streams, you can use the Kinesis management console or leverage one of the available AWS SDKs.  Additionally, you can audit the history of your stream encryption, validate the encryption status of a certain stream in the Kinesis Streams console, or check that the PutRecord or GetRecord transactions are encrypted using the AWS CloudTrail service.

 

Walkthrough: Kinesis Streams Server-Side Encryption

Let’s do a quick walkthrough of server-side encryption with Kinesis Streams. First, I’ll go to the Amazon Kinesis console and select the Streams console option.

Once in the Kinesis Streams console, I can add server-side encryption to one of my existing Kinesis streams or opt to create a new Kinesis stream.  For this walkthrough, I’ll opt to quickly create a new Kinesis stream, therefore, I’ll select the Create Kinesis stream button.

I’ll name my stream, KinesisSSE-stream, and allocate one shard for my stream. Remember that the data capacity of your stream is calculated based upon the number of shards specified for the stream.  You can use the Estimate the number of shards you’ll need dropdown within the console or read more calculations to estimate the number of shards in a stream here.  To complete the creation of my stream, now I click the Create Kinesis stream button.

 

With my KinesisSSE-stream created, I will select it in the dashboard and choose the Actions dropdown and select the Details option.


On the Details page of the KinesisSSE-stream, there is now a Server-side encryption section.  In this section, I will select the Edit button.

 

 

Now I can enable server-side encryption for my stream with an AWS KMS master key, by selecting the Enabled radio button. Once selected I can choose which AWS KMS master key to use for the encryption of  data in KinesisSSE-stream. I can either select the KMS master key generated by the Kinesis service, (Default) aws/kinesis, or select one of my own KMS master keys that I have previously generated.  I’ll select the default master key and all that is left is for me to click the Save button.


That’s it!  As you can see from my screenshots below, after only about 20 seconds, server-side encryption was added to my Kinesis stream and now any incoming data into my stream will be encrypted.  One thing to note is server-side encryption only encrypts incoming data after encryption has been enabled. Preexisting data that is in a Kinesis stream prior to server-side encryption being enabled will remain unencrypted.

 

Summary

Kinesis Streams with Server-side encryption using AWS KMS keys makes it easy for you to automatically encrypt the streaming data coming into your  stream. You can start, stop, or update server-side encryption for any Kinesis stream using the AWS management console or the AWS SDK. To learn more about Kinesis Server-Side encryption, AWS Key Management Service, or about Kinesis Streams review the Amazon Kinesis getting started guide, the AWS Key Management Service developer guide, or the Amazon Kinesis product page.

 

Enjoy streaming.

Tara

Under the Hood of Server-Side Encryption for Amazon Kinesis Streams

Post Syndicated from Damian Wylie original https://aws.amazon.com/blogs/big-data/under-the-hood-of-server-side-encryption-for-amazon-kinesis-streams/

Customers are using Amazon Kinesis Streams to ingest, process, and deliver data in real time from millions of devices or applications. Use cases for Kinesis Streams vary, but a few common ones include IoT data ingestion and analytics, log processing, clickstream analytics, and enterprise data bus architectures.

Within milliseconds of data arrival, applications (KCL, Apache Spark, AWS Lambda, Amazon Kinesis Analytics) attached to a stream are continuously mining value or delivering data to downstream destinations. Customers are then scaling their streams elastically to match demand. They pay incrementally for the resources that they need, while taking advantage of a fully managed, serverless streaming data service that allows them to focus on adding value closer to their customers.

These benefits are great; however, AWS learned that many customers could not take advantage of Kinesis Streams unless their data-at-rest within a stream was encrypted. Many customers did not want to manage encryption on their own, so they asked for a fully managed, automatic, server-side encryption mechanism leveraging centralized AWS Key Management Service (AWS KMS) customer master keys (CMK).

Motivated by this feedback, AWS added another fully managed, low cost aspect to Kinesis Streams by delivering server-side encryption via KMS managed encryption keys (SSE-KMS) in the following regions:

  • US East (N. Virginia)
  • US West (Oregon)
  • US West (N. California)
  • EU (Ireland)
  • Asia Pacific (Singapore)
  • Asia Pacific (Tokyo)

In this post, I cover the mechanics of the Kinesis Streams server-side encryption feature. I also share a few best practices and considerations so that you can get started quickly.

Understanding the mechanics

The following section walks you through how Kinesis Streams uses CMKs to encrypt a message in the PutRecord or PutRecords path before it is propagated to the Kinesis Streams storage layer, and then decrypt it in the GetRecords path after it has been retrieved from the storage layer.

When server-side encryption is enabled—which takes just a few clicks in the console—the partition key and payload for every incoming record is encrypted automatically as it’s flowing into Kinesis Streams, using the selected CMK. When data is at rest within a stream, it’s encrypted.

