Tag Archives: Security, Identity & Compliance

Introducing new central configuration capabilities in AWS Security Hub

2023-11-27 Nicholas Jaeger

Post Syndicated from Nicholas Jaeger original https://aws.amazon.com/blogs/security/introducing-new-central-configuration-capabilities-in-aws-security-hub/

As cloud environments—and security risks associated with them—become more complex, it becomes increasingly critical to understand your cloud security posture so that you can quickly and efficiently mitigate security gaps. AWS Security Hub offers close to 300 automated controls that continuously check whether the configuration of your cloud resources aligns with the best practices identified by Amazon Web Services (AWS) security experts and with industry standards. Furthermore, you can manage your cloud security posture at scale by using a single action to enable Security Hub across your organization with the default settings, and by aggregating findings across your organization accounts and Regions to a single account and Region of your choice.

With the release of the new central configuration feature of Security Hub, the setup and management of control and policy configurations is simplified and centralized to the same account you have already been using to aggregate findings. In this blog post, we will explain the benefits of the new feature and describe how you can quickly onboard to it.

Central configuration overview

With the release of the new central configuration capabilities in Security Hub, you are now able to use your delegated administrator (DA) account (an AWS Organizations account designated to manage Security Hub throughout your organization) to centrally manage Security Hub controls and standards and to view your Security Hub configuration throughout your organization from a single place. To facilitate this functionality, central configuration allows you to set up policies that specify whether or not Security Hub should be enabled and which standards and controls should be turned on. You can then choose to associate your policies with your entire organization or with specific accounts or organizational units (OUs), with your policies applying automatically across linked Regions. Policies applied to specific OUs (or to the entire organization) are inherited by child accounts. This not only applies to existing accounts, but also to new accounts added to those OUs (or to the entire organization) after you created the policy. Furthermore, when you add a new linked Region to Security Hub, your existing policies will be applied to that Region immediately. This allows you to stop maintaining manual lists of accounts and Regions to which you’d like to apply your custom configurations; instead, you can maintain several policies for your organization, with each one being associated to a different set of accounts in your organization. As a result, by using the central configuration capabilities, you can significantly reduce the time spent on configuring Security Hub and switch your focus to remediating its findings.

After applying your policies, Security Hub also provides you with a view of your organization that shows the policy status per OU and account while also preventing drift. This means that after you set up your organization by using central configuration, account owners will not be able to deviate from your chosen settings—your policies will serve as the source of truth for your organizational configuration, and you can use them to understand how Security Hub is configured for your organization.

The use of the new central configuration feature is now the recommended approach to configuring Security Hub, and its standards and controls, across some or all AWS accounts in your AWS Organizations structure.

Prerequisites

To get started with central configuration, you need to complete three prerequisites:

Enable AWS Config in the accounts and Regions where you plan to enable Security Hub. (For more information on how to optimize AWS Config configuration for Security Hub usage, see this blog post.)
Turn on Security Hub in your AWS Organizations management account at least in one Region where you plan to use Security Hub.
Use your Organizations management account to delegate an administrator account for Security Hub.

If you are new to Security Hub, simply navigate to it in the AWS Management Console from your organization management account, and the console will walk you through setting the last two prerequisites listed here. If you already use Security Hub, these can be configured from the Settings page in Security Hub. In both cases, upon completing these three prerequisites, you can proceed with the central configuration setup from the account you set as the DA.

Recommended setup

To begin the setup, open the Security Hub console from your AWS Organizations management account or from your Security Hub delegated administrator account. In the left navigation menu, choose Configuration to open the new Configuration page, shown in Figure 1. Choose Start central configuration.

Figure 1: The new Configuration page, where you can see your current organizational configuration and start using the new capabilities

If you signed in to Security Hub using the AWS Organizations management account, you will be brought to step 1, Designate delegated administrator, where you will be able to designate a new delegated administrator or confirm your existing selection before continuing the setup. If you signed in to Security Hub using your existing delegated administrator account, you will be brought directly to step 2, Centralize organization, which is shown in Figure 2. In step 2, you are first asked to choose your home Region, which is the AWS Region you will use to create your configuration policies. By default, the current Region is selected as your home Region, unless you already use cross-Region finding aggregation — in which case, your existing aggregation Region is pre-selected as your home Region.

You are then prompted to select your linked Regions, which are the Regions you will configure by using central configuration. Regions that were already linked as part of your cross-Region aggregation settings will be pre-selected. You will also be able to add additional Regions or choose to include all AWS Regions, including future Regions. If your selection includes opt-in Regions, note that Security Hub will not be enabled in them until you enable those Regions directly.

Figure 2: The Centralize organization page

Step 3, Configure organization, is shown in Figure 3. You will see a recommendation that you use the AWS recommended Security Hub configuration policy (SHCP) across your entire organization. This includes enabling the AWS Foundational Security Best Practices (FSBP) v1.0.0 standard and enabling new and existing FSBP controls in accounts in your AWS Organizations structure. This is the recommended configuration for most customers, because the AWS FSBP have been carefully curated by AWS security experts and represent trusted security practices for customers to build on.

Alternatively, if you already have a custom configuration in Security Hub and would like to import it into the new capabilities, choose Customize my Security Hub configuration and then choose Pre-populate configuration.

Figure 3: Step 3 – creating your first policy

Step 4, Review and apply, is where you can review the policy you just created. Until you complete this step, your organization’s configuration will not be changed. This step will override previous account configurations and create and apply your new policy. After you choose Create policy and apply, you will be taken to the new Configuration page, which was previously shown in Figure 1. The user interface will now be updated to include three tabs — Organization, Policies, and Invitation account — where you can do the following:

On the Organization tab, which serves as a single pane of glass for your organization configuration in Security Hub, you can see the policy status for each account and OU and verify that your desired configuration is in effect.
On the Policies tab, you can view your policies, update them, and create new ones.
On the Invitation accounts tab, you can view and update findings for invitation accounts, which do not belong to your AWS Organizations structure. These accounts cannot be configured using the new central configuration capabilities.

Together, those tabs serve as a single pane of glass for your organization configuration in Security Hub. To that end, the organization chart you now see shows which of your accounts have already been affected by the policy you just created and which are still pending. Normally, an account will show as pending only for a few minutes after you create new policies or update existing ones. However, an account can stay in pending status for up to 24 hours. During this time, Security Hub will try to configure the account with your chosen policy settings.

If Security Hub determines that a policy cannot be successfully propagated to an account, it will show its status as failed (see Figure 4). This is most likely to happen when you missed completing the prerequisites in the account where the failure is showing. For example, if AWS Config is not yet enabled in an account, the policy will have a failed status. When you hover your pointer over the word “Failed”, Security Hub will show an error message with details about the issue. After you fix the error, you can try again to apply the policy by selecting the failed account and choosing the Re-apply policy button.

Figure 4: The Organization tab on the <strong>Configuration</strong> page shows all your organization accounts, if they are being managed by a policy, and the policy status for each account and OU” width=”780″ class=”size-full wp-image-32053″ style=”border: 1px solid #bebebe”></p>
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Figure 4: The Organization tab on the Configuration page shows all your organization accounts, if they are being managed by a policy, and the policy status for each account and OU

Flexibility in onboarding to central configuration

As mentioned earlier, central configuration makes it significantly more accessible for you to centrally manage Security Hub and its controls and standards. This feature also gives you the granularity to choose the specific accounts to which your chosen settings will be applied. Even though we recommend to use central configuration to configure all your accounts, one advantage of the feature is that you can initially create a test configuration and then apply it across your organization. This is especially useful when you have already configured Security Hub using previously available methods and you would like to check that you have successfully imported your existing configuration.

When you onboard to central configuration, accounts in the organization are self-managed by default, which means that they still maintain their previous configuration until you apply a policy to them, to one of their parent OUs, or to the entire organization. This gives you the option to create a test policy when you onboard, apply it only to a test account or OU, and check that you achieved your desired outcome before applying it to other accounts in the organization.

Configure and deploy different policies per OU

Although we recommend that you use the policy recommended by Security Hub whenever possible, every customer has a different environment and some customization might be required. Central configuration does not require you to use the recommended policy, and you can instead create your own custom policies that specify how Security Hub is used across organization accounts and Regions. You can create one configuration policy for your entire organization, or multiple policies to customize Security Hub settings in different accounts.

In addition, you might need to implement different policies per OU. For example, you might need to do that when you have a finance account or OU in which you want to use Payment Card Industry Data Security Standard (PCI DSS) v3.2.1. In this case, you can go to the Policies tab, choose Create policy, specify the configuration you’d like to have, and apply it to those specific OUs or accounts, as shown in Figure 5. Note that each policy must be complete — which means that it must contain the full configuration settings you would like to apply to the chosen set of accounts or OUs. In particular, an account cannot inherit part of its settings from a policy associated with a parent OU, and the other part from its own policy. The benefit of this requirement is that each policy serves as the source of truth for the configuration of the accounts it is applied to. For more information on this behavior or on how to create new policies, see the Security Hub documentation.

Figure 5: Creation of a new policy with the FSBP and the PCI DSS standards

You might find it necessary to exempt accounts from being centrally configured. You have the option to set an account or OU to self-managed status. Then only the account owner can configure the settings for that account. This is useful if your organization has teams that need to be able to set their own security coverage. Unless you disassociate self-managed accounts from your Security Hub organization, you will still see findings from self-managed accounts, giving you organization-wide visibility into your security posture. However, you won’t be able to view the configuration of those accounts, because they are not centrally managed.

Understand and manage where controls are applied

In addition to being able to centrally create and view your policies, you can use the control details page to define, review, and apply how policies are configured at a control level. To access the control details page, go to the left navigation menu in Security Hub, choose Controls, and then choose any individual control.

The control details page allows you to review the findings of a control in accounts where it is already enabled. Then, if you decide that these findings are not relevant to specific accounts and OUs, or if you decide that you want to use the control in additional accounts where it is not currently enabled, you can choose Configure, view the policies to which the control currently applies, and update the configuration accordingly as shown in Figure 6.

Figure 6: Configuring a control from the control details page

Organizational visibility

As you might already have noticed in the earlier screenshot of the Organization view (Figure 4), the new central configuration capability gives you a new view of the policies applied (and by extension, the controls and standards deployed) to each account and OU. If you need to customize this configuration, you can modify an existing policy or create a new policy to quickly apply to all or a subset of your accounts. At a glance, you can also see which accounts are self-managed or don’t have Security Hub turned on.

Conclusion

Security Hub central configuration helps you to seamlessly configure Security Hub and its controls and standards across your accounts and Regions so that your organization’s accounts have the level of security controls coverage that you want. AWS recommends that you use this feature when configuring, deploying, and managing controls in Security Hub across your organization’s accounts and Regions. Central configuration is now available in all commercial AWS Regions. Try it out today by visiting the new Configuration page in Security Hub from your DA. You can benefit from the Security Hub 30-day free trial even if you use central configuration, and the trial offer will be automatically applied to organization accounts in which you didn’t use Security Hub before.

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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Use IAM Identity Center APIs to audit and manage application assignments

2023-11-27 Laura Reith

Post Syndicated from Laura Reith original https://aws.amazon.com/blogs/security/use-iam-identity-center-apis-to-audit-and-manage-application-assignments/

You can now use AWS IAM Identity Center application assignment APIs to programmatically manage and audit user and group access to AWS managed applications. Previously, you had to use the IAM Identity Center console to manually assign users and groups to an application. Now, you can automate this task so that you scale more effectively as your organization grows.

In this post, we will show you how to use IAM Identity Center APIs to programmatically manage and audit user and group access to applications. The procedures that we share apply to both organization instances and account instances of IAM Identity Center.

Automate management of user and group assignment to applications

IAM Identity Center is where you create, or connect, your workforce users one time and centrally manage their access to multiple AWS accounts and applications. You configure AWS managed applications to work with IAM Identity Center directly from within the relevant application console, and then manage which users or groups need permissions to the application.

You can already use the account assignment APIs to automate multi-account access and audit access assigned to your users using IAM Identity Center permission sets. Today, we expanded this capability with the new application assignment APIs. You can use these new APIs to programmatically control application assignments and develop automated workflows for auditing them.

AWS managed applications access user and group information directly from IAM Identity Center. One example of an AWS managed application is Amazon Redshift. When you configure Amazon Redshift as an AWS managed application with IAM Identity Center, and a user from your organization accesses the database, their group memberships defined in IAM Identity Center can map to Amazon Redshift database roles that grant them specific permissions. This makes it simpler for you to manage users because you don’t have to set database-object permissions for each individual. For more information, see The benefits of Redshift integration with AWS IAM Identity Center.

After you configure the integration between IAM Identity Center and Amazon Redshift, you can automate the assignment or removal of users and groups by using the DeleteApplicationAssignment and CreateApplicationAssignment APIs, as shown in Figure 1.

Figure 1: Use the CreateApplicationAssignment API to assign users and groups to Amazon Redshift

In this section, you will learn how to use Identity Center APIs to assign a group to your Amazon Redshift application. You will also learn how to delete the group assignment.

Prerequisites

To follow along with this walkthrough, make sure that you’ve completed the following prerequisites:

Enable IAM Identity Center, and use the Identity Store to manage your identity data. If you use an external identity provider, then you should handle the user creation and deletion processes in those systems.
Configure Amazon Redshift to use IAM Identity Center as its identity source. When you configure Amazon Redshift to use IAM Identity Center as its identity source, the application requires explicit assignment by default. This means that you must explicitly assign users to the application in the Identity Center console or APIs.
Install and configure AWS Command Line Interface (AWS CLI) version 2. For this example, you will use AWS CLI v2 to call the IAM Identity Center application assignment APIs. For more information, see Installing the AWS CLI and Configuring the AWS CLI.

Step 1: Get your Identity Center instance information

The first step is to run the following command to get the Amazon Resource Name (ARN) and Identity Store ID for the instance that you’re working with:

aws sso-admin list-instances

The output should look similar to the following:

{
  "Instances": [
      {
          "InstanceArn": "arn:aws:sso:::instance/ssoins-****************",
          "IdentityStoreId": "d-**********",
          "OwnerAccountId": "************",
          "Name": "MyInstanceName",
          "CreatedDate": "2023-10-08T16:45:19.839000-04:00",
          "State": {
              "Name": "ACTIVE"
          },
          "Status": "ACTIVE"
      }
  ],
  "NextToken": <<TOKEN>>
}

Take note of the IdentityStoreId and the InstanceArn — you will use both in the following steps.

Step 2: Create user and group in your Identity Store

The next step is to create a user and group in your Identity Store.

Note: If you already have a group in your Identity Center instance, get its GroupId and then proceed to Step 3. To get your GroupId, run the following command:
aws identitystore get-group-id --identity-store-id “d-********” –alternate-identifier “GroupName” ,

Create a new user by using the IdentityStoreId that you noted in the previous step.

aws identitystore create-user --identity-store-id "d-**********" --user-name "MyUser" --emails Value="[email protected]",Type="Work",Primary=true —display-name "My User" —name FamilyName="User",GivenName="My"

The output should look similar to the following:

{
    "UserId": "********-****-****-****-************",
    "IdentityStoreId": "d--********** "
}

Create a group in your Identity Store:

aws identitystore create-group --identity-store-id d-********** --display-name engineering

In the output, make note of the GroupId — you will need it later when you create the application assignment in Step 4:

{
    "GroupId": "********-****-****-****-************",
    "IdentityStoreId": "d-**********"
}

Run the following command to add the user to the group:

aws identitystore create-group-membership --identity-store-id d-********** --group-id ********-****-****-****-************ --member-id UserId=********-****-****-****-************

The result will look similar to the following:

{
    "MembershipId": "********-****-****-****-************",
    "IdentityStoreId": "d-**********"
}

Step 3: Get your Amazon Redshift application ARN instance

The next step is to determine the application ARN. To get the ARN, run the following command.

aws sso-admin list-applications --instance-arn "arn:aws:sso:::instance/ssoins-****************"

If you have more than one application in your environment, use the filter flag to specify the application account or the application provider. To learn more about the filter option, see the ListApplications API documentation.

In this case, we have only one application: Amazon Redshift. The response should look similar to the following. Take note of the ApplicationArn — you will need it in the next step.

{

    "ApplicationArn": "arn:aws:sso:::instance/ssoins-****************/apl-***************",
    "ApplicationProviderArn": "arn:aws:sso::aws:applicationProvider/Redshift",
    "Name": "Amazon Redshift",
    "InstanceArn": "arn:aws:sso:::instance/ssoins-****************",
    "Status": "DISABLED",
    "PortalOptions": {
        "Visible": true,
        "Visibility": "ENABLED",
        "SignInOptions": {
            "Origin": "IDENTITY_CENTER"
        }
    },
    "AssignmentConfig": {
        "AssignmentRequired": true
    },
    "Description": "Amazon Redshift",
    "CreatedDate": "2023-10-09T10:48:44.496000-07:00"
}

Step 4: Add your group to the Amazon Redshift application

Now you can add your new group to the Amazon Redshift application managed by IAM Identity Center. The principal-id is the GroupId that you created in Step 2.

aws sso-admin create-application-assignment --application-arn "arn:aws:sso:::instance/ssoins-****************/apl-***************" --principal-id "********-****-****-****-************" --principal-type "GROUP"

The group now has access to Amazon Redshift, but with the default permissions in Amazon Redshift. To grant access to databases, you can create roles that control the permissions available on a set of tables or views.

To create these roles in Amazon Redshift, you need to connect to your cluster and run SQL commands. To connect to your cluster, use one of the following options:

Figure 2 shows a connection to Amazon Redshift through the query editor v2.