When records are retrieved through a GetRecords request from the encrypted stream, they are decrypted automatically as they are flowing out of the service. That means your Kinesis Streams producers and consumers do not need to be aware of encryption. You have a fully managed data encryption feature at your fingertips, which can be enabled within seconds.

AWS also makes it easy to audit the application of server-side encryption. You can use the AWS Management Console for instant stream-level verification; the responses from PutRecord, PutRecords, and getRecords; or AWS CloudTrail.

Calling PutRecord or PutRecords

When server-side encryption is enabled for a particular stream, Kinesis Streams and KMS perform the following actions when your applications call PutRecord or PutRecords on a stream with server-side encryption enabled. The Amazon Kinesis Producer Library (KPL) uses PutRecords.

 

  1. Data is sent from a customer’s producer (client) to a Kinesis stream using TLS via HTTPS. Data in transit to a stream is encrypted by default.
  2. After data is received, it is momentarily stored in RAM within a front-end proxy layer.
  3. Kinesis Streams authenticates the producer, then impersonates the producer to request input keying material from KMS.
  4. KMS creates key material, encrypts it by using CMK, and sends both the plaintext and encrypted key material to the service, encrypted with TLS.
  5. The client uses the plaintext key material to derive data encryption keys (data keys) that are unique per-record.
  6. The client encrypts the payload and partition key using the data key in RAM within the front-end proxy layer and removes the plaintext data key from memory.
  7. The client appends the encrypted key material to the encrypted data.
  8. The plaintext key material is securely cached in memory within the front-end layer for reuse, until it expires after 5 minutes.
  9. The client delivers the encrypted message to a back-end store where it is stored at rest and fetchable by an authorized consumer through a GetRecords The Amazon Kinesis Client Library (KCL) calls GetRecords to retrieve records from a stream.

Calling getRecords

Kinesis Streams and KMS perform the following actions when your applications call GetRecords on a server-side encrypted stream.

 

  1. When a GeRecords call is made, the front-end proxy layer retrieves the encrypted record from its back-end store.
  2. The consumer (client) makes a request to KMS using a token generated by the customer’s request. KMS authorizes it.
  3. The client requests that KMS decrypt the encrypted key material.
  4. KMS decrypts the encrypted key material and sends the plaintext key material to the client.
  5. Kinesis Streams derives the per-record data keys from the decrypted key material.
  6. If the calling application is authorized, the client decrypts the payload and removes the plaintext data key from memory.
  7. The client delivers the payload over TLS and HTTPS to the consumer, requesting the records. Data in transit to a consumer is encrypted by default.

Verifying server-side encryption

Auditors or administrators often ask for proof that server-side encryption was or is enabled. Here are a few ways to do this.

To check if encryption is enabled now for your streams:

  • Use the AWS Management Console or the DescribeStream API operation. You can also see what CMK is being used for encryption.
  • See encryption in action by looking at responses from PutRecord, PutRecords, or GetRecords When encryption is enabled, the encryptionType parameter is set to “KMS”. If encryption is not enabled, encryptionType is not included in the response.

Sample PutRecord response

{
    "SequenceNumber": "49573959617140871741560010162505906306417380215064887298",
    "ShardId": "shardId-000000000000",
    "EncryptionType": "KMS"
}

Sample GetRecords response

{
    "Records": [
        {
            "Data": "aGVsbG8gd29ybGQ=", 
            "PartitionKey": "test", 
            "ApproximateArrivalTimestamp": 1498292565.825, 
            "EncryptionType": "KMS", 
            "SequenceNumber": "495735762417140871741560010162505906306417380215064887298"
        }, 
        {
            "Data": "ZnJvZG8gbGl2ZXMK", 
            "PartitionKey": "3d0d9301-3c30-4c48-a9a8-e485b2982b28", 
            "ApproximateArrivalTimestamp": 1498292801.747, 
            "EncryptionType": "KMS", 
            "SequenceNumber": "49573959617140871741560010162507115232237011062036103170"
        }
    ], 
    "NextShardIterator": "AAAAAAAAAAEvFypHZDx/4bJVAS34puwdiNcwssKqbh/XhRK7HSYRq3RS+YXJnVKJ8j0gQUt94bONdqQYHk9X9JHgefMUDKzDzndy5WbZWO4CS3hRdMdrbmJ/9KoR4lOfZvqTLt6JWQjDqXv0IaKs06/LHYcEA3oPcyQLOTJHdJl2EzplCTZnn/U295ovxvqF9g9DY8y2nVoMkdFLmdcEMVXjhCDKiRIt", 
    "MillisBehindLatest": 0
}

To check if encryption was enabled, use CloudTrail, which logs the StartStreamEncryption() and StopStreamEncryption() API calls made against a particular stream.

Getting started

It’s very easy to enable, disable, or modify server-side encryption for a particular stream.

  1. In the Kinesis Streams console, select a stream and choose Details.
  2. Select a CMK and select Enabled.
  3. Choose Save.

You can enable encryption only for a live stream, not upon stream creation.  Follow the same process to disable a stream. To use a different CMK, select it and choose Save.

Each of these tasks can also be accomplished using the StartStreamEncryption and StopStreamEncryption API operations.

Considerations

There are a few considerations you should be aware of when using server-side encryption for Kinesis Streams:

  • Permissions
  • Costs
  • Performance

Permissions

One benefit of using the “(Default) aws/kinesis” AWS managed key is that every producer and consumer with permissions to call PutRecord, PutRecords, or GetRecords inherits the right permissions over the “(Default) aws/kinesis” key automatically.

However, this is not necessarily the same case for a CMK. Kinesis Streams producers and consumers do not need to be aware of encryption. However, if you enable encryption using a custom master key but a producer or consumer doesn’t have IAM permissions to use it, PutRecord, PutRecords, or GetRecords requests fail.

This is a great security feature. On the other hand, it can effectively lead to data loss if you inadvertently apply a custom master key that restricts producers and consumers from interacting from the Kinesis stream. Take precautions when applying a custom master key. For more information about the minimum IAM permissions required for producers and consumers interacting with an encrypted stream, see Using Server-Side Encryption.

Costs

When you apply server-side encryption, you are subject to KMS API usage and key costs. Unlike custom KMS master keys, the “(Default) aws/kinesis” CMK is offered free of charge. However, you still need to pay for the API usage costs that Kinesis Streams incurs on your behalf.

API usage costs apply for every CMK, including custom ones. Kinesis Streams calls KMS approximately every 5 minutes when it is rotating the data key. In a 30-day month, the total cost of KMS API calls initiated by a Kinesis stream should be less than a few dollars.

Performance

During testing, AWS discovered that there was a slight increase (typically 0.2 millisecond or less per record) with put and get record latencies due to the additional overhead of encryption.

If you have questions or suggestions, please comment below.

New Information in the AWS IAM Console Helps You Follow IAM Best Practices

Post Syndicated from Rob Moncur original https://aws.amazon.com/blogs/security/newly-updated-features-in-the-aws-iam-console-help-you-adhere-to-iam-best-practices/

Today, we added new information to the Users section of the AWS Identity and Access Management (IAM) console to make it easier for you to follow IAM best practices. With this new information, you can more easily monitor users’ activity in your AWS account and identify access keys and passwords that you should rotate regularly. You can also better audit users’ MFA device usage and keep track of their group memberships. In this post, I show how you can use this new information to help you follow IAM best practices.

Monitor activity in your AWS account

The IAM best practice, monitor activity in your AWS account, encourages you to monitor user activity in your AWS account by using services such as AWS CloudTrail and AWS Config. In addition to monitoring usage in your AWS account, you should be aware of inactive users so that you can remove them from your account. By only retaining necessary users, you can help maintain the security of your AWS account.

To help you find users that are inactive, we added three new columns to the IAM user table: Last activity, Console last sign-in, and Access key last used.
Screenshot showing three new columns in the IAM user table

  1. Last activity – This column tells you how long it has been since the user has either signed in to the AWS Management Console or accessed AWS programmatically with their access keys. Use this column to find users who might be inactive, and consider removing them from your AWS account.
  2. Console last sign-in – This column displays the time since the user’s most recent console sign-in. Consider removing passwords from users who are not signing in to the console.
  3. Access key last used – This column displays the time since a user last used access keys. Use this column to find any access keys that are not being used, and deactivate or remove them.

Rotate credentials regularly

The IAM best practice, rotate credentials regularly, recommends that all users in your AWS account change passwords and access keys regularly. With this practice, if a password or access key is compromised without your knowledge, you can limit how long the credentials can be used to access your resources. To help your management efforts, we added three new columns to the IAM user table: Access key age, Password age, and Access key ID.

Screenshot showing three new columns in the IAM user table

  1. Access key age – This column shows how many days it has been since the oldest active access key was created for a user. With this information, you can audit access keys easily across all your users and identify the access keys that may need to be rotated.

Based on the number of days since the access key has been rotated, a green, yellow, or red icon is displayed. To see the corresponding time frame for each icon, pause your mouse pointer on the Access key age column heading to see the tooltip, as shown in the following screenshot.

Icons showing days since the oldest active access key was created

  1. Password age – This column shows the number of days since a user last changed their password. With this information, you can audit password rotation and identify users who have not changed their password recently. The easiest way to make sure that your users are rotating their password often is to establish an account password policy that requires users to change their password after a specified time period.
  2. Access key ID – This column displays the access key IDs for users and the current status (Active/Inactive) of those access key IDs. This column makes it easier for you to locate and see the state of access keys for each user, which is useful for auditing. To find a specific access key ID, use the search box above the table.