Figure 2: Query editor v2

By default, all users have CREATE and USAGE permissions on the PUBLIC schema of a database. To disallow users from creating objects in the PUBLIC schema of a database, use the REVOKE command to remove that permission. For more information, see Default database user permissions.

As the Amazon Redshift database administrator, you can create roles where the role name contains the identity provider namespace prefix and the group or user name. To do this, use the following syntax:

CREATE ROLE <identitycenternamespace:rolename>;

The rolename needs to match the group name in IAM Identity Center. Amazon Redshift automatically maps the IAM Identity Center group or user to the role created previously. To expand the permissions of a user, use the GRANT command.

The identityprovidernamespace is assigned when you create the integration between Amazon Redshift and IAM Identity Center. It represents your organization’s name and is added as a prefix to your IAM Identity Center managed users and roles in the Redshift database.

Your syntax should look like the following:

CREATE ROLE <AWSIdentityCenter:MyGroup>;

Step 5: Remove application assignment

If you decide that the new group no longer needs access to the Amazon Redshift application but should remain within the IAM Identity Center instance, run the following command:

aws sso-admin delete-application-assignment --application-arn "arn:aws:sso:::instance/ssoins-****************/apl-***************" --principal-id "********-****-****-****-************" --principal-type "GROUP"

Note: Removing an application assignment for a group doesn’t remove the group from your Identity Center instance.

When you remove or add user assignments, we recommend that you review the application’s documentation because you might need to take additional steps to completely onboard or offboard a given user or group. For example, when you remove a user or group assignment, you must also remove the corresponding roles in Amazon Redshift. You can do this by using the DROP ROLE command. For more information, see Managing database security.

Audit user and group access to applications

Let’s consider how you can use the new APIs to help you audit application assignments. In the preceding example, you used the AWS CLI to create and delete assignments to Amazon Redshift. Now, we will show you how to use the new ListApplicationAssignments API to list the groups that are currently assigned to your Amazon Redshift application.

aws sso-admin list-application-assignments --application-arn arn:aws:sso::****************:application/ssoins-****************/apl-****************

The output should look similar to the following — in this case, you have a single group assigned to the application.

{
    "ApplicationAssignments": [
        {
        "ApplicationArn": "arn:aws:sso::****************:application/ssoins-****************/apl-****************",
        "PrincipalId": "********-****-****-****-************",
        "PrincipalType": "GROUP"
        }
    ]
}

To see the group membership, use the PrincipalId information to query Identity Store and get information on the users assigned to the group with a combination of the ListGroupMemberships and DescribeGroupMembership APIs.

If you have several applications that IAM Identity Center manages, you can also create a script to automatically audit those applications. You can run this script periodically in an AWS Lambda function in your environment to maintain oversight of the members that are added to each application.

To get the script for this use case, see the multiple-instance-management-iam-identity-center GitHub repository. The repository includes instructions to deploy the script using Lambda within the AWS Organizations delegated administrator account. After deployment, you can invoke the Lambda function to get .csv files of every IAM Identity Center instance in your organization, the applications assigned to each instance, and the users that have access to those applications.

Conclusion

In this post, you learned how to use the IAM Identity Center application assignment APIs to assign users to Amazon Redshift and remove them from the application when they are no longer part of the organization. You also learned to list which applications are deployed in each account, and which users are assigned to each of those applications.

To learn more about IAM Identity Center, see the AWS IAM Identity Center user guide. To test the application assignment APIs, see the SSO-admin 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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Introducing IAM Access Analyzer custom policy checks

2023-11-27 Mitch Beaumont

Post Syndicated from Mitch Beaumont original https://aws.amazon.com/blogs/security/introducing-iam-access-analyzer-custom-policy-checks/

AWS Identity and Access Management (IAM) Access Analyzer was launched in late 2019. Access Analyzer guides customers toward least-privilege permissions across Amazon Web Services (AWS) by using analysis techniques, such as automated reasoning, to make it simpler for customers to set, verify, and refine IAM permissions. Today, we are excited to announce the general availability of IAM Access Analyzer custom policy checks, a new IAM Access Analyzer feature that helps customers accurately and proactively check IAM policies for critical permissions and increases in policy permissiveness.

In this post, we’ll show how you can integrate custom policy checks into builder workflows to automate the identification of overly permissive IAM policies and IAM policies that contain permissions that you decide are sensitive or critical.

What is the problem?

Although security teams are responsible for the overall security posture of the organization, developers are the ones creating the applications that require permissions. To enable developers to move fast while maintaining high levels of security, organizations look for ways to safely delegate the ability of developers to author IAM policies. Many AWS customers implement manual IAM policy reviews before deploying developer-authored policies to production environments. Customers follow this practice to try to prevent excessive or unwanted permissions finding their way into production. Depending on the volume and complexity of the policies that need to be reviewed; these reviews can be intensive and take time. The result is a slowdown in development and potential delay in deployment of applications and services. Some customers write custom tooling to remove the manual burden of policy reviews, but this can be costly to build and maintain.

How do custom policy checks solve that problem?

Custom policy checks are a new IAM Access Analyzer capability that helps security teams accurately and proactively identify critical permissions in their policies. Custom policy checks can also tell you if a new version of a policy is more permissive than the previous version. Custom policy checks use automated reasoning, a form of static analysis, to provide a higher level of security assurance in the cloud. For more information, see Formal Reasoning About the Security of Amazon Web Services.

Custom policy checks can be embedded in a continuous integration and continuous delivery (CI/CD) pipeline so that checks can be run against policies without having to deploy the policies. In addition, developers can run custom policy checks from their local development environments and get fast feedback about whether or not the policies they are authoring are in line with your organization’s security standards.

How to analyze IAM policies with custom policy checks

In this section, we provide step-by-step instructions for using custom policy checks to analyze IAM policies.

Prerequisites

To complete the examples in our walkthrough, you will need the following:

An AWS account, and an identity that has permissions to use the AWS services, and create the resources, used in the following examples. For more information, see the full sample code used in this blog post on GitHub.
An installed and configured AWS CLI. For more information, see Configure the AWS CLI.
The AWS Cloud Development Kit (AWS CDK). For installation instructions, refer to Install the AWS CDK.

Example 1: Use custom policy checks to compare two IAM policies and check that one does not grant more access than the other

In this example, you will create two IAM identity policy documents, NewPolicyDocument and ExistingPolicyDocument. You will use the new CheckNoNewAccess API to compare these two policies and check that NewPolicyDocument does not grant more access than ExistingPolicyDocument.

Step 1: Create two IAM identity policy documents

Use the following command to create ExistingPolicyDocument.

cat << EOF > existing-policy-document.json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ec2:StartInstances",
                "ec2:StopInstances"
            ],
            "Resource": "arn:aws:ec2:*:*:instance/*",
            "Condition": {
                "StringEquals": {
                    "aws:ResourceTag/Owner": "\${aws:username}"
                }
            }
        }
    ]
}
EOF

Use the following command to create NewPolicyDocument.

cat << EOF > new-policy-document.json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ec2:StartInstances",
                "ec2:StopInstances"
            ],
            "Resource": "arn:aws:ec2:*:*:instance/*"
        }
    ]
}
EOF

Notice that ExistingPolicyDocument grants access to the ec2:StartInstances and ec2:StopInstances actions if the condition key aws:ResourceTag/Owner resolves to true. In other words, the value of the tag matches the policy variable aws:username. NewPolicyDocument grants access to the same actions, but does not include a condition key.

Step 2: Check the policies by using the AWS CLI

Use the following command to call the CheckNoNewAccess API to check whether NewPolicyDocument grants more access than ExistingPolicyDocument.

aws accessanalyzer check-no-new-access \
--new-policy-document file://new-policy-document.json \
--existing-policy-document file://existing-policy-document.json \
--policy-type IDENTITY_POLICY

After a moment, you will see a response from Access Analyzer. The response will look similar to the following.

{
    "result": "FAIL",
    "message": "The modified permissions grant new access compared to your existing policy.",
    "reasons": [
        {
            "description": "New access in the statement with index: 1.",
            "statementIndex": 1
        }
    ]
}

In this example, the validation returned a result of FAIL. This is because NewPolicyDocument is missing the condition key, potentially granting any principal with this identity policy attached more access than intended or needed.

Example 2: Use custom policy checks to check that an IAM policy does not contain sensitive permissions

In this example, you will create an IAM identity-based policy that contains a set of permissions. You will use the CheckAccessNotGranted API to check that the new policy does not give permissions to disable AWS CloudTrail or delete any associated trails.

Step 1: Create a new IAM identity policy document

Use the following command to create IamPolicyDocument.

cat << EOF > iam-policy-document.json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "cloudtrail:StopLogging",
                "cloudtrail:Delete*"
            ],
            "Resource": ["*"] 
        }
    ]
}
EOF

Step 2: Check the policy by using the AWS CLI

Use the following command to call the CheckAccessNotGranted API to check if the new policy grants permission to the set of sensitive actions. In this example, you are asking Access Analyzer to check that IamPolicyDocument does not contain the actions cloudtrail:StopLogging or cloudtrail:DeleteTrail (passed as a list to the access parameter).
```
aws accessanalyzer check-access-not-granted \
--policy-document file://iam-policy-document.json \
--access actions=cloudtrail:StopLogging,cloudtrail:DeleteTrail \
--policy-type IDENTITY_POLICY
```

Because the policy that you created contains both cloudtrail:StopLogging and cloudtrail:DeleteTrail actions, Access Analyzer returns a FAIL.

{
    "result": "FAIL",
    "message": "The policy document grants access to perform one or more of the listed actions.",
    "reasons": [
        {
            "description": "One or more of the listed actions in the statement with index: 0.",
            "statementIndex": 0
        }
    ]
}

Example 3: Integrate custom policy checks into the developer workflow

Building on the previous two examples, in this example, you will automate the analysis of the IAM policies defined in an AWS CloudFormation template. Figure 1 shows the workflow that will be used. The workflow will initiate each time a pull request is created against the main branch of an AWS CodeCommit repository called my-iam-policy (the commit stage in Figure 1). The first check uses the CheckNoNewAccess API to determine if the updated policy is more permissive than a reference IAM policy. The second check uses the CheckAccessNotGranted API to automatically check for critical permissions within the policy (the validation stage in Figure 1). In both cases, if the updated policy is more permissive, or contains critical permissions, a comment with the results of the validation is posted to the pull request. This information can then be used to decide whether the pull request is merged into the main branch for deployment (the deploy stage is shown in Figure 1).

Figure 1: Diagram of the pipeline that will check policies

Step 1: Deploy the infrastructure and set up the pipeline

Use the following command to download and unzip the Cloud Development Kit (CDK) project associated with this blog post.

git clone https://github.com/aws-samples/access-analyzer-automated-policy-analysis-blog.git
cd ./access-analyzer-automated-policy-analysis-blog

Create a virtual Python environment to contain the project dependencies by using the following command.
```
python3 -m venv .venv
```
Activate the virtual environment with the following command.
```
source .venv/bin/activate
```
Install the project requirements by using the following command.
```
pip install -r requirements.txt
```
Use the following command to update the CDK CLI to the latest major version.
```
npm install -g aws-cdk@2 --force
```
Before you can deploy the CDK project, use the following command to bootstrap your AWS environment. Bootstrapping is the process of creating resources needed for deploying CDK projects. These resources include an Amazon Simple Storage Service (Amazon S3) bucket for storing files and IAM roles that grant permissions needed to perform deployments.
```
cdk bootstrap
```
Finally, use the following command to deploy the pipeline infrastructure.
```
cdk deploy --require-approval never
```
The deployment will take a few minutes to complete. Feel free to grab a coffee and check back shortly.

When the deployment completes, there will be two stack outputs listed: one with a name that contains CodeCommitRepo and another with a name that contains ConfigBucket. Make a note of the values of these outputs, because you will need them later.

The deployed pipeline is displayed in the AWS CodePipeline console and should look similar to the pipeline shown in Figure 2.

Figure 2: AWS CodePipeline and CodeBuild Management Console view

In addition to initiating when a pull request is created, the newly deployed pipeline can also be initiated when changes to the main branch of the AWS CodeCommit repository are detected. The pipeline has three stages, CheckoutSources, IAMPolicyAnalysis, and deploy. The CheckoutSource stage checks out the contents of the my-iam-policy repository when the pipeline is triggered due to a change in the main branch.

The IAMPolicyAnalysis stage, which runs after the CheckoutSource stage or when a pull request has been created against the main branch, has two actions. The first action, Check no new access, verifies that changes to the IAM policies in the CloudFormation template do not grant more access than a pre-defined reference policy. The second action, Check access not granted, verifies that those same updates do not grant access to API actions that are deemed sensitive or critical. Finally, the Deploy stage will deploy the resources defined in the CloudFormation template, if the actions in the IAMPolicyAnalysis stage are successful.

To analyze the IAM policies, the Check no new access and Check access not granted actions depend on a reference policy and a predefined list of API actions, respectively.
Use the following command to create the reference policy.
```
cd ../ 
cat << EOF > cnna-reference-policy.json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "*",
            "Resource": "*"
        },
        {
            "Effect": "Deny",
            "Action": "iam:PassRole",
            "Resource": "arn:aws:iam::*:role/my-sensitive-roles/*"
        }
    ]
}	
EOF
```
This reference policy sets out the maximum permissions for policies that you plan to validate with custom policy checks. The iam:PassRole permission is a permission that allows an IAM principal to pass an IAM role to an AWS service, like Amazon Elastic Compute Cloud (Amazon EC2) or AWS Lambda. The reference policy says that the only way that a policy is more permissive is if it allows iam:PassRole on this group of sensitive resources: arn:aws:iam::*:role/my-sensitive-roles/*”.

Why might a reference policy be useful? A reference policy helps ensure that a particular combination of actions, resources, and conditions is not allowed in your environment. Reference policies typically allow actions and resources in one statement, then deny the problematic permissions in a second statement. This means that a policy that is more permissive than the reference policy allows access to a permission that the reference policy has denied.

In this example, a developer who is authorized to create IAM roles could, intentionally or unintentionally, create an IAM role for an AWS service (like EC2 for AWS Lambda) that has permission to pass a privileged role to another service or principal, leading to an escalation of privilege.
Use the following command to create a list of sensitive actions. This list will be parsed during the build pipeline and passed to the CheckAccessNotGranted API. If the policy grants access to one or more of the sensitive actions in this list, a result of FAIL will be returned. To keep this example simple, add a single API action, as follows.
```
cat << EOF > sensitive-actions.file
dynamodb:DeleteTable
EOF
```
So that the CodeBuild projects can access the dependencies, use the following command to copy the cnna-reference-policy.file and sensitive-actions.file to an S3 bucket. Refer to the stack outputs you noted earlier and replace <ConfigBucket> with the name of the S3 bucket created in your environment.
```
aws s3 cp ./cnna-reference-policy.json s3://<ConfgBucket>/cnna-reference-policy.json
aws s3 cp ./sensitive-actions.file s3://<ConfigBucket>/sensitive-actions.file
```

Step 2: Create a new CloudFormation template that defines an IAM policy

With the pipeline deployed, the next step is to clone the repository that was created and populate it with a CloudFormation template that defines an IAM policy.

Install git-remote-codecommit by using the following command.
```
pip install git-remote-codecommit
```
For more information on installing and configuring git-remote-codecommit, see the AWS CodeCommit User Guide.
With git-remote-codecommit installed, use the following command to clone the my-iam-policy repository from AWS CodeCommit.
```
git clone codecommit://my-iam-policy && cd ./my-iam-policy
```
If you’ve configured a named profile for use with the AWS CLI, use the following command, replacing <profile> with the name of your named profile.
```
git clone codecommit://<profile>@my-iam-policy && cd ./my-iam-policy
```

Use the following command to create the CloudFormation template in the local clone of the repository.

cat << EOF > ec2-instance-role.yaml
---
AWSTemplateFormatVersion: 2010-09-09
Description: CloudFormation Template to deploy base resources for access_analyzer_blog
Resources:
  EC2Role:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: 2012-10-17
        Statement:
        - Effect: Allow
          Principal:
            Service: ec2.amazonaws.com
          Action: sts:AssumeRole
      Path: /
      Policies:
      - PolicyName: my-application-permissions
        PolicyDocument:
          Version: 2012-10-17
          Statement:
          - Effect: Allow
            Action:
              - 'ec2:RunInstances'
              - 'lambda:CreateFunction'
              - 'lambda:InvokeFunction'
              - 'dynamodb:Scan'
              - 'dynamodb:Query'
              - 'dynamodb:UpdateItem'
              - 'dynamodb:GetItem'
            Resource: '*'
          - Effect: Allow
            Action:
              - iam:PassRole 
            Resource: "arn:aws:iam::*:role/my-custom-role"
        
  EC2InstanceProfile:
    Type: AWS::IAM::InstanceProfile
    Properties:
      Path: /
      Roles:
        - !Ref EC2Role
EOF

The actions in the IAMPolicyValidation stage are run by a CodeBuild project. CodeBuild environments run arbitrary commands that are passed to the project using a buildspec file. Each project has already been configured to use an inline buildspec file.

You can inspect the buildspec file for each project by opening the project’s Build details page as shown in Figure 3.

Figure 3: AWS CodeBuild console and build details

Step 3: Run analysis on the IAM policy

The next step involves checking in the first version of the CloudFormation template to the repository and checking two things. First, that the policy does not grant more access than the reference policy. Second, that the policy does not contain any of the sensitive actions defined in the sensitive-actions.file.