Enable MFA for privileged users

Another IAM best practice is to enable multi-factor authentication (MFA) for privileged IAM users. With MFA, users have a device that generates a unique authentication code (a one-time password [OTP]). Users must provide both their normal credentials (such as their user name and password) and the OTP when signing in.

To help you see if MFA has been enabled for your users, we’ve improved the MFA column to show you if MFA is enabled and which type of MFA (hardware, virtual, or SMS) is enabled for each user, where applicable.

Screenshot showing the improved "MFA" column

Use groups to assign permissions to IAM users

Instead of defining permissions for individual IAM users, it’s usually more convenient to create groups that relate to job functions (such as administrators, developers, and accountants), define the relevant permissions for each group, and then assign IAM users to those groups. All the users in an IAM group inherit the permissions assigned to the group. This way, if you need to modify permissions, you can make the change once for everyone in a group instead of making the change one time for each user. As people move around in your company, you can change the group membership of the IAM user.

To better understand which groups your users belong to, we’ve made updates:

  1. Groups – This column now lists the groups of which a user is a member. This information makes it easier to understand and compare multiple users’ permissions at once.
  2. Group count – This column shows the number of groups to which each user belongs.Screenshot showing the updated "Groups" and "Group count" columns

Customize your view

Choosing which columns you see in the User table is easy to do. When you click the button with the gear icon in the upper right corner of the table, you can choose the columns you want to see, as shown in the following screenshots.

Screenshot showing gear icon  Screenshot of "Manage columns" dialog box

Conclusion

We made these improvements to the Users section of the IAM console to make it easier for you to follow IAM best practices in your AWS account. Following these best practices can help you improve the security of your AWS resources and make your account easier to manage.

If you have comments about this post, submit them in the “Comments” section below. If you have questions or suggestions, please start a new thread on the IAM forum.

– Rob

AWS Online Tech Talks – June 2017

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2017/

As the sixth month of the year, June is significant in that it is not only my birth month (very special), but it contains the summer solstice in the Northern Hemisphere, the day with the most daylight hours, and the winter solstice in the Southern Hemisphere, the day with the fewest daylight hours. In the United States, June is also the month in which we celebrate our dads with Father’s Day and have month-long celebrations of music, heritage, and the great outdoors.

Therefore, the month of June can be filled with lots of excitement. So why not add even more delight to the month, by enhancing your cloud computing skills. This month’s AWS Online Tech Talks features sessions on Artificial Intelligence (AI), Storage, Big Data, and Compute among other great topics.

June 2017 – Schedule

Noted below are the upcoming scheduled live, online technical sessions being held during the month of June. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts. All schedule times for the online tech talks are shown in the Pacific Time (PDT) time zone.

Webinars featured this month are:

Thursday, June 1

Storage

9:00 AM – 10:00 AM: Deep Dive on Amazon Elastic File System

Big Data

10:30 AM – 11:30 AM: Migrating Big Data Workloads to Amazon EMR

Serverless

12:00 Noon – 1:00 PM: Building AWS Lambda Applications with the AWS Serverless Application Model (AWS SAM)

 

Monday, June 5

Artificial Intelligence

9:00 AM – 9:40 AM: Exploring the Business Use Cases for Amazon Lex

 

Tuesday, June 6

Management Tools

9:00 AM – 9:40 AM: Automated Compliance and Governance with AWS Config and AWS CloudTrail

 

Wednesday, June 7

Storage

9:00 AM – 9:40 AM: Backing up Amazon EC2 with Amazon EBS Snapshots

Big Data

10:30 AM – 11:10 AM: Intro to Amazon Redshift Spectrum: Quickly Query Exabytes of Data in S3

DevOps

12:00 Noon – 12:40 PM: Introduction to AWS CodeStar: Quickly Develop, Build, and Deploy Applications on AWS

 

Thursday, June 8

Artificial Intelligence

9:00 AM – 9:40 AM: Exploring the Business Use Cases for Amazon Polly

10:30 AM – 11:10 AM: Exploring the Business Use Cases for Amazon Rekognition

 

Monday, June 12

Artificial Intelligence

9:00 AM – 9:40 AM: Exploring the Business Use Cases for Amazon Machine Learning

 

Tuesday, June 13

Compute

9:00 AM – 9:40 AM: DevOps with Visual Studio, .NET and AWS

IoT

10:30 AM – 11:10 AM: Create, with Intel, an IoT Gateway and Establish a Data Pipeline to AWS IoT

Big Data

12:00 Noon – 12:40 PM: Real-Time Log Analytics using Amazon Kinesis and Amazon Elasticsearch Service

 

Wednesday, June 14

Containers

9:00 AM – 9:40 AM: Batch Processing with Containers on AWS

Security & Identity

12:00 Noon – 12:40 PM: Using Microsoft Active Directory across On-premises and Cloud Workloads

 

Thursday, June 15

Big Data

12:00 Noon – 1:00 PM: Building Big Data Applications with Serverless Architectures

 

Monday, June 19

Artificial Intelligence

9:00 AM – 9:40 AM: Deep Learning for Data Scientists: Using Apache MxNet and R on AWS

 

Tuesday, June 20

Storage

9:00 AM – 9:40 AM: Cloud Backup & Recovery Options with AWS Partner Solutions

Artificial Intelligence

10:30 AM – 11:10 AM: An Overview of AI on the AWS Platform

 

The AWS Online Tech Talks series covers a broad range of topics at varying technical levels. These sessions feature live demonstrations & customer examples led by AWS engineers and Solution Architects. Check out the AWS YouTube channel for more on-demand webinars on AWS technologies.