To begin tracking the CloudFormation template created earlier, use the following command.
```
git add ec2-instance-role.yaml 
```

Commit the changes you have made to the repository.

git commit -m 'committing a new CFN template with IAM policy'

Finally, push these changes to the remote repository.
```
git push
```
Pushing these changes will initiate the pipeline. After a few minutes the pipeline should complete successfully. To view the status of the pipeline, do the following:
1. Navigate to https://<region>.console.aws.amazon.com/codesuite/codepipeline/pipelines (replacing <region> with your AWS Region).
2. Choose the pipeline called accessanalyzer-pipeline.
3. Scroll down to the IAMPolicyValidation stage of the pipeline.
4. For both the check no new access and check access not granted actions, choose View Logs to inspect the log output.
If you inspect the build logs for both the check no new access and check access not granted actions within the pipeline, you should see that there were no blocking or non-blocking findings, similar to what is shown in Figure 4. This indicates that the policy was validated successfully. In other words, the policy was not more permissive than the reference policy, and it did not include any of the critical permissions.

Figure 4: CodeBuild log entry confirming that the IAM policy was successfully validated

Step 4: Create a pull request to merge a new update to the CloudFormation template

In this step, you will make a change to the IAM policy in the CloudFormation template. The change deliberately makes the policy grant more access than the reference policy. The change also includes a critical permission.

Use the following command to create a new branch called add-new-permissions in the local clone of the repository.
```
git checkout -b add-new-permissions
```

Next, edit the IAM policy in ec2-instance-role.yaml to include an additional API action, dynamodb:Delete* and update the resource property of the inline policy to use an IAM role in the /my-sensitive-roles/*” path. You can copy the following example, if you’re unsure of how to do this.

---
AWSTemplateFormatVersion: 2010-09-09
Description: CloudFormation Template to deploy base resources for access_analyzer_blog
Resources:
  EC2Role:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: 2012-10-17
        Statement:
        - Effect: Allow
          Principal:
            Service: ec2.amazonaws.com
          Action: sts:AssumeRole
      Path: /
      Policies:
      - PolicyName: my-application-permissions
        PolicyDocument:
          Version: 2012-10-17
          Statement:
          - Effect: Allow
            Action:
              - 'ec2:RunInstances'
              - 'lambda:CreateFunction'
              - 'lambda:InvokeFunction'
              - 'dynamodb:Scan'
              - 'dynamodb:Query'
              - 'dynamodb:UpdateItem'
              - 'dynamodb:GetItem'
              - 'dynamodb:Delete*'
            Resource: '*'
          - Effect: Allow
            Action:
              - iam:PassRole 
            Resource: "arn:aws:iam::*:role/my-sensitive-roles/my-custom-admin-role"
        
  EC2InstanceProfile:
    Type: AWS::IAM::InstanceProfile
    Properties:
      Path: /
      Roles:
        - !Ref EC2Role

Commit the policy change and push the updated policy document to the repo by using the following commands.

git add ec2-instance-role.yaml 
git commit -m "adding new permission and allowing my ec2 instance to assume a pass sensitive IAM role"

The add-new-permissions branch is currently a local branch. Use the following command to push the branch to the remote repository. This action will not initiate the pipeline, because the pipeline only runs when changes are made to the repository’s main branch.
```
git push -u origin add-new-permissions
```
With the new branch and changes pushed to the repository, follow these steps to create a pull request:
1. Navigate to https://console.aws.amazon.com/codesuite/codecommit/repositories (don’t forget to the switch to the correct Region).
2. Choose the repository called my-iam-policy.
3. Choose the branch add-new-permissions from the drop-down list at the top of the repository screen.
  
  Figure 5: my-iam-policy repository with new branch available
4. Choose Create pull request.
5. Enter a title and description for the pull request.
6. (Optional) Scroll down to see the differences between the current version and new version of the CloudFormation template highlighted.
7. Choose Create pull request.
The creation of the pull request will Initiate the pipeline to fetch the CloudFormation template from the repository and run the check no new access and check access not granted analysis actions.
After a few minutes, choose the Activity tab for the pull request. You should see a comment from the pipeline that contains the results of the failed validation.

Figure 6: Results from the failed validation posted as a comment to the pull request

Why did the validations fail?

The updated IAM role and inline policy failed validation for two reasons. First, the reference policy said that no one should have more permissions than the reference policy does. The reference policy in this example included a deny statement for the iam:PassRole permission with a resource of /my-sensitive-role/*. The new created inline policy included an allow statement for the iam:PassRole permission with a resource of arn:aws:iam::*:role/my-sensitive-roles/my-custom-admin-role. In other words, the new policy had more permissions than the reference policy.

Second, the list of critical permissions included the dynamodb:DeleteTable permission. The inline policy included a statement that would allow the EC2 instance to perform the dynamodb:DeleteTable action.

Cleanup

Use the following command to delete the infrastructure that was provisioned as part of the examples in this blog post.

cdk destroy

Conclusion

In this post, I introduced you to two new IAM Access Analyzer APIs: CheckNoNewAccess and CheckAccessNotGranted. The main example in the post demonstrated one way in which you can use these APIs to automate security testing throughout the development lifecycle. The example did this by integrating both APIs into the developer workflow and validating the developer-authored IAM policy when the developer created a pull request to merge changes into the repository’s main branch. The automation helped the developer to get feedback about the problems with the IAM policy quickly, allowing the developer to take action in a timely way. This is often referred to as shifting security left — identifying misconfigurations early and automatically supporting an iterative, fail-fast model of continuous development and testing. Ultimately, this enables teams to make security an inherent part of a system’s design and architecture and can speed up product development workflow.

You can find the full sample code used in this blog post on GitHub.

To learn more about IAM Access Analyzer and the new custom policy checks feature, see the IAM Access Analyzer documentation.

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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Amazon EKS Pod Identity simplifies IAM permissions for applications on Amazon EKS clusters

2023-11-27 Donnie Prakoso

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/amazon-eks-pod-identity-simplifies-iam-permissions-for-applications-on-amazon-eks-clusters/

Starting today, you can use Amazon EKS Pod Identity to simplify your applications that access AWS services. This enhancement provides you with a seamless and easy to configure experience that lets you define required IAM permissions for your applications in Amazon Elastic Kubernetes Service (Amazon EKS) clusters so you can connect with AWS services outside the cluster.

Amazon EKS Pod Identity helps you solve growing challenges for managing permissions across many of your EKS clusters.

Simplifying experience with Amazon EKS Pod Identity
In 2019, we introduced IAM roles for service accounts (IRSA). IRSA lets you associate an IAM role with a Kubernetes service account. This helps you to implement the principle of least privilege by giving pods only the permissions they need. This approach prioritizes pods in IAM and helps developers configure applications with fine-grained permissions that enable the least privileged access to AWS services.

Now, with Amazon EKS Pod Identity, it’s even easier to configure and automate granting AWS permissions to Kubernetes identities. As the cluster administrator, you no longer need to switch between Amazon EKS and IAM services to authenticate your applications to all AWS resources.

The overall workflow to start using Amazon EKS Pod Identity can be summarized in a few simple steps:

Step 1: Create an IAM role with required permissions for your application and specify pods.eks.amazonaws.com as the service principal in its trust policy.
Step 2: Install Amazon EKS Pod Identity Agent add-on using the Amazon EKS console or AWS Command Line Interface (AWS CLI).
Step 3: Map the role to a service account directly in the Amazon EKS console, APIs, or AWS CLI.

Once it’s done, any new pods that use that service account will automatically be configured to receive IAM credentials.

Let’s get started
Let me show you how you can get started with EKS Pod Identity. For the demo in this post, I need to configure permission for a simple API running in my Amazon EKS cluster, which will return the list of files in my Amazon Simple Storage Service (Amazon S3) bucket.

First, I need to create an IAM role to provide the required permissions so my applications can run properly. In my case, I need to configure permissions to access my S3 bucket.

Next, on the same IAM role, I need to configure its trust policy and configure the principal to pods.eks.amazonaws.com. The following is the IAM template that I use:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "pods.eks.amazonaws.com"
            },
            "Action": [
                "sts:AssumeRole",
                "sts:TagSession"
            ]
        }
    ]
}

At this stage, my IAM role is ready, and now we need to configure the Amazon EKS Pod Identity Agent in my cluster. For this article, I’m using my existing EKS cluster. If you want to learn how to do that, visit Getting started with Amazon EKS.

Moving on, I navigate to the Amazon EKS dashboard and then select my EKS cluster.

In my EKS cluster page, I need to select the Add-ons tab and then choose Get more add-ons.

Then, I need to add the Amazon EKS Pod Identity Agent add-on.

On the next page, I can add additional configuration if needed. In this case, I leave the default configuration and choose Next.

Then, I just need to review my add-on configuration and choose Create.

After a few minutes, the Amazon EKS Pod Identity Agent add-on is active for my cluster.

Once I have Amazon EKS Pod Identity in my cluster, I need to associate the IAM role to my Kubernetes pods.

I need to navigate to the Access tab in my EKS cluster. On the Pod Identity associations section, I select Create Pod Identity association to map my IAM role to Kubernetes pods.

Here, I use the IAM role that I created in the beginning. I also need to define my Kubernetes namespace and service account. If they don’t exist yet, I can type in the name of the namespace and service account. If they already exist, I can select them from the dropdown. Then, I choose Create.

Those are all the steps I need to do to configure IAM permissions for my applications running on Amazon EKS with EKS Pod Identity. Now, I can see my IAM role is listed in Pod Identity associations.

When I test my API running on Amazon EKS, it runs as expected and returns the list of files in my S3 bucket.

curl -X https://<API-URL> -H "Accept: application/json" 

{
   "files": [
         "test-file-1.md",
         "test-file-2.md"
    ]        
}

I found that Amazon EKS Pod Identity simplifies the experience of managing IAM roles for my applications running on Amazon EKS. I can easily reuse IAM roles across multiple EKS clusters without needing to update the role trust policy each time a new cluster is created.

New AWS APIs to configure EKS Pod Identity
You also have the flexibility to configure Amazon EKS Pod Identity for your cluster using AWS CLI. Amazon EKS Pod Identity provides a new set of APIs that you can use.

For example, I can use aws eks create-addon to install the Amazon EKS Pod Identity Agent add-on into my cluster. Here’s the AWS CLI command:

$ aws eks create-addon \
--cluster-name <CLUSTER_NAME> \
--addon-name eks-pod-identity-agent \
--addon-version v1.0.0-eksbuild.1

{
    "addon": {
    "addonName": "eks-pod-identity-agent",
    "clusterName": "<CLUSTER_NAME>",
    "status": "CREATING",
    "addonVersion": "v1.0.0-eksbuild.1",
    "health": {
        "issues": []
        },
    "addonArn": "<ARN>",
    "createdAt": 1697734297.597,
    "modifiedAt": 1697734297.612,
    "tags": {}
    }
}

Another example of what you can do with AWS APIs is to map the IAM role into your Kubernetes pods.

$ aws eks create-pod-identity-association \
  --cluster-name <CLUSTER_NAME> \
  --namespace <NAMESPACE> \
  --service-account <SERVICE_ACCOUNT_NAME> \
  --role-arn <IAM_ROLE_ARN>

Things to know

Availability – Amazon EKS Pod Identity is available in all AWS Regions supported by Amazon EKS, except the AWS GovCloud (US-East), AWS GovCloud (US-West), China (Beijing, operated by Sinnet), and China (Ningxia, operated by NWCD).

Pricing – Amazon EKS Pod Identity is available at no charge.

Supported Amazon EKS cluster – Amazon EKS Pod Identity supports Kubernetes running version 1.24 and above in Amazon EKS. You can see EKS Pod Identity cluster versions for more information.

Supported AWS SDK versions – You need to update your application to use the latest AWS SDK versions. Check out AWS developer tools to find out how to install and update your AWS SDK.

Get started today and visit EKS Pod Identities documentation page to learn more about how to simplify IAM management for your applications.

Happy building!
— Donnie

How to use the BatchGetSecretsValue API to improve your client-side applications with AWS Secrets Manager

2023-11-26 Brendan Paul

Post Syndicated from Brendan Paul original https://aws.amazon.com/blogs/security/how-to-use-the-batchgetsecretsvalue-api-to-improve-your-client-side-applications-with-aws-secrets-manager/

AWS Secrets Manager is a service that helps you manage, retrieve, and rotate database credentials, application credentials, OAuth tokens, API keys, and other secrets throughout their lifecycles. You can use Secrets Manager to help remove hard-coded credentials in application source code. Storing the credentials in Secrets Manager helps avoid unintended or inadvertent access by anyone who can inspect your application’s source code, configuration, or components. You can replace hard-coded credentials with a runtime call to the Secrets Manager service to retrieve credentials dynamically when you need them.

In this blog post, we introduce a new Secrets Manager API call, BatchGetSecretValue, and walk you through how you can use it to retrieve multiple Secretes Manager secrets.

New API — BatchGetSecretValue

Previously, if you had an application that used Secrets Manager and needed to retrieve multiple secrets, you had to write custom code to first identify the list of needed secrets by making a ListSecrets call, and then call GetSecretValue on each individual secret. Now, you don’t need to run ListSecrets and loop. The new BatchGetSecretValue API reduces code complexity when retrieving secrets, reduces latency by running bulk retrievals, and reduces the risk of reaching Secrets Manager service quotas.

Security considerations

Though you can use this feature to retrieve multiple secrets in one API call, the access controls for Secrets Manager secrets remain unchanged. This means AWS Identity and Access Management (IAM) principals need the same permissions as if they were to retrieve each of the secrets individually. If secrets are retrieved using filters, principals must have both permissions for list-secrets and get-secret-value on secrets that are applicable. This helps protect secret metadata from inadvertently being exposed. Resource policies on secrets serve as another access control mechanism, and AWS principals must be explicitly granted permissions to access individual secrets if they’re accessing secrets from a different AWS account (see Cross-account access for more information). Later in this post, we provide some examples of how you can restrict permissions of this API call through an IAM policy or a resource policy.

Solution overview

In the following sections, you will configure an AWS Lambda function to use the BatchGetSecretValue API to retrieve multiple secrets at once. You also will implement attribute based access control (ABAC) for Secrets Manager secrets, and demonstrate the access control mechanisms of Secrets Manager. In following along with this example, you will incur costs for the Secrets Manager secrets that you create, and the Lambda function invocations that are made. See the Secrets Manager Pricing and Lambda Pricing pages for more details.

Prerequisites

To follow along with this walk-through, you need:

Five resources that require an application secret to interact with, such as databases or a third-party API key.
Access to an IAM principal that can:
- Create Secrets Manager secrets through the AWS Command Line Interface (AWS CLI) or AWS Management Console.
- Create an IAM role to be used as a Lambda execution role.
- Create a Lambda function.

Step 1: Create secrets

First, create multiple secrets with the same resource tag key-value pair using the AWS CLI. The resource tag will be used for ABAC. These secrets might look different depending on the resources that you decide to use in your environment. You can also manually create these secrets in the Secrets Manager console if you prefer.

Run the following commands in the AWS CLI, replacing the secret-string values with the credentials of the resources that you will be accessing:

aws secretsmanager create-secret --name MyTestSecret1 --description "My first test secret created with the CLI for resource 1." --secret-string "{\"user\":\"username\",\"password\":\"EXAMPLE-PASSWORD-1\"}" --tags "[{\"Key\":\"app\",\"Value\":\"app1\"},{\"Key\":\"environment\",\"Value\":\"production\"}]"
aws secretsmanager create-secret --name MyTestSecret2 --description "My second test secret created with the CLI for resource 2." --secret-string "{\"user\":\"username\",\"password\":\"EXAMPLE-PASSWORD-2\"}" --tags "[{\"Key\":\"app\",\"Value\":\"app1\"},{\"Key\":\"environment\",\"Value\":\"production\"}]"
aws secretsmanager create-secret --name MyTestSecret3 --description "My third test secret created with the CLI for resource 3." --secret-string "{\"user\":\"username\",\"password\":\"EXAMPLE-PASSWORD-3\"}" --tags "[{\"Key\":\"app\",\"Value\":\"app1\"},{\"Key\":\"environment\",\"Value\":\"production\"}]"
aws secretsmanager create-secret --name MyTestSecret4 --description "My fourth test secret created with the CLI for resource 4." --secret-string "{\"user\":\"username\",\"password\":\"EXAMPLE-PASSWORD-4 \"}" --tags "[{\"Key\":\"app\",\"Value\":\"app1\"},{\"Key\":\"environment\",\"Value\":\"production\"}]"
aws secretsmanager create-secret --name MyTestSecret5 --description "My fifth test secret created with the CLI for resource 5." --secret-string "{\"user\":\"username\",\"password\":\"EXAMPLE-PASSWORD-5\"}" --tags "[{\"Key\":\"app\",\"Value\":\"app1\"},{\"Key\":\"environment\",\"Value\":\"production\"}]"

Next, create a secret with a different resource tag value for the app key, but the same environment key-value pair. This will allow you to demonstrate that the BatchGetSecretValue call will fail when an IAM principal doesn’t have permissions to retrieve and list the secrets in a given filter.

Create a secret with a different tag, replacing the secret-string values with credentials of the resources that you will be accessing.

aws secretsmanager create-secret --name MyTestSecret6 --description "My test secret created with the CLI." --secret-string "{\"user\":\"username\",\"password\":\"EXAMPLE-PASSWORD-6\"}" --tags "[{\"Key\":\"app\",\"Value\":\"app2\"},{\"Key\":\"environment\",\"Value\":\"production\"}]"

Step 2: Create an execution role for your Lambda function

In this example, create a Lambda execution role that only has permissions to retrieve secrets that are tagged with the app:app1 resource tag.

Create the policy to attach to the role

Navigate to the IAM console.
Select Policies from the navigation pane.
Choose Create policy in the top right corner of the console.
In Specify Permissions, select JSON to switch to the JSON editor view.

Copy and paste the following policy into the JSON text editor.

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "Statement1",
			"Effect": "Allow",
			"Action": [
				"secretsmanager:ListSecretVersionIds",
				"secretsmanager:GetSecretValue",
				"secretsmanager:GetResourcePolicy",
				"secretsmanager:DescribeSecret"
			],
			"Resource": [
				"*"
			],
			"Condition": {
				"StringNotEquals": {
					"aws:ResourceTag/app": [
						"${aws:PrincipalTag/app}"
					]
				}
			}
		},
		{
			"Sid": "Statement2",
			"Effect": "Allow",
			"Action": [
				"secretsmanager:ListSecrets"
			],
			"Resource": ["*"]
		}
	]
}

Choose Next.
Enter LambdaABACPolicy for the name.
Choose Create policy.

Create the IAM role and attach the policy

Select Roles from the navigation pane.
Choose Create role.
Under Select Trusted Identity, leave AWS Service selected.
Select the dropdown menu under Service or use case and select Lambda.
Choose Next.
Select the checkbox next to the LambdaABACPolicy policy you just created and choose Next.
Enter a name for the role.
Select Add tags and enter app:app1 as the key value pair for a tag on the role.
Choose Create Role.

Step 3: Create a Lambda function to access secrets

Navigate to the Lambda console.
Choose Create Function.
Enter a name for your function.
Select the Python 3.10 runtime.
Select change default execution role and attach the execution role you just created.
Choose Create Function.

Figure 1: create a Lambda function to access secrets

In the Code tab, copy and paste the following code:

import json
import boto3
from botocore.exceptions import ClientError
import urllib.request
import json

session = boto3.session.Session()
# Create a Secrets Manager client
client = session.client(
        service_name='secretsmanager'
    )
    

def lambda_handler(event, context):

     application_secrets = client.batch_get_secret_value(Filters =[
        {
        'Key':'tag-key',
        'Values':[event["TagKey"]]
        },
        {
        'Key':'tag-value',
        'Values':[event["TagValue"]]
        }
        ])


    ### RESOURCE 1 CONNECTION ###
    try:
        print("TESTING CONNECTION TO RESOURCE 1")
        resource_1_secret = application_secrets["SecretValues"][0]
        ## IMPLEMENT RESOURCE CONNECTION HERE

        print("SUCCESFULLY CONNECTED TO RESOURCE 1")
    
    except Exception as e:
        print("Failed to connect to resource 1")
        return e

    ### RESOURCE 2 CONNECTION ###
    try:
        print("TESTING CONNECTION TO RESOURCE 2")
        resource_2_secret = application_secrets["SecretValues"][1]
        ## IMPLEMENT RESOURCE CONNECTION HERE
        
        print("SUCCESFULLY CONNECTED TO RESOURCE 2")
    
    except Exception as e:
        print("Failed to connect to resource 2",)
        return e

    
    ### RESOURCE 3 CONNECTION ###
    try:
        print("TESTING CONNECTION TO RESOURCE 3")
        resource_3_secret = application_secrets["SecretValues"][2]
        ## IMPLEMENT RESOURCE CONNECTION HERE
        
        print("SUCCESFULLY CONNECTED TO DB 3")
        
    except Exception as e:
        print("Failed to connect to resource 3")
        return e 

    ### RESOURCE 4 CONNECTION ###
    try:
        print("TESTING CONNECTION TO RESOURCE 4")
        resource_4_secret = application_secrets["SecretValues"][3]
        ## IMPLEMENT RESOURCE CONNECTION HERE
        
        print("SUCCESFULLY CONNECTED TO RESOURCE 4")
        
    except Exception as e:
        print("Failed to connect to resource 4")
        return e

    ### RESOURCE 5 CONNECTION ###
    try:
        print("TESTING ACCESS TO RESOURCE 5")
        resource_5_secret = application_secrets["SecretValues"][4]
        ## IMPLEMENT RESOURCE CONNECTION HERE
        
        print("SUCCESFULLY CONNECTED TO RESOURCE 5")
        
    except Exception as e:
        print("Failed to connect to resource 5")
        return e
    
    return {
        'statusCode': 200,
        'body': json.dumps('Successfully Completed all Connections!')
    }

You need to configure connections to the resources that you’re using for this example. The code in this example doesn’t create database or resource connections to prioritize flexibility for readers. Add code to connect to your resources after the “## IMPLEMENT RESOURCE CONNECTION HERE” comments.
Choose Deploy.

Step 4: Configure the test event to initiate your Lambda function

Above the code source, choose Test and then Configure test event.
In the Event JSON, replace the JSON with the following:
```
{
"TagKey": "app",
“TagValue”:”app1”
}
```
Enter a Name for your event.
Choose Save.

Step 5: Invoke the Lambda function

Invoke the Lambda by choosing Test.

Step 6: Review the function output

Review the response and function logs to see the new feature in action. Your function logs should show successful connections to the five resources that you specified earlier, as shown in Figure 2.

Figure 2: Review the function output

Step 7: Test a different input to validate IAM controls

In the Event JSON window, replace the JSON with the following:

{
  "TagKey": "environment",
“TagValue”:”production”
}

You should now see an error message from Secrets Manager in the logs similar to the following:

User: arn:aws:iam::123456789012:user/JohnDoe is not authorized to perform: 
secretsmanager:GetSecretValue because no resource-based policy allows the secretsmanager:GetSecretValue action

As you can see, you were able to retrieve the appropriate secrets based on the resource tag. You will also note that when the Lambda function tried to retrieve secrets for a resource tag that it didn’t have access to, Secrets Manager denied the request.

How to restrict use of BatchGetSecretValue for certain IAM principals

When dealing with sensitive resources such as secrets, it’s recommended that you adhere to the principle of least privilege. Service control policies, IAM policies, and resource policies can help you do this. Below, we discuss three policies that illustrate this:

Policy 1: IAM ABAC policy for Secrets Manager

This policy denies requests to get a secret if the principal doesn’t share the same project tag as the secret that the principal is trying to retrieve. Note that the effectiveness of this policy is dependent on correctly applied resource tags and principal tags. If you want to take a deeper dive into ABAC with Secrets Manager, see Scale your authorization needs for Secrets Manager using ABAC with IAM Identity Center.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "Statement1",
      "Effect": "Deny",
      "Action": [
        "secretsmanager:GetSecretValue",
	“secretsmanager:BatchGetSecretValue”
      ],
      "Resource": [
        "*"
      ],
      "Condition": {
        "StringNotEquals": {
          "aws:ResourceTag/project": [
            "${aws:PrincipalTag/project}"
          ]
        }
      }
    }
  ]
}

Policy 2: Deny BatchGetSecretValue calls unless from a privileged role

This policy example denies the ability to use the BatchGetSecretValue unless it’s run by a privileged workload role.

"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "Statement1",
			"Effect": "Deny",
			"Action": [
				"secretsmanager:BatchGetSecretValue",
			],
			"Resource": [
				"arn:aws:secretsmanager:us-west-2:12345678910:secret:testsecret"
			],
			"Condition": {
				"StringNotLike": {
					"aws:PrincipalArn": [
						"arn:aws:iam::123456789011:role/prod-workload-role"
					]
				}
			}
		}]
}

Policy 3: Restrict actions to specified principals

Finally, let’s take a look at an example resource policy from our data perimeters policy examples. This resource policy restricts Secrets Manager actions to the principals that are in the organization that this secret is a part of, except for AWS service accounts.

{
    "Version": "2012-10-17",
    "Statement": 
	[
        {
            "Sid": "EnforceIdentityPerimeter",
            "Effect": "Deny",
            "Principal": 
			{
                "AWS": "*"
            },
            "Action": "secretsmanager:*",
            "Resource": "*",
            "Condition": 
			{
                "StringNotEqualsIfExists": 
				{
                    "aws:PrincipalOrgID": "<my-org-id>"
                },
                "BoolIfExists": 
				{
                    "aws:PrincipalIsAWSService": "false"
                }
            }
        },
     ]
}

Conclusion

In this blog post, we introduced the BatchGetSecretValue API, which you can use to improve operational excellence, performance efficiency, and reduce costs when using Secrets Manager. We looked at how you can use the API call in a Lambda function to retrieve multiple secrets that have the same resource tag and showed an example of an IAM policy to restrict access to this API.

To learn more about Secrets Manager, see the AWS Secrets Manager documentation or the AWS Security Blog.

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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How to use the PassRole permission with IAM roles

2023-11-25 Liam Wadman

Post Syndicated from Liam Wadman original https://aws.amazon.com/blogs/security/how-to-use-the-passrole-permission-with-iam-roles/

iam:PassRole is an AWS Identity and Access Management (IAM) permission that allows an IAM principal to delegate or pass permissions to an AWS service by configuring a resource such as an Amazon Elastic Compute Cloud (Amazon EC2) instance or AWS Lambda function with an IAM role. The service then uses that role to interact with other AWS resources in your accounts. Typically, workloads, applications, or services run with different permissions than the developer who creates them, and iam:PassRole is the mechanism in AWS to specify which IAM roles can be passed to AWS services, and by whom.

In this blog post, we’ll dive deep into iam:PassRole, explain how it works and what’s required to use it, and cover some best practices for how to use it effectively.

A typical example of using iam:PassRole is a developer passing a role’s Amazon Resource Name (ARN) as a parameter in the Lambda CreateFunction API call. After the developer makes the call, the service verifies whether the developer is authorized to do so, as seen in Figure 1.

Figure 1: Developer passing a role to a Lambda function during creation

The following command shows the parameters the developer needs to pass during the CreateFunction API call. Notice that the role ARN is a parameter, but there is no passrole parameter.

aws lambda create-function 
    --function-name my-function 
    --runtime nodejs14.x 
    --zip-file fileb://my-function.zip 
    --handler my-function.handler 
    --role arn:aws:iam::123456789012:role/service-role/MyTestFunction-role-tges6bf4

The API call will create the Lambda function only if the developer has the iam:PassRole permission as well as the CreateFunction API permissions. If the developer is lacking either of these, the request will be denied.

Now that the permissions have been checked and the Function resource has been created, the Lambda service principal will assume the role you passed whenever your function is invoked and use the role to make requests to other AWS services in your account.

Understanding IAM PassRole

When we say that iam:PassRole is a permission, we mean specifically that it is not an API call; it is an IAM action that can be specified within an IAM policy. The iam:PassRole permission is checked whenever a resource is created with an IAM service role or is updated with a new IAM service role.

Here is an example IAM policy that allows a principal to pass a role named lambda_role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "iam:PassRole",
      "Resource": [
        "arn:aws:iam::111122223333:role/lambda_role"
      ]
    }
  ]
}

The roles that can be passed are specified in the Resource element of the IAM policy. It is possible to list multiple IAM roles, and it is possible to use a wildcard (*) to match roles that begins with the pattern you specify. Use a wildcard as the last characters only when you’re matching a role pattern, to help prevent over-entitlement.

Note: We recommend that you avoid using resource ”*” with the iam:PassRole action in most cases, because this could grant someone the permission to pass any role, opening the possibility of unintended privilege escalation.

The iam:PassRole action can only grant permissions when used in an identity-based policy attached to an IAM role or user, and it is governed by all relevant AWS policy types, such as service control policies (SCPs) and VPC endpoint policies.

When a principal attempts to pass a role to an AWS service, there are three prerequisites that must be met to allow the service to use that role:

The principal that attempts to pass the role must have the iam:PassRole permission in an identity-based policy with the role desired to be passed in the Resource field, all IAM conditions met, and no implicit or explicit denies in other policies such as SCPs, VPC endpoint policies, session policies, or permissions boundaries.
The role that is being passed is configured via the trust policy to trust the service principal of the service you’re trying to pass it to. For example, the role that you pass to Amazon EC2 has to trust the Amazon EC2 service principal, ec2.amazonaws.com.
To learn more about role trust policies, see this blog post. In certain scenarios, the resource may end up being created or modified even if a passed IAM role doesn’t trust the required service principal, but the AWS service won’t be able to use the role to perform actions.
The role being passed and the principal passing the role must both be in the same AWS account.

Best practices for using iam:PassRole

In this section, you will learn strategies to use when working with iam:PassRole within your AWS account.

Place iam:PassRole in its own policy statements

As we demonstrated earlier, the iam:PassRole policy action takes an IAM role for a resource. If you specify a wildcard as a resource in a policy granting iam:PassRole permission, it means that the principals to whom this policy applies will be able to pass any role in that account, allowing them to potentially escalate their privilege beyond what you intended.

To be able to specify the Resource value and be more granular in comparison to other permissions you might be granting in the same policy, we recommend that you keep the iam:PassRole action in its own policy statement, as indicated by the following example.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "iam:PassRole",
      "Resource": [
        "arn:aws:iam::111122223333:role/lambda_role"
      ]
    },
    {
      "Effect": "Allow",
      "Action": "cloudwatch:GetMetricData",
      "Resource": [
        "*"
      ]
    }
  ]
}

Use IAM paths or naming conventions to organize IAM roles within your AWS accounts

You can use IAM paths or a naming convention to grant a principal access to pass IAM roles using wildcards (*) in a portion of the role ARN. This reduces the need to update IAM policies whenever new roles are created.

In your AWS account, you might have IAM roles that are used for different reasons, for example roles that are used for your applications, and roles that are used by your security team. In most circumstances, you would not want your developers to associate a security team’s role to the resources they are creating, but you still want to allow them to create and pass business application roles.

You may want to give developers the ability to create roles for their applications, as long as they are safely governed. You can do this by verifying that those roles have permissions boundaries attached to them, and that they are created in a specific IAM role path. You can then allow developers to pass only the roles in that path. To learn more about using permissions boundaries, see our Example Permissions Boundaries GitHub repo.

In the following example policy, access is granted to pass only the roles that are in the /application_role/ path.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "iam:PassRole",
      "Resource": [
        "arn:aws:iam::111122223333:role/application_role/*"
      ]
    }
  ]
}

Protect specific IAM paths with an SCP

You can also protect specific IAM paths by using an SCP.

In the following example, the SCP prevents your principals from passing a role unless they have a tag of “team” with a value of “security” when the role they are trying to pass is in the IAM path /security_app_roles/.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Deny",
      "Action": "iam:PassRole",
      "Resource": "arn:aws:iam::*:role/security_app_roles/*",
      "Condition": {
        "StringNotEquals": {
          "aws:PrincipalTag/team": "security"
        }
      }
    }
  ]
}

Similarly, you can craft a policy to only allow a specific naming convention or IAM path to pass a role in a specific path. For example, the following SCP shows how to prevent a role outside of the IAM path security_response_team from passing a role in the IAM path security_app_roles.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Deny",
      "Action": "iam:PassRole",
      "Resource": "arn:aws:iam::*:role/security_app_roles/*",
      "Condition": {
        "ArnNotLike": {
          "aws:PrincipalARN": "arn:aws:iam::*:role/security_response_team/*"
        }
      }
    }
  ]
}

Using variables and tags with iam:PassRole

iam:PassRole does not support using the iam:ResourceTag or aws:ResourceTag condition keys to specify which roles can be passed. However, the IAM policy language supports using variables as part of the Resource element in an IAM policy.

The following IAM policy example uses the aws:PrincipalTag condition key as a variable in the Resource element. That allows this policy to construct the IAM path based on the values of the caller’s IAM tags or Session tags.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "iam:PassRole",
      "Resource": [
"arn:aws:iam::111122223333:role/${aws:PrincipalTag/AllowedRolePath}/*"
      ]
    }
  ]
}

If there was no value set for the AllowedRolePath tag, the resource would not match any role ARN, and no iam:PassRole permissions would be granted.

Pass different IAM roles for different use cases, and for each AWS service

As a best practice, use a single IAM role for each use case, and avoid situations where the same role is used by multiple AWS services.

We recommend that you also use different IAM roles for different workloads in your AWS accounts, even if those workloads are built on the same AWS service. This will allow you to grant only the permissions necessary to your workloads and make it possible to adhere to the principle of least privilege.

Using iam:PassRole condition keys

The iam:PassRole action has two available condition keys, iam:PassedToService and iam:AssociatedResourceArn.

iam:PassedToService allows you to specify what service a role may be passed to. iam:AssociatedResourceArn allows you to specify what resource ARNs a role may be associated with.

As mentioned previously, we typically recommend that customers use an IAM role with only one AWS service wherever possible. This is best accomplished by listing a single AWS service in a role’s trust policy, reducing the need to use the iam:PassedToService condition key in the calling principal’s identity-based policy. In circumstances where you have an IAM role that can be assumed by more than one AWS service, you can use iam:PassedToService to specify which service the role can be passed to. For example, the following policy allows ExampleRole to be passed only to the Amazon EC2 service.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "iam:PassRole",
      "Resource": "arn:aws:iam::*:role/ExampleRole",
      "Condition": {
        "StringEquals": {
          "iam:PassedToService": "ec2.amazonaws.com"
        }
      }
    }
  ]
}

When you use iam:AssociatedResourceArn, it’s important to understand that ARN formats typically do not change, but each AWS resource will have a unique ARN. Some AWS resources have non-predictable components, such as EC2 instance IDs in their ARN. This means that when you’re using iam:AssociatedResourceArn, if an AWS resource is ever deleted and a new resource created, you might need to modify the IAM policy with a new resource ARN to allow a role to be associated with it.

Most organizations prefer to limit who can delete and modify resources in their AWS accounts, rather than limit what resource a role can be associated with. An example of this would be limiting which principals can modify a Lambda function, rather than limiting which function a role can be associated with, because in order to pass a role to Lambda, the principals would need permissions to update the function itself.