Tara

Building a Secure Cross-Account Continuous Delivery Pipeline

Post Syndicated from Anuj Sharma original https://aws.amazon.com/blogs/devops/aws-building-a-secure-cross-account-continuous-delivery-pipeline/

Most organizations create multiple AWS accounts because they provide the highest level of resource and security isolation. In this blog post, I will discuss how to use cross account AWS Identity and Access Management (IAM) access to orchestrate continuous integration and continuous deployment.

Do I need multiple accounts?

If you answer “yes” to any of the following questions you should consider creating more AWS accounts:

  • Does your business require administrative isolation between workloads? Administrative isolation by account is the most straightforward way to grant independent administrative groups different levels of administrative control over AWS resources based on workload, development lifecycle, business unit (BU), or data sensitivity.
  • Does your business require limited visibility and discoverability of workloads? Accounts provide a natural boundary for visibility and discoverability. Workloads cannot be accessed or viewed unless an administrator of the account enables access to users managed in another account.
  • Does your business require isolation to minimize blast radius? Separate accounts help define boundaries and provide natural blast-radius isolation to limit the impact of a critical event such as a security breach, an unavailable AWS Region or Availability Zone, account suspensions, and so on.
  • Does your business require a particular workload to operate within AWS service limits without impacting the limits of another workload? You can use AWS account service limits to impose restrictions on a business unit, development team, or project. For example, if you create an AWS account for a project group, you can limit the number of Amazon Elastic Compute Cloud (Amazon EC2) or high performance computing (HPC) instances that can be launched by the account.
  • Does your business require strong isolation of recovery or auditing data? If regulatory requirements require you to control access and visibility to auditing data, you can isolate the data in an account separate from the one where you run your workloads (for example, by writing AWS CloudTrail logs to a different account).
  • Do your workloads depend on specific instance reservations to support high availability (HA) or disaster recovery (DR) capacity requirements? Reserved Instances (RIs) ensure reserved capacity for services such as Amazon EC2 and Amazon Relational Database Service (Amazon RDS) at the individual account level.

Use case

The identities in this use case are set up as follows:

  • DevAccount

Developers check the code into an AWS CodeCommit repository. It stores all the repositories as a single source of truth for application code. Developers have full control over this account. This account is usually used as a sandbox for developers.

  • ToolsAccount

A central location for all the tools related to the organization, including continuous delivery/deployment services such as AWS CodePipeline and AWS CodeBuild. Developers have limited/read-only access in this account. The Operations team has more control.

  • TestAccount

Applications using the CI/CD orchestration for test purposes are deployed from this account. Developers and the Operations team have limited/read-only access in this account.

  • ProdAccount

Applications using the CI/CD orchestration tested in the ToolsAccount are deployed to production from this account. Developers and the Operations team have limited/read-only access in this account.

Solution

In this solution, we will check in sample code for an AWS Lambda function in the Dev account. This will trigger the pipeline (created in AWS CodePipeline) and run the build using AWS CodeBuild in the Tools account. The pipeline will then deploy the Lambda function to the Test and Prod accounts.

 

Setup

  1. Clone this repository. It contains the AWS CloudFormation templates that we will use in this walkthrough.
git clone https://github.com/awslabs/aws-refarch-cross-account-pipeline.git
  1. Follow the instructions in the repository README to push the sample AWS Lambda application to an AWS CodeCommit repository in the Dev account.
  2. Install the AWS Command Line Interface as described here. To prepare your access keys or assume-role to make calls to AWS, configure the AWS CLI as described here.

Walkthrough

Note: Follow the steps in the order they’re written. Otherwise, the resources might not be created correctly.

  1. In the Tools account, deploy this CloudFormation template. It will create the customer master keys (CMK) in AWS Key Management Service (AWS KMS), grant access to Dev, Test, and Prod accounts to use these keys, and create an Amazon S3 bucket to hold artifacts from AWS CodePipeline.
aws cloudformation deploy --stack-name pre-reqs \
--template-file ToolsAcct/pre-reqs.yaml --parameter-overrides \
DevAccount=ENTER_DEV_ACCT TestAccount=ENTER_TEST_ACCT \
ProductionAccount=ENTER_PROD_ACCT

In the output section of the CloudFormation console, make a note of the Amazon Resource Number (ARN) of the CMK and the S3 bucket name. You will need them in the next step.