Using iam:PassRole with service-linked roles

If you’re dealing with a service that uses service-linked roles (SLRs), most of the time you don’t need the iam:PassRole permission. This is because in most cases such services will create and manage the SLR on your behalf, so that you don’t pass a role as part of a service configuration, and therefore, the iam:PassRole permission check is not performed.

Some AWS services allow you to create multiple SLRs and pass them when you create or modify resources by using those services. In this case, you need the iam:PassRole permission on service-linked roles, just the same as you do with a service role.

For example, Amazon EC2 Auto Scaling allows you to create multiple SLRs with specific suffixes and then pass a role ARN in the request as part of the ec2:CreateAutoScalingGroup API action. For the Auto Scaling group to be successfully created, you need permissions to perform both the ec2:CreateAutoScalingGroup and iam:PassRole actions.

SLRs are created in the /aws-service-role/ path. To help confirm that principals in your AWS account are only passing service-linked roles that they are allowed to pass, we recommend using suffixes and IAM policies to separate SLRs owned by different teams.

For example, the following policy allows only SLRs with the _BlueTeamSuffix to be passed.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "iam:PassRole",
      "Resource": [
        "arn:aws:iam::*:role/aws-service-role/*_BlueTeamSuffix"
      ]
    }
  ]
}

You could attach this policy to the role used by the blue team to allow them to pass SLRs they’ve created for their use case and that have their specific suffix.

AWS CloudTrail logging

Because iam:PassRole is not an API call, there is no entry in AWS CloudTrail for it. To identify what role was passed to an AWS service, you must check the CloudTrail trail for events that created or modified the relevant AWS service’s resource.

In Figure 2, you can see the CloudTrail log created after a developer used the Lambda CreateFunction API call with the role ARN noted in the role field.

Figure 2: CloudTrail log of a CreateFunction API call

PassRole and VPC endpoints

Earlier, we mentioned that iam:PassRole is subject to VPC endpoint policies. If a request that requires the iam:PassRole permission is made over a VPC endpoint with a custom VPC endpoint policy configured, iam:PassRole should be allowed through the Action element of that VPC endpoint policy, or the request will be denied.

Conclusion

In this post, you learned about iam:PassRole, how you use it to interact with AWS services and resources, and the three prerequisites to successfully pass a role to a service. You now also know best practices for using iam:PassRole in your AWS accounts. To learn more, see the documentation on granting a user permissions to pass a role to an AWS service.

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 Security, Identity, & Compliance re:Post or contact AWS Support.

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Upcoming improvements to your AWS sign-in experience

2023-11-24 Khaled Zaky

Post Syndicated from Khaled Zaky original https://aws.amazon.com/blogs/security/upcoming-improvements-to-your-aws-sign-in-experience/

Starting in mid-2024, Amazon Web Services (AWS) will introduce a series of UI improvements to the AWS sign-in pages. Our primary focus is to revamp the UI, especially the root and AWS Identity and Access Management (IAM) user sign-in page and switch role page. With these design updates, we aim to facilitate smoother transitions and provide clearer access to essential sign-in features. In this blog post, we provide an overview of the upcoming changes.

Redesigned root and IAM user sign-in page

When you visit the updated sign-in page for the root and IAM users, you’ll experience a refreshed interface. We’ve provided clearer form labels, more detailed descriptions, and improved tooltip guidance to distinguish between user types. To address customer feedback, we’ve enhanced compatibility with password managers, offering a smoother auto-fill, and refined the layout for more intuitive navigation.

Note: Although you don’t need to take specific actions to benefit from these updates, if your setup depends on the current UI for automated tasks, you might notice some changes. For the most reliable and stable experience, use the AWS supported options to grant programmatic access to your users. For more information, review the programmatic access options in the documentation.

Figure 1: Root and IAM user sign-in page

Additionally, to enhance the resilience of IAM user sign-ins, we will introduce improvements that allow the use of Regional endpoints for direct AWS Management Console sign-in within specific AWS Regions. This enhancement will be available ahead of the scheduled UI refresh. For example, the Regional endpoint for the console in the US West (Oregon) Region looks like this: https://us-west-2.console.aws.amazon.com. For a full list of the console Regional endpoints, see AWS Management Console endpoints and quotas.

To provide console access to people, we recommend that you use AWS IAM Identity Center. For more information, see the IAM Identity Center User Guide. However, when you enable programmatic access, you should create IAM users only if necessary for access keys or service-specific credentials. We recommend that you grant programmatic access through IAM roles or IAM Roles Anywhere.

Figure 2: IAM sign-in page

Refreshed switch role page

To further improve the user experience, we are updating the switch role page to match the look and feel of other AWS pages, providing a consistent navigation experience.

Figure 3: Switch role page

Conclusion

We’re excited to introduce these improvements to the AWS sign-in experience, set to launch in mid-2024. We encourage users who rely on the current UI for automated tasks to familiarize themselves with the changes. By making sure that your systems and process are aligned with the new interface, you can help maintain a smooth transition.

If you have questions or feedback, start a new thread in IAM re:Post or reach out to AWS Support.

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AWS Security Profile: Chris Betz, CISO of AWS

2023-11-21 Chris Betz

Post Syndicated from Chris Betz original https://aws.amazon.com/blogs/security/aws-security-profile-chris-betz-ciso-of-aws/

In the AWS Security Profile series, we feature the people who work in Amazon Web Services (AWS) Security and help keep our customers safe and secure. This interview is with Chris Betz, Chief Information Security Officer (CISO), who began his role as CISO of AWS in August of 2023.

How did you get started in security? What prompted you to pursue this field?

I’ve always had a passion for technology, and for keeping people out of harm’s way. When I found computer science and security in the Air Force, this world opened up to me that let me help others, be a part of building amazing solutions, and engage my competitive spirit. Security has the challenges of the ultimate chess game, though with real and impactful consequences. I want to build reliable, massively scaled systems that protect people from malicious actors. This is really hard to do and a huge challenge I undertake every day. It’s an amazing team effort that brings together the smartest folks that I know, competing with threat actors.

What are you most excited about in your new role?

One of the most exciting things about my role is that I get to work with some of the smartest people in the field of security, people who inspire, challenge, and teach me something new every day. It’s exhilarating to work together to make a significant difference in the lives of people all around the world, who trust us at AWS to keep their information secure. Security is constantly changing, we get to learn, adapt, and get better every single day. I get to spend my time helping to build a team and culture that customers can depend on, and I’m constantly impressed and amazed at the caliber of the folks I get work with here.

How does being a former customer influence your role as AWS CISO?

I was previously the CISO at Capital One and was an AWS customer. As a former customer, I know exactly what it’s like to be a customer who relies on a partner for significant parts of their security. There needs to be a lot of trust, a lot of partnership across the shared responsibility model, and consistent focus on what’s being done to keep sensitive data secure. Every moment that I’m here at AWS, I’m reminded about things from the customer perspective and how I can minimize complexity, and help customers leverage the “super powers” that the cloud provides for CISOs who need to defend the breadth of their digital estate. I know how important it is to earn and keep customer trust, just like the trust I needed when I was in their shoes. This mindset influences me to learn as much as I can, never be satisfied with ”good enough,” and grab every opportunity I can to meet and talk with customers about their security.

What’s been the most dramatic change you’ve seen in the security industry recently?

This is pretty easy to answer: artificial intelligence (AI). This is a really exciting time. AI is dominating the news and is on the mind of every security professional, everywhere. We’re witnessing something very big happening, much like when the internet came into existence and we saw how the world dramatically changed because of it. Every single sector was impacted, and AI has the same potential. Many customers use AWS machine learning (ML) and AI services to help improve signal-to-noise ratio, take over common tasks to free up valuable time to dig deeper into complex cases, and analyze massive amounts of threat intelligence to determine the right action in less time. The combination of Data + Compute power + AI is a huge advantage for cloud companies.

AI and ML have been a focus for Amazon for more than 25 years, and we get to build on an amazing foundation. And it’s exciting to take advantage of and adapt to the recent big changes and the impact this is having on the world. At AWS, we’re focused on choice and broadening access to generative AI and foundation models at every layer of the ML stack, including infrastructure (chips), developer tools, and AI services. What a great time to be in security!

What’s the most challenging part of being a CISO?

Maintaining a culture of security involves each person, each team, and each leader. That’s easy to say, but the challenge is making it tangible—making sure that each person sees that, even though their title doesn’t have “security” in it, they are still an integral part of security. We often say, “If you have access, you have responsibility.” We work hard to limit that access. And CISOs must constantly work to build and maintain a culture of security and help every single person who has access to data understand that security is an important part of their job.

What’s your short- and long-term vision for AWS Security?

Customers trust AWS to protect their data so they can innovate and grow quickly, so in that sense, our vision is for security to be a growth lever for our customers, not added friction. Cybersecurity is key to unlocking innovation, so managing risk and aligning the security posture of AWS with our business objectives will continue for the immediate future and long term. For our customers, my vision is to continue helping them understand that investing in security helps them move faster and take the right risks—the kind of risks they need to remain competitive and innovative. When customers view security as a business accelerator, they achieve new technical capabilities and operational excellence. Strong security is the ultimate business enabler.

If you could give one piece of advice to all CISOs, what would it be?

Nail Zero Trust. Zero Trust is the path to the strongest, most effective security, and getting back to the core concepts is important. While Zero Trust is a different journey for every organization, it’s a natural evolution of cybersecurity and defense in depth in particular. No matter what’s driving organizations toward Zero Trust—policy considerations or the growing patchwork of data protection and privacy regulations—Zero Trust meaningfully improves security outcomes through an iterative process. When companies get this right, they can quickly identify and investigate threats and take action to contain or disrupt unwanted activity.

What are you most proud of in your career?

I’m proud to have worked—and still be working with—such talented, capable, and intelligent security professionals who care deeply about security and are passionate about making the world a safer place. Being among the world’s top security experts really makes me grateful and humble for all the amazing opportunities I’ve had to work alongside them, working together to solve problems and being part of creating a legacy to make security better.

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How to use multiple instances of AWS IAM Identity Center

2023-11-20 Laura Reith

Post Syndicated from Laura Reith original https://aws.amazon.com/blogs/security/how-to-use-multiple-instances-of-aws-iam-identity-center/

Recently, AWS launched a new feature that allows deployment of account instances of AWS IAM Identity Center . With this launch, you can now have two types of IAM Identity Center instances: organization instances and account instances. An organization instance is the IAM Identity Center instance that’s enabled in the management account of your organization created with AWS Organizations. This instance is used to manage access to AWS accounts and applications across your entire organization. Organization instances are the best practice when deploying IAM Identity Center. Many customers have requested a way to enable AWS applications using test or sandbox identities. The new account instances are intended to support sand-boxed deployments of AWS managed applications such as Amazon CodeCatalyst and are only usable from within the account and AWS Region in which they were created. They can exist in a standalone account or in a member account within AWS Organizations.

In this blog post, we show you when to use each instance type, how to control the deployment of account instances, and how you can monitor, manage, and audit these instances at scale using the enhanced IAM Identity Center APIs.

IAM Identity Center instance types

IAM Identity Center now offers two deployment types, the traditional organization instance and an account instance, shown in Figure 1. In this section, we show you the differences between the two.

Figure 1: IAM Identity Center instance types

Organization instance of IAM Identity Center

An organization instance of IAM Identity Center is the fully featured version that’s available with AWS Organizations. This type of instance helps you securely create or connect your workforce identities and manage their access centrally across AWS accounts and applications in your organization. The recommended use of an organization instance of Identity Center is for workforce authentication and authorization on AWS for organizations of any size and type.

Using the organization instance of IAM Identity Center, your identity center administrator can create and manage user identities in the Identity Center directory, or connect your existing identity source, including Microsoft Active Directory, Okta, Ping Identity, JumpCloud, Google Workspace, and Azure Active Directory (Entra ID). There is only one organization instance of IAM Identity Center at the organization level. If you have enabled IAM Identity Center before November 15, 2023, you have an organization instance.

Account instances of IAM Identity Center

Account instances of IAM Identity Center provide a subset of the features of the organization instance. Specifically, account instances support user and group assignments initially only to Amazon CodeCatalyst. They are bound to a single AWS account, and you can deploy them in either member accounts of an organization or in standalone AWS accounts. You can only deploy one account instance per AWS account regardless of Region.

You can use account instances of IAM Identity Center to provide access to supported Identity Center enabled application if the application is in the same account and Region.

Account instances of Identity Center don’t support permission sets or assignments to customer managed applications. If you’ve enabled Identity Center before November 15, 2023 then you must enable account instance creation from your management account. To learn more see Enable account instances in the AWS Management Console documentation. If you haven’t yet enabled Identity Center, then account instances are now available to you.

When should I use account instances of IAM Identity Center?

Account instances are intended for use in specific situations where organization instances are unavailable or impractical, including:

You want to run a temporary trial of a supported AWS managed application to determine if it suits your business needs. See Additional Considerations.
You are unable to deploy IAM Identity Center across your organization, but still want to experiment with one or more AWS managed applications. See Additional Considerations.
You have an organization instance of IAM Identity Center, but you want to deploy a supported AWS managed application to an isolated set of users that are distinct from those in your organization instance.

Additional considerations

When working with multiple instances of IAM Identity Center, you want to keep a number of things in mind:

Each instance of IAM Identity Center is separate and distinct from other Identity Center instances. That is, users and assignments are managed separately in each instance without a means to keep them in sync.
Migration between instances isn’t possible. This means that migrating an application between instances requires setting up that application from scratch in the new instance.
Account instances have the same considerations when changing your identity source as an organization instance. In general, you want to set up with the right identity source before adding assignments.
Automating assigning users to applications through the IAM Identity Center public APIs also requires using the applications APIs to ensure that those users and groups have the right permissions within the application. For example, if you assign groups to CodeCatalyst using Identity Center, you still have to assign the groups to the CodeCatalyst space from the Amazon CodeCatalyst page in the AWS Management Console. See the Setting up a space that supports identity federation documentation.
By default, account instances require newly added users to register a multi-factor authentication (MFA) device when they first sign in. This can be altered in the AWS Management Console for Identity Center for a specific instance.

Controlling IAM Identity Center instance deployments

If you’ve enabled IAM Identity Center prior to November 15, 2023 then account instance creation is off by default. If you want to allow account instance creation, you must enable this feature from the Identity Center console in your organization’s management account. This includes scenarios where you’re using IAM Identity Center centrally and want to allow deployment and management of account instances. See Enable account instances in the AWS Management Console documentation.

If you enable IAM Identity Center after November 15, 2023 or if you haven’t enabled Identity Center at all, you can control the creation of account instances of Identity Center through a service control policy (SCP). We recommend applying the following sample policy to restrict the use of account instances to all but a select set of AWS accounts. The sample SCP that follows will help you deny creation of account instances of Identity Center to accounts in the organization unless the account ID matches the one you specified in the policy. Replace <ALLOWED-ACCOUNT_ID> with the ID of the account that is allowed to create account instances of Identity Center:

{
    "Version": "2012-10-17",
    "Statement" : [
        {
            "Sid": "DenyCreateAccountInstances",
            "Effect": "Deny",
            "Action": [
                "sso:CreateInstance"
            ],
            "Resource": "*",
            "Condition": {
                "StringNotEquals": [
                    "aws:PrincipalAccount": ["<ALLOWED-ACCOUNT-ID>"]
                ]
            }
        }
    ]
}

To learn more about SCPs, see the AWS Organizations User Guide on service control policies.

Monitoring instance activity with AWS CloudTrail

If your organization has an existing log ingestion pipeline solution to collect logs and generate reports through AWS CloudTrail, then IAM Identity Center supported CloudTrail operations will automatically be present in your pipeline, including additional account instances of IAM Identity Center actions such as sso:CreateInstance.

To create a monitoring solution for IAM Identity Center events in your organization, you should set up monitoring through AWS CloudTrail. CloudTrail is a service that records events from AWS services to facilitate monitoring activity from those services in your accounts. You can create a CloudTrail trail that captures events across all accounts and all Regions in your organization and persists them to Amazon Simple Storage Service (Amazon S3).

After creating a trail for your organization, you can use it in several ways. You can send events to Amazon CloudWatch Logs and set up monitoring and alarms for Identity Center events, which enables immediate notification of supported IAM Identity Center CloudTrail operations. With multiple instances of Identity Center deployed within your organization, you can also enable notification of instance activity, including new instance creation, deletion, application registration, user authentication, or other supported actions.

If you want to take action on IAM Identity Center events, you can create a solution to process events using additional service such as Amazon Simple Notification Service, Amazon Simple Queue Service, and the CloudTrail Processing Library. With this solution, you can set your own business logic and rules as appropriate.

Additionally, you might want to consider AWS CloudTrail Lake, which provides a powerful data store that allows you to query CloudTrail events without needing to manage a complex data loading pipeline. You can quickly create a data store for new events, which will immediately start gathering data that can be queried within minutes. To analyze historical data, you can copy your organization trail to CloudTrail Lake.

The following is an example of a simple query that shows you a list of the Identity Center instances created and deleted, the account where they were created, and the user that created them. Replace <Event_data_store_ID> with your store ID.

SELECT 
    userIdentity.arn AS userARN, eventName, userIdentity.accountId 
FROM 
    <Event_data_store_ID> 
WHERE 
    userIdentity.arn IS NOT NULL 
    AND eventName = 'DeleteInstance' 
    OR eventName = 'CreateInstance'

You can save your query result to an S3 bucket and download a copy of the results in CSV format. To learn more, follow the steps in Download your CloudTrail Lake saved query results. Figure 2 shows the CloudTrail Lake query results.