  1. In the Dev account, which hosts the AWS CodeCommit repository, deploy this CloudFormation template. This template will create the IAM roles, which will later be assumed by the pipeline running in the Tools account. Enter the AWS account number for the Tools account and the CMK ARN.
aws cloudformation deploy --stack-name toolsacct-codepipeline-role \
--template-file DevAccount/toolsacct-codepipeline-codecommit.yaml \
--capabilities CAPABILITY_NAMED_IAM \
--parameter-overrides ToolsAccount=ENTER_TOOLS_ACCT CMKARN=FROM_1st_Step
  1. In the Test and Prod accounts where you will deploy the Lambda code, execute this CloudFormation template. This template creates IAM roles, which will later be assumed by the pipeline to create, deploy, and update the sample AWS Lambda function through CloudFormation.
aws cloudformation deploy --stack-name toolsacct-codepipeline-cloudformation-role \
--template-file TestAccount/toolsacct-codepipeline-cloudformation-deployer.yaml \
--capabilities CAPABILITY_NAMED_IAM \
--parameter-overrides ToolsAccount=ENTER_TOOLS_ACCT CMKARN=FROM_1st_STEP  \
S3Bucket=FROM_1st_STEP
  1. In the Tools account, which hosts AWS CodePipeline, execute this CloudFormation template. This creates a pipeline, but does not add permissions for the cross accounts (Dev, Test, and Prod).
aws cloudformation deploy --stack-name sample-lambda-pipeline \
--template-file ToolsAcct/code-pipeline.yaml \
--parameter-overrides DevAccount=ENTER_DEV_ACCT TestAccount=ENTER_TEST_ACCT \
ProductionAccount=ENTER_PROD_ACCT CMKARN=FROM_1st_STEP \
S3Bucket=FROM_1st_STEP--capabilities CAPABILITY_NAMED_IAM
  1. In the Tools account, execute this CloudFormation template, which give access to the role created in step 4. This role will be assumed by AWS CodeBuild to decrypt artifacts in the S3 bucket. This is the same template that was used in step 1, but with different parameters.
aws cloudformation deploy --stack-name pre-reqs \
--template-file ToolsAcct/pre-reqs.yaml \
--parameter-overrides CodeBuildCondition=true
  1. In the Tools account, execute this CloudFormation template, which will do the following:
    1. Add the IAM role created in step 2. This role is used by AWS CodePipeline in the Tools account for checking out code from the AWS CodeCommit repository in the Dev account.
    2. Add the IAM role created in step 3. This role is used by AWS CodePipeline in the Tools account for deploying the code package to the Test and Prod accounts.
aws cloudformation deploy --stack-name sample-lambda-pipeline \
--template-file ToolsAcct/code-pipeline.yaml \
--parameter-overrides CrossAccountCondition=true \
--capabilities CAPABILITY_NAMED_IAM

What did we just do?

  1. The pipeline created in step 4 and updated in step 6 checks out code from the AWS CodeCommit repository. It uses the IAM role created in step 2. The IAM role created in step 4 has permissions to assume the role created in step 2. This role will be assumed by AWS CodeBuild to decrypt artifacts in the S3 bucket, as described in step 5.
  2. The IAM role created in step 2 has permission to check out code. See here.
  3. The IAM role created in step 2 also has permission to upload the checked-out code to the S3 bucket created in step 1. It uses the KMS keys created in step 1 for server-side encryption.
  4. Upon successfully checking out the code, AWS CodePipeline triggers AWS CodeBuild. The AWS CodeBuild project created in step 4 is configured to use the CMK created in step 1 for cryptography operations. See here. The AWS CodeBuild role is created later in step 4. In step 5, access is granted to the AWS CodeBuild role to allow the use of the CMK for cryptography.
  5. AWS CodeBuild uses pip to install any libraries for the sample Lambda function. It also executes the aws cloudformation package command to create a Lambda function deployment package, uploads the package to the specified S3 bucket, and adds a reference to the uploaded package to the CloudFormation template. See here.
  6. Using the role created in step 3, AWS CodePipeline executes the transformed CloudFormation template (received as an output from AWS CodeBuild) in the Test account. The AWS CodePipeline role created in step 4 has permissions to assume the IAM role created in step 3, as described in step 5.
  7. The IAM role assumed by AWS CodePipeline passes the role to an IAM role that can be assumed by CloudFormation. AWS CloudFormation creates and updates the Lambda function using the code that was built and uploaded by AWS CodeBuild.

This is what the pipeline looks like using the sample code:

Conclusion

Creating multiple AWS accounts provides the highest degree of isolation and is appropriate for a number of use cases. However, keeping a centralized account to orchestrate continuous delivery and deployment using AWS CodePipeline and AWS CodeBuild eliminates the need to duplicate the delivery pipeline. You can secure the pipeline through the use of cross account IAM roles and the encryption of artifacts using AWS KMS. For more information, see Providing Access to an IAM User in Another AWS Account That You Own in the IAM User Guide.