Figure 2: AWS CloudTrail Lake query results

If you want to automate the sourcing, aggregation, normalization, and data management of security data across your organization using the Open Cyber Security Framework (OCSF) standard, you will benefit from using Amazon Security Lake. This service helps make your organization’s security data broadly accessible to your preferred security analytics solutions to power use cases such like threat detection, investigation, and incident response. Learn more in What is Amazon Security Lake?

Instance management and discovery within an organization

You can create account instances of IAM Identity Center in a standalone account or in an account that belongs to your organization. Creation can happen from an API call (CreateInstance) from the Identity Center console in a member account or from the setup experience of a supported AWS managed application. Learn more about Supported AWS managed applications.

If you decide to apply the DenyCreateAccountInstances SCP shown earlier to accounts in your organization, you will no longer be able to create account instances of IAM Identity Center in those accounts. However, you should also consider that when you invite a standalone AWS account to join your organization, the account might have an existing account instance of Identity Center.

To identify existing instances, who’s using them, and what they’re using them for, you can audit your organization to search for new instances. The following script shows how to discover all IAM Identity Center instances in your organization and export a .csv summary to an S3 bucket. This script is designed to run on the account where Identity Center was enabled. Click here to see instructions on how to use this script.

. . .
. . .
accounts_and_instances_dict={}
duplicated_users ={}

main_session = boto3.session.Session()
sso_admin_client = main_session.client('sso-admin')
identity_store_client = main_session.client('identitystore')
organizations_client = main_session.client('organizations')
s3_client = boto3.client('s3')
logger = logging.getLogger()
logger.setLevel(logging.INFO)

#create function to list all Identity Center instances in your organization
def lambda_handler(event, context):
    application_assignment = []
    user_dict={}
    
    current_account = os.environ['CurrentAccountId']
 
    logger.info("Current account %s", current_account)
    
    paginator = organizations_client.get_paginator('list_accounts')
    page_iterator = paginator.paginate()
    for page in page_iterator:
        for account in page['Accounts']:
            get_credentials(account['Id'],current_account)
            #get all instances per account - returns dictionary of instance id and instances ARN per account
            accounts_and_instances_dict = get_accounts_and_instances(account['Id'], current_account)
                    
def get_accounts_and_instances(account_id, current_account):
    global accounts_and_instances_dict
    
    instance_paginator = sso_admin_client.get_paginator('list_instances')
    instance_page_iterator = instance_paginator.paginate()
    for page in instance_page_iterator:
        for instance in page['Instances']:
            #send back all instances and identity centers
            if account_id == current_account:
                accounts_and_instances_dict = {current_account:[instance['IdentityStoreId'],instance['InstanceArn']]}
            elif instance['OwnerAccountId'] != current_account: 
                accounts_and_instances_dict[account_id]= ([instance['IdentityStoreId'],instance['InstanceArn']])
    return accounts_and_instances_dict
  . . .  
  . . .
  . . .

The following table shows the resulting IAM Identity Center instance summary report with all of the accounts in your organization and their corresponding Identity Center instances.

AccountId	IdentityCenterInstance
111122223333	d-111122223333
111122224444	d-111122223333
111122221111	d-111111111111

Duplicate user detection across multiple instances

A consideration of having multiple IAM Identity Center instances is the possibility of having the same person existing in two or more instances. In this situation, each instance creates a unique identifier for the same person and the identifier associates application-related data to the user. Create a user management process for incoming and outgoing users that is similar to the process you use at the organization level. For example, if a user leaves your organization, you need to revoke access in all Identity Center instances where that user exists.

The code that follows can be added to the previous script to help detect where duplicates might exist so you can take appropriate action. If you find a lot of duplication across account instances, you should consider adopting an organization instance to reduce your management overhead.

...
#determine if the member in IdentityStore have duplicate
def get_users(identityStoreId, user_dict): 
    global duplicated_users
    paginator = identity_store_client.get_paginator('list_users')
    page_iterator = paginator.paginate(IdentityStoreId=identityStoreId)
    for page in page_iterator:
        for user in page['Users']:
            if ( 'Emails' not in user ):
                print("user has no email")
            else:
                for email in user['Emails']:
                    if email['Value'] not in user_dict:
                        user_dict[email['Value']] = identityStoreId
                    else:
                        print("Duplicate user found " + user['UserName'])
                        user_dict[email['Value']] = user_dict[email['Value']] + "," + identityStoreId
                        duplicated_users[email['Value']] = user_dict[email['Value']]
    return user_dict 
...

The following table shows the resulting report with duplicated users in your organization and their corresponding IAM identity Center instances.

User_email	IdentityStoreId
[email protected]	d-111122223333, d-111111111111
[email protected]	d-111122223333, d-111111111111, d-222222222222
[email protected]	d-111111111111, d-222222222222

The full script for all of the above use cases is available in the multiple-instance-management-iam-identity-center GitHub repository. The repository includes instructions to deploy the script using AWS Lambda within the management account. After deployment, you can invoke the Lambda function to get .csv files of every IAM Identity center instance in your organization, the applications assigned to each instance, and the users that have access to those applications. With this function, you also get a report of users that exist in more than one local instance.

Conclusion

In this post, you learned the differences between an IAM Identity Center organization instance and an account instance, considerations for when to use an account instance, and how to use Identity Center APIs to automate discovery of Identity Center account instances in your organization.

To learn more about IAM Identity Center, see the AWS IAM Identity Center user guide.

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AWS achieves SNI 27001 certification for the AWS Asia Pacific (Jakarta) Region

2023-11-20 Airish Mariano

Post Syndicated from Airish Mariano original https://aws.amazon.com/blogs/security/aws-achieves-sni-27001-certification-for-the-aws-asia-pacific-jakarta-region/

Amazon Web Services (AWS) is proud to announce the successful completion of its first Standar Nasional Indonesia (SNI) certification for the AWS Asia Pacific (Jakarta) Region in Indonesia. SNI is the Indonesian National Standard, and it comprises a set of standards that are nationally applicable in Indonesia. AWS is now certified according to the SNI 27001 requirements. An independent third-party auditor that is accredited by the Komite Akreditasi Nasional (KAN/National Accreditation Committee) assessed AWS, per regulations in Indonesia.

SNI 27001 is based on the ISO/IEC 27001 standard, which provides a framework for the development and implementation of an effective information security management system (ISMS). An ISMS that is implemented according to this standard is a tool for risk management, cyber-resilience, and operational excellence.

AWS achieved the certification for compliance with SNI 27001 on October 28, 2023. The SNI 27001 certification covers the Asia Pacific (Jakarta) Region in Indonesia. For a full list of AWS services that are certified under the SNI 27001, see the SNI 27001 compliance page. Customers can also download the latest SNI 27001 certificate on AWS Artifact, a self-service portal for on-demand access to AWS compliance reports. Sign in to AWS Artifact in the AWS Management Console, or learn more at Getting Started with AWS Artifact.

AWS is committed to bringing new services into the scope of its compliance programs to help you meet your architectural, business, and regulatory needs. If you have questions about the SNI 27001 certification, contact your AWS account team.

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Establishing a data perimeter on AWS: Require services to be created only within expected networks

2023-11-20 Harsha Sharma

Post Syndicated from Harsha Sharma original https://aws.amazon.com/blogs/security/establishing-a-data-perimeter-on-aws-require-services-to-be-created-only-within-expected-networks/

Welcome to the fifth post in the Establishing a data perimeter on AWS series. Throughout this series, we’ve discussed how a set of preventative guardrails can create an always-on boundary to help ensure that your trusted identities are accessing your trusted resources over expected networks. In a previous post, we emphasized the importance of preventing access from unexpected locations, even for authorized users. For example, you wouldn’t expect non-public corporate data to be accessed from outside the corporate network. In this post, we demonstrate how to use preventative controls to help ensure that your resources are deployed within your Amazon Virtual Private Cloud (Amazon VPC), so that you can effectively enforce the network perimeter controls. We also explore detective controls you can use to detect the lack of adherence to this requirement.

Let’s begin with a quick refresher on the fundamental concept of data perimeters using Figure 1 as a reference. Customers generally prefer establishing a high-level perimeter to help prevent untrusted entities from coming in and data from going out. The perimeter defines what access customers expect within their AWS environment. It refers to the access patterns among your identities, resources, and networks that should always be blocked. Using those three elements, an assertion can be made to define your perimeter’s goal: access can only be allowed if the identity is trusted, the resource is trusted, and the network is expected. If any of these conditions are false, then the access inside the perimeter is unintended and should be denied. The perimeter is composed of controls implemented on your identities, resources, and networks to maintain that the necessary conditions are true.

Figure 1: A high-level depiction of defining a perimeter around your AWS resources to prevent interaction with unintended IAM principals, unintended resources, and unexpected networks

Now, let’s consider a scenario to understand the problem statement this post is trying to solve. Assume a setup like the one in Figure 2, where an application needs to access an Amazon Simple Storage Service (Amazon S3) bucket using its temporary AWS Identity and Access Management (IAM) credentials over an Amazon S3 VPC endpoint.

Figure 2: Scenario of a simple app using its temporary credential to access an S3 bucket

From our previous posts in this series, we’ve learned that we can use the following set of capabilities to build a network perimeter to achieve our control objectives for this sample scenario.

Control objective	Implemented using	Applicable IAM capability
My identities can access resources only from expected networks. For example, in Figure 2, my application’s temporary credential can only access my S3 bucket when my application is within my expected network space.	Service control policies (SCP)	aws:SourceIp aws:SourceVpc aws:SourceVpce
My resources can only be accessed from expected networks. For example, in Figure 2, my S3 bucket can only be accessed from my expected network space.	Resource-based policies	aws:SourceIp aws:SourceVpc aws:SourceVpce

But there are certain AWS services that allow for different network deployment models, such as providing the choice of associating the service resources with either an AWS managed VPC or a customer managed VPC. For example, an AWS Lambda function always runs inside a VPC owned by the Lambda service (AWS managed VPC) and by default isn’t connected to VPCs in your account (customer managed VPC). For more information, see Connecting Lambda functions to your VPC.

This means that if your application code was deployed as a Lambda function that isn’t connected to your VPC, then the function cannot access your resources with standard network perimeter controls enforced. Let’s understand this situation better using Figure 3, where a Lambda function isn’t configured to connect to the customer VPC. This function cannot access your S3 bucket over the internet because of how the recommended data perimeter in the preceding table has been defined, that is, to only allow your bucket to be accessible from a known network segment (the customer VPC and IP CIDR range) and only allow the IAM role associated with the Lambda function to allow accessing the bucket from known networks. The function also cannot access your S3 bucket through your S3 VPC endpoint because the function isn’t associated with the customer VPC. Lastly, unless other compensating controls are in place, this function might be able to access untrusted resources as your standard data perimeter controls enforced with the VPC endpoint policies won’t be in effect, which might not meet your company’s security requirements.

Figure 3: Lambda function configured to be associated with AWS managed VPC

This means that for the Lambda function to conform to your data perimeter, it must be associated with your network segment (customer VPC) as shown in Figure 4.

Figure 4: Lambda function configured to be associated with the customer managed VPC

To make sure that your Lambda functions are deployed into your networks so that they can access your resources under the purview of data perimeter controls, it’s preferable to have a way to automatically prevent deployment or configuration errors. Additionally, if you have a large deployment of Lambda functions across hundreds or even thousands of accounts, you want an efficient way to enforce conformance of these functions to your data perimeter.

To solve for this problem and make sure that an application team or a developer cannot create a function that’s not associated with your VPC, you can use the lambda:VpcIds or lambda:SubnetIds IAM condition keys (for more information, see Using IAM condition keys for VPC settings). These keys allow you to create and update functions only when VPC settings are satisfied.

In the following SCP example, an IAM principal that is subject to the following SCP policy will only be able to create or update a Lambda function if the function is associated with a VPC (customer VPC). When the customer VPC isn’t specified, the lambda:VpcIds condition key has no value—it is null—and thus this policy will deny creating or updating the function. For more information about how the Null condition operator functions, see Condition operator to check existence of condition keys.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "EnforceVPCFunction",
      "Action": [
          "lambda:CreateFunction",
          "lambda:UpdateFunctionConfiguration"
       ],
      "Effect": "Deny",
      "Resource": "*",
      "Condition": {
        "Null": {
           "lambda:VpcIds": "true"
        }
      }
    }
  ]
}

Additionally, you can use variations of the preceding example and create more fine-grained controls using these condition keys. For more such examples, see Example policies with condition keys for VPC settings.

AWS services such as AWS Glue and Amazon SageMaker have similar feature behavior and provide similar condition keys. For example, the glue:VpcIds condition key allows you to govern the creation of AWS Glue jobs only in your VPC. For further details and an example policy, see Control policies that control settings using condition keys.

Similarly, Amazon SageMaker Studio, SageMaker notebook instances, SageMaker training, and deployed inference containers are internet accessible or enabled by default. The sagemaker:VpcSubnets condition key can be used to restrict launching these resources in a VPC. For more information, see Condition keys for Amazon SageMaker, Connect to Resources From Within a VPC, and Run Training and Inference Containers in Internet-Free Mode.

Detective controls

The AWS Well-Architected Framework recommends applying a defense in-depth approach with multiple security controls (see Security Pillar). This is why in addition to the preventative controls discussed in the form of condition keys in this post, you should also consider using AWS native fully managed governance tools to help you manage your environment’s deployed resources and their conformance to your data perimeter (see Management and Governance on AWS).

For example, AWS Config provides managed rules to check for Lambda functions inside a VPC and Sagemaker notebooks inside a VPC. You can also use the built-in checks of AWS Security Hub to detect and consolidate findings, such as [Lambda.3] Lambda functions should be in a VPC and [SageMaker.2] SageMaker notebook instances should be launched in a custom VPC.

You can also use similar detective controls for AWS services that don’t currently offer built-in preventative controls. For example, OpenSearch Service has an AWS Config managed rule for OpenSearch in VPC only and security hub check for [Opensearch.2] OpenSearch domains should be in a VPC.

Conclusion

In this post, we discussed how you can enforce that specific AWS services resources can only be created such that they adhere to your data perimeter. We used a sample scenario to dive into AWS Lambda and its network deployment options. We then used IAM condition keys as preventative controls to enforce predictable creation of Lambda functions conforming with our security standard. We also discussed additional AWS services that have similar behavior when the same concepts apply. Finally, we briefly discussed some AWS provided managed rules and security checks that you can use as supplementary detective controls to ensure that your preventative controls are in effect as expected.

Additional resources

The following are some additional resources that you can use to further explore data perimeters.

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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Download AWS Security Hub CSV report

2023-11-17 Pablo Pagani

Post Syndicated from Pablo Pagani original https://aws.amazon.com/blogs/security/download-aws-security-hub-csv-report/

AWS Security Hub provides a comprehensive view of your security posture in Amazon Web Services (AWS) and helps you check your environment against security standards and best practices. In this post, I show you a solution to export Security Hub findings to a .csv file weekly and send an email notification to download the file from Amazon Simple Storage Service (Amazon S3). By using this solution, you can share the report with others without providing access to your AWS account. You can also use it to generate assessment reports and prioritize and build a remediation roadmap.

When you enable Security Hub, it collects and consolidates findings from AWS security services that you’re using, such as threat detection findings from Amazon GuardDuty, vulnerability scans from Amazon Inspector, S3 bucket policy findings from Amazon Macie, publicly accessible and cross-account resources from AWS Identity and Access Management Access Analyzer, and resources missing AWS WAF coverage from AWS Firewall Manager. Security Hub also consolidates findings from integrated AWS Partner Network (APN) security solutions.

Cloud security processes can differ from traditional on-premises security in that security is often decentralized in the cloud. With traditional on-premises security operations, security alerts are typically routed to centralized security teams operating out of security operations centers (SOCs). With cloud security operations, it’s often the application builders or DevOps engineers who are best situated to triage, investigate, and remediate security alerts.

This solution uses the Security Hub API, AWS Lambda, Amazon S3, and Amazon Simple Notification Service (Amazon SNS). Findings are aggregated into a .csv file to help identify common security issues that might require remediation action.

Solution overview

This solution assumes that Security Hub is enabled in your AWS account. If it isn’t enabled, set up the service so that you can start seeing a comprehensive view of security findings across your AWS accounts.

How the solution works

An Amazon EventBridge time-based event invokes a Lambda function for processing.
The Lambda function gets finding results from the Security Hub API and writes them into a .csv file.
The API uploads the file into Amazon S3 and generates a presigned URL with a 24-hour duration, or the duration of the temporary credential used in Lambda, whichever ends first.
Amazon SNS sends an email notification to the address provided during deployment. This email address can be updated afterwards through the Amazon SNS console.
The email includes a link to download the file.

Figure 1: Solution overview, deployed through AWS CloudFormation

Fields included in the report:

Note: You can extend the report by modifying the Lambda function to add fields as needed.

Solution resources

The solution provided with this blog post consists of an AWS CloudFormation template named security-hub-full-report-email.json that deploys the following resources:

An Amazon SNS topic named SecurityHubRecurringFullReport and an email subscription to the topic.

Figure 2: SNS topic created by the solution
The email address that subscribes to the topic is captured through a CloudFormation template input parameter. The subscriber is notified by email to confirm the subscription. After confirmation, the subscription to the SNS topic is created. Additional subscriptions can be added as needed to include additional emails or distribution lists.

Figure 3: SNS email subscription
The SendSecurityHubFullReportEmail Lambda function queries the Security Hub API to get findings into a .csv file that’s written to Amazon S3. A pre-authenticated link to the file is generated and sends the email message to the SNS topic described above.

Figure 4: Lambda function created by the solution
An IAM role for the Lambda function to be able to create logs in CloudWatch, get findings from Security Hub, publish messages to SNS, and put objects into an S3 bucket.

Figure 5: Permissions policy for the Lambda function
An EventBridge rule that runs on a schedule named SecurityHubFullReportEmailSchedule used to invoke the Lambda function that generates the findings report. The default schedule is every Monday at 8:00 AM UTC. This schedule can be overwritten by using a CloudFormation input parameter. Learn more about creating cron expressions.

Figure 6: Example of the EventBridge schedule created by the solution

Deploy the solution

Use the following steps to deploy this solution in a single AWS account. If you have a Security Hub administrator account or are using Security Hub cross-Region aggregation, the report will get the findings from the linked AWS accounts and Regions.

To deploy the solution

Download the CloudFormation template security-hub-full-report-email.json from our GitHub repository.
Copy the template to an S3 bucket within your target AWS account and Region. Copy the object URL for the CloudFormation template .json file.
On the AWS Management Console, go to the CloudFormation console. Choose Create Stack and select With new resources.

Figure 7: Create stack with new resources
Under Specify template, in the Amazon S3 URL textbox, enter the S3 object URL for the .json file that you uploaded in step 1.

Figure 8: Specify S3 URL for CloudFormation template
Choose Next. On the next page, do the following:
1. Stack name: Enter a name for the stack.
2. Email address: Enter the email address of the subscriber to the Security Hub findings email.
3. RecurringScheduleCron: Enter the cron expression for scheduling the Security Hub findings email. The default is every Monday at 8:00 AM UTC. Learn more about creating cron expressions.
4. SecurityHubRegion: Enter the Region where Security Hub is aggregating the findings.
Figure 9: Enter stack name and parameters
Choose Next.
Keep all defaults in the screens that follow and choose Next.
Check the box I acknowledge that AWS CloudFormation might create IAM resources, and then choose Create stack.

Test the solution

You can send a test email after the deployment is complete. To do this, open the Lambda console and locate the SendSecurityHubFullReportEmail Lambda function. Perform a manual invocation with an event payload to receive an email within a few minutes. You can repeat this procedure as many times as you want.

Conclusion

In this post I’ve shown you an approach for rapidly building a solution for sending weekly findings report of the security posture of your AWS account as evaluated by Security Hub. This solution helps you to be diligent in reviewing outstanding findings and to remediate findings in a timely way based on their severity. You can extend the solution in many ways, including:

Send a file to an email-enabled ticketing service, such as ServiceNow or another security information and event management (SIEM) that you use.
Add links to internal wikis for workflows such as organizational exceptions to vulnerabilities or other internal processes.
Extend the solution by modifying the filters, email content, and delivery frequency.

To learn more about how to set up and customize Security Hub, see these additional blog posts.

If you have feedback about this post, submit comments in the Comments section below. If you have any questions about this post, start a thread on the AWS Security Hub re:Post forum.

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2023 Canadian Centre for Cyber Security Assessment Summary report available with 20 additional services

2023-11-17 Naranjan Goklani

Post Syndicated from Naranjan Goklani original https://aws.amazon.com/blogs/security/2023-canadian-centre-for-cyber-security-assessment-summary-report-available-with-20-additional-services/

At Amazon Web Services (AWS), we are committed to providing continued assurance to our customers through assessments, certifications, and attestations that support the adoption of current and new AWS services and features. We are pleased to announce the availability of the 2023 Canadian Centre for Cyber Security (CCCS) assessment summary report for AWS. With this assessment, a total of 150 AWS services and features are assessed in the Canada (Central) Region, including 20 additional AWS services and features. The assessment report is available for review and download on demand through AWS Artifact.

The full list of services in scope for the CCCS assessment is available on the Services in Scope page. The 20 new services and features are the following:

The CCCS is Canada’s authoritative source of cyber security expert guidance for the Canadian government, industry, and the general public. Public and commercial sector organizations across Canada rely on CCCS’s rigorous Cloud Service Provider (CSP) IT Security (ITS) assessment in their decision to use CSP services. In addition, CCCS’s ITS assessment process is a mandatory requirement for AWS to provide cloud services to Canadian federal government departments and agencies.

The CCCS cloud service provider information technology security assessment process determines if the Government of Canada (GC) ITS requirements for the CCCS Medium cloud security profile (previously referred to as GC’s PROTECTED B/Medium Integrity/Medium Availability [PBMM] profile) are met as described in ITSG-33 (IT security risk management: A lifecycle approach, Annex 3 – Security control catalogue). As of November 2023, 150 AWS services in the Canada (Central) Region have been assessed by CCCS and meet the requirements for the Medium cloud security profile. Meeting the Medium cloud security profile is required to host workloads that are classified up to and including Medium categorization. On a periodic basis, CCCS assesses new or previously unassessed services and re-assesses the AWS services that were previously assessed to verify that they continue to meet the GC’s requirements. CCCS prioritizes the assessment of new AWS services based on their availability in Canada, and customer demand for the AWS services. The full list of AWS services that have been assessed by CCCS is available on our Services in Scope for CCCS Assessment page.

To learn more about the CCCS assessment or our other compliance and security programs, visit AWS Compliance Programs. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Contact Us 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.

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Implement an early feedback loop with AWS developer tools to shift security left

2023-11-17 Barry Conway

Post Syndicated from Barry Conway original https://aws.amazon.com/blogs/security/implement-an-early-feedback-loop-with-aws-developer-tools-to-shift-security-left/

Early-feedback loops exist to provide developers with ongoing feedback through automated checks. This enables developers to take early remedial action while increasing the efficiency of the code review process and, in turn, their productivity.

Early-feedback loops help provide confidence to reviewers that fundamental security and compliance requirements were validated before review. As part of this process, common expectations of code standards and quality can be established, while shifting governance mechanisms to the left.

In this post, we will show you how to use AWS developer tools to implement a shift-left approach to security that empowers your developers with early feedback loops within their development practices. You will use AWS CodeCommit to securely host Git repositories, AWS CodePipeline to automate continuous delivery pipelines, AWS CodeBuild to build and test code, and Amazon CodeGuru Reviewer to detect potential code defects.

Why the shift-left approach is important

Developers today are an integral part of organizations, building and maintaining the most critical customer-facing applications. Developers must have the knowledge, tools, and processes in place to help them identify potential security issues before they release a product to production.

This is why the shift-left approach is important. Shift left is the process of checking for vulnerabilities and issues in the earlier stages of software development. By following the shift-left process (which should be part of a wider application security review and threat modelling process), software teams can help prevent undetected security issues when they build an application. The modern DevSecOps workflow continues to shift left towards the developer and their practices with the aim to achieve the following:

Drive accountability among developers for the security of their code
Empower development teams to remediate issues up front and at their own pace
Improve risk management by enabling early visibility of potential security issues through early feedback loops

You can use AWS developer tools to help provide this continual early feedback for developers upon each commit of code.

Solution prerequisites

To follow along with this solution, make sure that you have the following prerequisites in place:

An AWS account
Access to these AWS services:

Make sure that you have a general working knowledge of the listed services and DevOps practices.

Solution overview

The following diagram illustrates the architecture of the solution.

Figure 1: Solution overview

We will show you how to set up a continuous integration and continuous delivery (CI/CD) pipeline by using AWS developer tools—CodeCommit, CodePipeline, CodeBuild, and CodeGuru—that you will integrate with the code repository to detect code security vulnerabilities. As shown in Figure 1, the solution has the following steps:

The developer commits the new branch into the code repository.
The developer creates a pull request to the main branch.
Pull requests initiate two jobs: an Amazon CodeGuru Reviewer code scan and a CodeBuild job.
1. CodeGuru Reviewer uses program analysis and machine learning to help detect potential defects in your Java and Python code, and provides recommendations to improve the code. CodeGuru Reviewer helps detect security vulnerabilities, secrets, resource leaks, concurrency issues, incorrect input validation, and deviation from best practices for using AWS APIs and SDKs.
2. You can configure the CodeBuild deployment with third-party tools, such as Bandit for Python to help detect security issues in your Python code.
CodeGuru Reviewer or CodeBuild writes back the findings of the code scans to the pull request to provide a single common place for developers to review the findings that are relevant to their specific code updates.

The following table presents some other tools that you can integrate into the early-feedback toolchain, depending on the type of code or artefacts that you are evaluating:

Early feedback – security tools	Usage	License
cfn-guard , cfn-nag , cfn-lint	Infrastructure linting and validation	cfn-guard license, cfn-nag license, cfn-lint license
CodeGuru, Bandit	Python	Bandit license
CodeGuru	Java
npm-audit, Dependabot	npm libraries	Dependabot license

When you deploy the solution in your AWS account, you can review how Bandit for Python has been built into the deployment pipeline by using AWS CodeBuild with a configured buildspec file, as shown in Figure 2. You can implement the other tools in the table by using a similar approach.

Figure 2: Bandit configured in CodeBuild

Walkthrough

To deploy the solution, you will complete the following steps:

Deploy the solution

The first step is to deploy the required resources into your AWS environment by using CloudFormation.

To deploy the solution

Choose the following Launch Stack button to deploy the solution’s CloudFormation template:

The solution deploys in the AWS US East (N. Virginia) Region (us-east-1) by default because each service listed in the Prerequisites section is available in this Region. To deploy the solution in a different Region, use the Region selector in the console navigation bar and make sure that the services required for this walkthrough are supported in your newly selected Region. For service availability by Region, see AWS Services by Region.
On the Quick Create Stack screen, do the following:
1. Leave the provided parameter defaults in place.
2. Scroll to the bottom, and in the Capabilities section, select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
3. Choose Create Stack.
When the CloudFormation template has completed, open the AWS Cloud9 console.
In the Environments table, for the provisioned shift-left-blog-cloud9-ide environment, choose Open, as shown in Figure 3.

Figure 3: Cloud9 environments
The provisioned Cloud9 environment opens in a new tab. Wait for Cloud9 to initialize the two sample code repositories: shift-left-sample-app-java and shift-left-sample-app-python, as shown in Figure 4. For this post, you will work only with the Python sample repository shift-left-sample-app-python, but the procedures we outline will also work for the Java repository.

Figure 4: Cloud9 IDE

Associate CodeGuru Reviewer with a code repository

The next step is to associate the Python code repository with CodeGuru Reviewer. After you associate the repository, CodeGuru Reviewer analyzes and comments on issues that it finds when you create a pull request.

To associate CodeGuru Reviewer with a repository

Open the CodeGuru console, and in the left navigation pane, under Reviewer, choose Repositories.
In the Repositories section, choose Associate repository and run analysis.
In the Associate repository section, do the following:
1. For Select source provider, select AWS CodeCommit.
2. For Repository location,select shift-left-sample-app-python.
In the Run a repository analysis section, do the following, as shown in Figure 5:
1. For Source branch, select main.
2. For Code review name – optional, enter a name.
3. For Tags – optional, leave the default settings.
4. Choose Associate repository and run analysis.
  
  Figure 5: CodeGuru repository configuration
CodeGuru initiates the Full repository analysis and the status is Pending, as shown in Figure 6. The full analysis takes about 5 minutes to complete. Wait for the status to change from Pending to Completed.

Figure 6: CodeGuru full analysis pending

Create a pull request

The next step is to create a new branch and to push sample code to the repository by creating a pull request so that the code scan can be initiated by CodeGuru Reviewer and the CodeBuild job.

To create a new branch

In the Cloud9 IDE, locate the terminal and create a new branch by running the following commands.
```
cd ~/environment/shift-left-sample-app-python
git checkout -b python-test
```
Confirm that you are working from the new branch, which will be highlighted in the Cloud9 IDE terminal, as shown in Figure 7.
```
git branch -v
```
Figure 7: Cloud9 IDE terminal

To create a new file and push it to the code repository

Create a new file called sample.py.
```
touch sample.py
```
Copy the following sample code, paste it into the sample.py file, and save the changes, as shown in Figure 8.
```
import requests

data = requests.get("https://www.example.org/", verify = False)
print(data.status_code)
```
Figure 8: Cloud9 IDE noncompliant code
Commit the changes to the repository.
```
git status
git add -A
git commit -m "shift left blog python sample app update"
```
Note: if you receive a message to set your name and email address, you can ignore it because Git will automatically set these for you, and the Git commit will complete successfully.
Push the changes to the code repository, as shown in Figure 9.
```
git push origin python-test
```
Figure 9: Git push

To create a new pull request

Open the CodeCommit console and select the code repository called shift-left-sample-app-python.
From the Branches dropdown, select the new branch that you created and pushed, as shown in Figure 10.

Figure 10: CodeCommit branch selection
In your new branch, select the file sample.py, confirm that the file has the changes that you made, and then choose Create pull request, as shown in Figure 11.

Figure 11: CodeCommit pull request

A notification appears stating that the new code updates can be merged.
In the Source dropdown, choose the new branch python-test. In the Destination dropdown, choose the main branch where you intend to merge your code changes when the pull request is closed.
To have CodeCommit run a comparison between the main branch and your new branch python-test, choose Compare. To see the differences between the two branches, choose the Changes tab at the bottom of the page. CodeCommit also assesses whether the two branches can be merged automatically when the pull request is closed.
When you’re satisfied with the comparison results for the pull request, enter a Title and an optional Description, and then choose Create pull request. Your pull request appears in the list of pull requests for the CodeCommit repository, as shown in Figure 12.

Figure 12: Pull request

The creation of this pull request has automatically started two separate code scans. The first is a CodeGuru incremental code review and the second uses CodeBuild, which utilizes Bandit to perform a security code scan of the Python code.

Review code scan results and resolve detected security vulnerabilities

The next step is to review the code scan results to identify security vulnerabilities and the recommendations on how to fix them.

To review the code scan results

Open the CodeGuru console, and in the left navigation pane, under Reviewer, select Code reviews.
On the Incremental code reviews tab, make sure that you see a new code review item created for the preceding pull request.

Figure 13: CodeGuru Code review
After a few minutes, when CodeGuru completes the incremental analysis, choose the code review to review the CodeGuru recommendations on the pull request. Figure 14 shows the CodeGuru recommendations for our example.

Figure 14: CodeGuru recommendations
Open the CodeBuild console and select the CodeBuild job called shift-left-blog-pr-Python. In our example, this job should be in a Failed state.

Open the CodeBuild run, and under the Build history tab, select the CodeBuild job, which is in Failed state. Under the Build Logs tab, scroll down until you see the following errors in the logs. Note that the severity of the finding is High, which is why the CodeBuild job failed. You can review the Bandit scanning options in the Bandit documentation.

Test results:
>> Issue: [B501:request_with_no_cert_validation] Call to requests with verify=False disabling SSL certificate checks, security issue.
   Severity: High   Confidence: High
   CWE: CWE-295 (https://cwe.mitre.org/data/definitions/295.html)
   More Info: https://bandit.readthedocs.io/en/1.7.5/plugins/b501_request_with_no_cert_validation.html
   Location: sample.py:3:7

2   
3   data = requests.get("https://www.example.org/", verify = False)
4   print(data.status_code)

Navigate to the CodeCommit console, and on the Activity tab of the pull request, review the CodeGuru recommendations. You can also review the results of the CodeBuild jobs that Bandit performed, as shown in Figure 15.

Figure 15: CodeGuru recommendations and CodeBuild logs

This demonstrates how developers can directly link the relevant information relating to security code scans with their code development and associated pull requests, hence shifting to the left the required security awareness for developers.

To resolve the detected security vulnerabilities

In the Cloud9 IDE, navigate to the file sample.py in the Python sample repository, as shown in Figure 16.

Figure 16: Cloud9 IDE sample.py
Copy the following code and paste it in the sample.py file, overwriting the existing code. Save the update.
```
import requests

data = requests.get("https://www.example.org", timeout=5)
print(data.status_code)
```

Commit the changes by running the following commands.

git status
git add -A
git commit -m "shift left python sample.py resolve security errors"
git push origin python-test

Open the CodeCommit console and choose the Activity tab on the pull request that you created earlier. You will see a banner indicating that the pull request was updated. You will also see new comments indicating that new code scans using CodeGuru and CodeBuild were initiated for the new pull request update.
In the CodeGuru console, on the Incremental code reviews page, check that a new code scan has begun. When the scans are finished, review the results in the CodeGuru console and the CodeBuild build logs, as described previously. The previously detected security vulnerability should now be resolved.
In the CodeCommit console, on the Activity tab, under Activity history, review the comments to verify that each of the code scans has a status of Passing, as shown in Figure 17.

Figure 17: CodeCommit activity history
Now that the security issue has been resolved, merge the pull request into the main branch of the code repository. Choose Merge, and under Merge strategy, select Fast Forward merge.

AWS account clean-up

Clean up the resources created by this solution to avoid incurring future charges.

To clean up your account

Start by deleting the CloudFormation stacks for the Java and Python sample applications that you deployed. In the CloudFormation console, in the Stacks section, select one of these stacks and choose Delete; then select the other stack and choose Delete.

Figure 18: Delete repository stack
To initiate deletion of the Cloud9 CloudFormation stack, select it and choose Delete.
Open the Amazon S3 console, and in the search box, enter shift-left to search for the S3 bucket that CodePipeline used.

Figure 19: Select CodePipeline S3 bucket
Select the S3 bucket, select all of the object folders in the bucket, and choose Delete

Figure 20: Select CodePipeline S3 objects
To confirm deletion of the objects, in the section Permanently delete objects?, enter permanently delete, and then choose Delete objects. A banner message that states Successfully deleted objects appears at the top confirming the object deletion.
Navigate back to the CloudFormation console, select the stack named shift-left-blog, and choose Delete.