References

AWS Big Data Blog Month in Review: April 2017

Post Syndicated from Derek Young original https://aws.amazon.com/blogs/big-data/aws-big-data-blog-month-in-review-april-2017/

Another month of big data solutions on the Big Data Blog. Please take a look at our summaries below and learn, comment, and share. Thank you for reading!

NEW POSTS

Amazon QuickSight Spring Announcement: KPI Charts, Export to CSV, AD Connector, and More! 
In this blog post, we share a number of new features and enhancements in Amazon Quicksight. You can now create key performance indicator (KPI) charts, define custom ranges when importing Microsoft Excel spreadsheets, export data to comma separated value (CSV) format, and create aggregate filters for SPICE data sets. In the Enterprise Edition, we added an additional option to connect to your on-premises Active Directory using AD Connector. 

Securely Analyze Data from Another AWS Account with EMRFS
Sometimes, data to be analyzed is spread across buckets owned by different accounts. In order to ensure data security, appropriate credentials management needs to be in place. This is especially true for large enterprises storing data in different Amazon S3 buckets for different departments. This post shows how you can use a custom credentials provider to access S3 objects that cannot be accessed by the default credentials provider of EMRFS.

Querying OpenStreetMap with Amazon Athena
This post explains how anyone can use Amazon Athena to quickly query publicly available OSM data stored in Amazon S3 (updated weekly) as an AWS Public Dataset. Imagine that you work for an NGO interested in improving knowledge of and access to health centers in Africa. You might want to know what’s already been mapped, to facilitate the production of maps of surrounding villages, and to determine where infrastructure investments are likely to be most effective.

Build a Real-time Stream Processing Pipeline with Apache Flink on AWS
This post outlines a reference architecture for a consistent, scalable, and reliable stream processing pipeline that is based on Apache Flink using Amazon EMR, Amazon Kinesis, and Amazon Elasticsearch Service. An AWSLabs GitHub repository provides the artifacts that are required to explore the reference architecture in action. Resources include a producer application that ingests sample data into an Amazon Kinesis stream and a Flink program that analyses the data in real time and sends the result to Amazon ES for visualization.

Manage Query Workloads with Query Monitoring Rules in Amazon Redshift
Amazon Redshift is a powerful, fully managed data warehouse that can offer significantly increased performance and lower cost in the cloud. However, queries which hog cluster resources (rogue queries) can affect your experience. In this post, you learn how query monitoring rules can help spot and act against such queries. This, in turn, can help you to perform smooth business operations in supporting mixed workloads to maximize cluster performance and throughput.

Amazon QuickSight Now Supports Audit Logging with AWS CloudTrail
In this post, we announce support for AWS CloudTrail in Amazon QuickSight, which allows logging of QuickSight events across an AWS account. Whether you have an enterprise setting or a small team scenario, this integration will allow QuickSight administrators to accurately answer questions such as who last changed an analysis, or who has connected to sensitive data. With CloudTrail, administrators have better governance, auditing and risk management of their QuickSight usage.

Near Zero Downtime Migration from MySQL to DynamoDB
This post introduces two methods of seamlessly migrating data from MySQL to DynamoDB, minimizing downtime and converting the MySQL key design into one more suitable for NoSQL.


Want to learn more about Big Data or Streaming Data? Check out our Big Data and Streaming data educational pages.

Leave a comment below to let us know what big data topics you’d like to see next on the AWS Big Data Blog.

Amazon Simple Queue Service Introduces Server-Side Encryption for Queues

Post Syndicated from Craig Liebendorfer original https://aws.amazon.com/blogs/security/amazon-simple-queue-service-introduces-server-side-encryption-for-queues/

SQS + SSE image

You can now use Amazon Simple Queue Service (SQS) to exchange sensitive data between applications using server-side encryption (SSE). SQS is a fully managed message queuing service for reliably communicating between distributed software components and microservices at any scale. You can use SQS to take advantage of the scale, cost, and operational benefits of a managed messaging service. The addition of SSE allows you to transmit sensitive data with the increased security of using encrypted queues.

SQS SSE uses the 256-bit Advanced Encryption Standard (AES-256 GCM algorithm) to encrypt each message body by using a unique key. AWS Key Management Service (KMS) provides encryption key management. In addition, KMS works with AWS CloudTrail to provide logs of all encryption key usage to help meet your regulatory and compliance needs.

SQS SSE is now available in the US West (Oregon) and US East (Ohio) Regions, with more AWS Regions to follow. There is no additional charge for using encrypted queues, but there is a charge to use KMS. For more information about KMS and CloudTrail pricing, see AWS Key Management Service Pricing.

To learn more, see the full post on the AWS Blog.

– Craig

Amazon QuickSight Now Supports Audit Logging with AWS CloudTrail

Post Syndicated from Craig Liebendorfer original https://aws.amazon.com/blogs/security/amazon-quicksight-now-supports-logging-with-aws-cloudtrail/

QuickSight diagram

Amazon QuickSight democratizes business intelligence, making it easier and cheaper for you to provide advanced business analytics capabilities to everyone in your organization. Amazon QuickSight also enables you to understand your business better and helps you make data-driven decisions more quickly. However, determining who has access to which data in your organization can still be an administrative challenge.

Today, we are happy to announce that Amazon QuickSight now supports AWS CloudTrail, which enables you to log Amazon QuickSight events across your account. Amazon QuickSight administrators can now quickly and accurately answer questions such as who changed an analysis last or who connected to a sensitive database. CloudTrail support in Amazon QuickSight gives your administrators better governance, auditing, and risk management of your company’s Amazon QuickSight usage.

To learn more, see the full AWS Big Data Blog post.

– Craig

Amazon QuickSight Now Supports Audit Logging with AWS CloudTrail

Post Syndicated from Jose Kunnackal original https://aws.amazon.com/blogs/big-data/amazon-quicksight-now-supports-audit-logging-with-cloudtrail/

We launched Amazon QuickSight to democratize BI. Our goal is to make it easier and cheaper to roll out advanced business analytics capabilities to everyone in an organization. Overall, this enables better understanding of business, and allows faster data-driven decisions in an organization. In the past, the ability to share data presented an administrative challenge – that of knowing who has access to what data. Solving this problem ensures compliance with policies, and also provides an opportunity for businesses to see how employees use data to drive crucial decisions.

Today, we are happy to announce support for AWS CloudTrail in Amazon QuickSight, which allows logging of QuickSight events across an AWS account. Whether you have an enterprise setting or a small team scenario, this integration will allow QuickSight administrators to accurately answer questions such as who last changed an analysis, or who has connected to sensitive data. With CloudTrail, administrators have better governance, auditing and risk management of their QuickSight usage

You can get started with CloudTrail with just a few clicks. Any AWS account that is enabled for CloudTrail will automatically see QuickSight activity included in the CloudTrail logs. When enabled, CloudTrail starts logging events including:

  • Account subscribe/unsubscribe
  • Data source create/update/delete
  • Data set create/update/delete
  • Analysis create/access/update/delete
  • Dashboard create/access/update/delete
  • SPICE capacity purchases
  • User subscription purchases

A full list of all supported events can be found in the QuickSight documentation.

With CloudTrail logging enabled, you can easily track QuickSight activities in your account, starting with the question of who signed up for the service.

Beyond sign up, the QuickSight CloudTrail integration logs details of both data access and user management actions within the account. For example, attempts to connect to an external data source (below).

Or, sharing of a dataset:

Or even a simple dashboard access (below):

Through these details, administrators can track the origins of malicious activity, better understand user activity, or simply ensure that QuickSight activity is in compliance with policy. As with all CloudTrail data, the log is hosted in an S3 bucket configured by the AWS Account administrator. QuickSight users will not have access to this bucket, unless permitted by the administrator.

Finally, what better way to understand access and usage than to visualize the data in QuickSight! Using the native Amazon Athena integration, you can follow the steps here to pull the CloudTrail data from S3 into Athena. You can choose to query your data using Athena, or to bring data into SPICE for fast visualizations with QuickSight.

Here, I chose to pull data into SPICE, using a custom SQL statement to pull in only the specific fields that I was interested in.

Once QuickSight ingests the data into SPICE, I can analyze it using a variety of visualization types and filter capabilities.

First, I created a visual to display all the events recorded in this AWS account. As an administrator, I can view account activity at a glance, quickly identifying anything out of the ordinary. For example, if there are spikes in connection attempts, as the administrator, I might want to look into the specific user attempting to connect, and then try to understand whether this is malicious activity, or simply a case of an expired password.

To add the ability to view activity by username, I added the username field as a drill-down option in the QuickSight field wells.

This immediately makes the hierarchy available in the visual, allowing me to view the users who performed a specific activity.

It can also be useful to look at usage patterns. This allows administrators to better understand user behavior. You can see when users are just consuming dashboards and analyses, when they connect to specific data sources, when they purchase SPICE capacity, and so on. This allows you to detect hotspots in user behavior for abnormal activity, or to save costs by identifying inactive user accounts.

With Athena and QuickSight, you can get rich insights from your CloudTrail data in a few minutes!

AWS CloudTrail logging for QuickSight is available for both Standard and Enterprise Editions, across all supported QuickSight regions. Standard CloudTrail pricing applies for events generated via QuickSight.

Learn more

To learn more about these capabilities and start using them in your dashboards, check out the QuickSight User Guide.

Stay engaged

If you have questions and suggestions, you can post them on the QuickSight Discussion Forum.

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