Conclusion

In this blog post, we showed you how to implement a solution that enables early feedback on code development through status comments in the CodeCommit pull request activity tab by using Amazon CodeGuru Reviewer and CodeBuild to perform automated code security scans on the creation of a code repository pull request.

We configured CodeBuild with Bandit for Python to demonstrate how you can integrate third-party or open-source tools into the development cycle. You can use this approach to integrate other tools into the workflow.

Shifting security left early in the development cycle can help you identify potential security issues earlier and empower teams to remediate issues earlier, helping to prevent the need to refactor code towards the end of a build.

This solution provides a simple method that you can use to view and understand potential security issues with your newly developed code and thus enhances your awareness of the security requirements within your organization.

It’s simple to get started. Sign up for an AWS account, deploy the provided CloudFormation template through the Launch Stack button, commit your code, and start scanning for vulnerabilities.

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

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Use scalable controls for AWS services accessing your resources

2023-11-16 James Greenwood

Post Syndicated from James Greenwood original https://aws.amazon.com/blogs/security/use-scalable-controls-for-aws-services-accessing-your-resources/

Sometimes you want to configure an AWS service to access your resource in another service. For example, you can configure AWS CloudTrail, a service that monitors account activity across your AWS infrastructure, to write log data to your bucket in Amazon Simple Storage Service (Amazon S3). When you do this, you want assurance that the service will only access your resource on your behalf—you don’t want an untrusted entity to be able to use the service to access your resource. Before today, you could achieve this by using the two AWS Identity and Access Management (IAM) condition keys, aws:SourceAccount and aws:SourceArn. You can use these condition keys to help make sure that a service accesses your resource only on behalf of specific accounts or resources that you trust. However, because these condition keys require you to specify individual accounts and resources, they can be difficult to manage at scale, especially in larger organizations.

Recently, IAM launched two new condition keys that can help you achieve this in a more scalable way that is simpler to manage within your organization:

aws:SourceOrgID — use this condition key to make sure that an AWS service can access your resources only when the request originates from a particular organization ID in AWS Organizations.
aws:SourceOrgPaths — use this condition key to make sure that an AWS service can access your resources only when the request originates from one or more organizational units (OUs) in your organization.

In this blog post, we describe how you can use the four available condition keys, including the two new ones, to help you control how AWS services access your resources.

Background

Imagine a scenario where you configure an AWS service to access your resource in another service. Let’s say you’re using Amazon CloudWatch to observe resources in your AWS environment, and you create an alarm that activates when certain conditions occur. When the alarm activates, you want it to publish messages to a topic that you create in Amazon Simple Notification Service (Amazon SNS) to generate notifications.

Figure 1 depicts this process.

Figure 1: Amazon CloudWatch publishing messages to an SNS topic

In this scenario, there’s a resource-based policy controlling access to your SNS topic. For CloudWatch to publish messages to it, you must configure the policy to allow access by CloudWatch. When you do this, you identify CloudWatch using an AWS service principal, in this case cloudwatch.amazonaws.com.

Cross-service access

This is an example of a common pattern known as cross-service access. With cross-service access, a calling service accesses your resource in a called service, and a resource-based policy attached to your resource grants access to the calling service. The calling service is identified using an AWS service principal in the form <SERVICE-NAME>.amazonaws.com, and it accesses your resource on behalf of an originating resource, such as a CloudWatch alarm.

Figure 2 shows cross-service access.

Figure 2: Cross-service access

When you configure cross-service access, you want to make sure that the calling service will access your resource only on your behalf. That means you want the originating resource to be controlled by someone whom you trust. If an untrusted entity creates their own CloudWatch alarm in their AWS environment, for example, then their alarm should not be able to publish messages to your SNS topic.

If an untrusted entity could use a calling service to access your resource on their behalf, it would be an example of what’s known as the confused deputy problem. The confused deputy problem is a security issue in which an entity that doesn’t have permission to perform an action coerces a more privileged entity (in this case, a calling service) to perform the action instead.

Use condition keys to help prevent cross-service confused deputy issues

AWS provides global condition keys to help you prevent cross-service confused deputy issues. You can use these condition keys to control how AWS services access your resources.

Before today, you could use the aws:SourceAccount or aws:SourceArn condition keys to make sure that a calling service accesses your resource only when the request originates from a specific account (with aws:SourceAccount) or a specific originating resource (with aws:SourceArn). However, there are situations where you might want to allow multiple resources or accounts to use a calling service to access your resource. For example, you might want to create many VPC flow logs in an organization that publish to a central S3 bucket. To achieve this using the aws:SourceAccount or aws:SourceArn condition keys, you must enumerate all the originating accounts or resources individually in your resource-based policies. This can be difficult to manage, especially in large organizations, and can potentially cause your resource-based policy documents to reach size limits.

Now, you can use the new aws:SourceOrgID or aws:SourceOrgPaths condition keys to make sure that a calling service accesses your resource only when the request originates from a specific organization (with aws:SourceOrgID) or a specific organizational unit (with aws:SourceOrgPaths). This helps avoid the need to update policies when accounts are added or removed, reduces the size of policy documents, and makes it simpler to create and review policy statements.

The following table summarizes the four condition keys that you can use to help prevent cross-service confused deputy issues. These keys work in a similar way, but with different levels of granularity.

Use case	Condition key	Value	Allowed operators	Single/multi valued	Example value
Allow a calling service to access your resource only on behalf of an organization that you trust.	`aws:SourceOrgID`	AWS organization ID of the resource making a cross-service access request	String operators	Single-valued key	o-a1b2c3d4e5
Allow a calling service to access your resource only on behalf of an organizational unit (OU) that you trust.	`aws:SourceOrgPaths`	Organization entity paths of the resource making a cross-service access request	Set operators and string operators	Multivalued key	o-a1b2c3d4e5/r-ab12/ou-ab12-11111111/ou-ab12-22222222/
Allow a calling service to access your resource only on behalf of an account that you trust.	`aws:SourceAccount`	AWS account ID of the resource making a cross-service access request	String operators	Single-valued key	111122223333
Allow a calling service to access your resource only on behalf of a resource that you trust.	`aws:SourceArn`	Amazon Resource Name (ARN) of the resource making a cross- service access request	ARN operators (recommended) or string operators	Single-valued key	arn:aws:cloudwatch:eu-west-1:111122223333:alarm:myalarm

When to use the condition keys

AWS recommends that you use these condition keys in any resource-based policy statements that allow access by an AWS service, except where the relevant condition key is not yet supported by the service. To find out whether a condition key is supported by a particular service, see AWS global condition context keys in the AWS Identity and Access Management User Guide.

Note: Only use these condition keys in resource-based policies that allow access by an AWS service. Don’t use them in other use cases, including identity-based policies and service control policies (SCPs), where these condition keys won’t be populated.

Use condition keys for defense in depth

AWS services use a variety of mechanisms to help prevent cross-service confused deputy issues, and the details vary by service. For example, where a calling service accesses an S3 bucket, some services use S3 prefixes to help prevent confused deputy issues. For more information, see the relevant service documentation.

Where supported by the service, AWS recommends that you use the condition keys we describe in this post regardless of whether the service has another mechanism in place to help prevent cross-service confused deputy issues. This helps to make your intentions explicit, provide defense in depth, and guard against misconfigurations.

Example use cases

Let’s walk through some example use cases to learn how to use these condition keys in practice.

First, imagine you’re using Amazon Virtual Private Cloud (Amazon VPC) to manage logically isolated virtual networks. In Amazon VPC, you can configure flow logs, which capture information about your network traffic. Let’s say you want a flow log to write data into an S3 bucket for later analysis. This process is depicted in Figure 3.

Figure 3: Amazon VPC writing flow logs to an S3 bucket

This constitutes another cross-service access scenario. In this case, Amazon VPC is the calling service, Amazon S3 is the called service, the VPC flow log is the originating resource, and the S3 bucket is your resource in the called service.

To allow access, the resource-based policy for your S3 bucket (known as a bucket policy) must allow Amazon VPC to put objects there. The Principal element in this policy specifies the AWS service principal of the service that will access the resource, which for VPC flow logs is delivery.logs.amazonaws.com.

Initial policy without confused deputy prevention

The following is an initial version of the bucket policy that allows Amazon VPC to put objects in the bucket but doesn’t yet provide confused deputy prevention. We’re showing this policy for illustration purposes; don’t use it in its current form.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "PARTIAL-EXAMPLE-DO-NOT-USE",
            "Effect": "Allow",
            "Principal": {
                "Service": "delivery.logs.amazonaws.com"
            },
            "Action": "s3:PutObject",
            "Resource": "arn:aws:s3:::<DOC-EXAMPLE-BUCKET>/*"
        }
    ]
}

Note: For simplicity, we only show one of the policy statements that you need to allow VPC flow logs to write to a bucket. In a real-life bucket policy for flow logs, you need two policy statements: one allowing actions on the bucket, and one allowing actions on the bucket contents. These are described in Publish flow logs to Amazon S3. Both policy statements work in the same way with respect to confused deputy prevention.

This policy statement allows Amazon VPC to put objects in the bucket. However, it allows Amazon VPC to do that on behalf of any flow log in any account. There’s nothing in the policy to tell Amazon VPC that it should access this bucket only if the flow log belongs to a specific organization, OU, account, or resource that you trust.

Let’s now update the policy to help prevent cross-service confused deputy issues. For the rest of this post, the remaining policy samples provide confused deputy protection, but at different levels of granularity.

Specify a trusted organization

Continuing with the previous example, imagine that you now have an organization in AWS Organizations, and you want to create VPC flow logs in various accounts within your organization that publish to a central S3 bucket. You want Amazon VPC to put objects in the bucket only if the request originates from a flow log that resides in your organization.

You can achieve this by using the new aws:SourceOrgID condition key. In a cross-service access scenario, this condition key evaluates to the ID of the organization that the request came from. You can use this condition key in the Condition element of a resource-based policy to allow actions only if aws:SourceOrgID matches the ID of a specific organization, as shown in the following example. In your own policy, make sure to replace <DOC-EXAMPLE-BUCKET> and <MY-ORGANIZATION-ID> with your own information.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VPCLogsDeliveryWrite",
            "Effect": "Allow",
            "Principal": {
                "Service": "delivery.logs.amazonaws.com"
            },
            "Action": "s3:PutObject",
            "Resource": "arn:aws:s3:::<DOC-EXAMPLE-BUCKET>/*",
            "Condition": {
                "StringEquals": {
                    "aws:SourceOrgID": "<MY-ORGANIZATION-ID>"
                }
            }
        }
    ]
}

The revised policy states that Amazon VPC can put objects in the bucket only if the request originates from a flow log in your organization. Now, if someone creates a flow log outside your organization and configures it to access your bucket, they will get an access denied error.

You can use aws:SourceOrgID in this way to allow a calling service to access your resource only if the request originates from a specific organization, as shown in Figure 4.

Figure 4: Specify a trusted organization using aws:SourceOrgID

Specify a trusted OU

What if you don’t want to trust your entire organization, but only part of it? Let’s consider a different scenario. Imagine that you want to send messages from Amazon SNS into a queue in Amazon Simple Queue Service (Amazon SQS) so they can be processed by consumers. This is depicted in Figure 5.

Figure 5: Amazon SNS sending messages to an SQS queue

Now imagine that you want your SQS queue to receive messages only if they originate from an SNS topic that resides in a specific organizational unit (OU) in your organization. For example, you might want to allow messages only if they originate from a production OU that is subject to change control.

You can achieve this by using the new aws:SourceOrgPaths condition key. As before, you use this condition key in a resource-based policy attached to your resource. In a cross-service access scenario, this condition key evaluates to the AWS Organizations entity path that the request came from. An entity path is a text representation of an entity within an organization.

You build an entity path for an OU by using the IDs of the organization, root, and all OUs in the path down to and including the OU. For example, consider the organizational structure shown in Figure 6.

Figure 6: Example organization structure

In this example, you can specify the Prod OU by using the following entity path:

o-a1b2c3d4e5/r-ab12/ou-ab12-11111111/ou-ab12-22222222/

For more information about how to construct an entity path, see Understand the AWS Organizations entity path.

Let’s now match the aws:SourceOrgPaths condition key against a specific entity path in the Condition element of a resource-based policy for an SQS queue. In your own policy, make sure to replace <MY-QUEUE-ARN> and <MY-ENTITY-PATH> with your own information.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "Allow-SNS-SendMessage",
            "Effect": "Allow",
            "Principal": {
                "Service": "sns.amazonaws.com"
            },
            "Action": "sqs:SendMessage",
            "Resource": "<MY-QUEUE-ARN>",
            "Condition": {
                "Null": {
                    "aws:SourceOrgPaths": "false"
                },
                "ForAllValues:StringEquals": {
                    "aws:SourceOrgPaths": "<MY-ENTITY-PATH>"
                }
            }
        }
    ]
}

Note: aws:SourceOrgPaths is a multivalued condition key, which means it’s capable of having multiple values in the request context. At the time of writing, it contains a single entity path if the request originates from an account in an organization, and a null value if the request originates from an account that’s not in an organization. Because this key is multivalued, you need to use both a set operator and a string operator to compare values.

In this policy, there are two conditions in the Condition block. The first uses the Null condition operator and compares with a false value to confirm that the condition key’s value is not null. The second uses set operator ForAllValues, which returns true if every condition key value in the request matches at least one value in your policy condition, and string operator StringEquals, which requires an exact match with a value specified in your policy condition.

Note: The reason for the null check is that set operator ForAllValues returns true when a condition key resolves to null. With an Allow effect and the null check in place, access is denied if the request originates from an account that’s not in an organization.

With this policy applied to your SQS queue, Amazon SNS can send messages to your queue only if the message came from an SNS topic in a specific OU.

You can use aws:SourceOrgPaths in this way to allow a calling service to access your resource only if the request originates from a specific organizational unit, as shown in Figure 7.

Figure 7: Specify a trusted OU using aws:SourceOrgPaths

Specify a trusted OU and its children

In the previous example, we specified a trusted OU, but that didn’t include its child OUs. What if you want to include its children as well?

You can achieve this by replacing the string operator StringEquals with StringLike. This allows you to use wildcards in the entity path. Using the organization structure from the previous example, the following Condition evaluates to true only if the condition key value is not null and the request originates from the Prod OU or any of its child OUs.

"Condition": {
    "Null": {
        "aws:SourceOrgPaths": "false"
    },
    "ForAllValues:StringLike": {
        "aws:SourceOrgPaths": "o-a1b2c3d4e5/r-ab12/ou-ab12-11111111/ou-ab12-22222222/*"
    }
}

Specify a trusted account

If you want to be more granular, you can allow a service to access your resource only if the request originates from a specific account. You can achieve this by using the aws:SourceAccount condition key. In a cross-service access scenario, this condition key evaluates to the ID of the account that the request came from.

The following Condition evaluates to true only if the request originates from the account that you specify in the policy. In your own policy, make sure to replace <MY-ACCOUNT-ID> with your own information.

"Condition": {
    "StringEquals": {
        "aws:SourceAccount": "<MY-ACCOUNT-ID>"
    }
}

You can use this condition element within a resource-based policy to allow a calling service to access your resource only if the request originates from a specific account, as shown in Figure 8.

Figure 8: Specify a trusted account using aws:SourceAccount

Specify a trusted resource

If you want to be even more granular, you can allow a service to access your resource only if the request originates from a specific resource. For example, you can allow Amazon SNS to send messages to your SQS queue only if the request originates from a specific topic within Amazon SNS.

You can achieve this by using the aws:SourceArn condition key. In a cross-service access scenario, this condition key evaluates to the Amazon Resource Name (ARN) of the originating resource. This provides the most granular form of cross-service confused deputy prevention.

The following Condition evaluates to true only if the request originates from the resource that you specify in the policy. In your own policy, make sure to replace <MY-RESOURCE-ARN> with your own information.

"Condition": {
    "ArnEquals": {
        "aws:SourceArn": "<MY-RESOURCE-ARN>"
    }
}

Note: AWS recommends that you use an ARN operator rather than a string operator when comparing ARNs. This example uses ArnEquals to match the condition key value against the ARN specified in the policy.

You can use this condition element within a resource-based policy to allow a calling service to access your resource only if the request comes from a specific originating resource, as shown in Figure 9.

Figure 9: Specify a trusted resource using aws:SourceArn

Specify multiple trusted resources, accounts, OUs, or organizations

The four condition keys allow you to specify multiple trusted entities by matching against an array of values. This allows you to specify multiple trusted resources, accounts, OUs, or organizations in your policies.

Conclusion

In this post, you learned about cross-service access, in which an AWS service communicates with another AWS service to access your resource. You saw that it’s important to make sure that such services access your resources only on your behalf in order to help avoid cross-service confused deputy issues.

We showed you how to help prevent cross-service confused deputy issues by using two new condition keys aws:SourceOrgID and aws:SourceOrgPaths, as well as the other available condition keys aws:SourceAccount and aws:SourceArn. You learned that you should use these condition keys in any resource-based policy statements that allow access by an AWS service, if the condition key is supported by the service. This helps make sure that a calling service can access your resource only when the request originates from a specific organization, OU, account, or resource that you trust.

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 re:Post or contact AWS Support.

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Automate and enhance your code security with AI-powered services

2023-11-16 Dylan Souvage

Post Syndicated from Dylan Souvage original https://aws.amazon.com/blogs/security/automate-and-enhance-your-code-security-with-ai-powered-services/

Organizations are increasingly embracing a shift-left approach when it comes to security, actively integrating security considerations into their software development lifecycle (SDLC). This shift aligns seamlessly with modern software development practices such as DevSecOps and continuous integration and continuous deployment (CI/CD), making it a vital strategy in today’s rapidly evolving software development landscape. At its core, shift left promotes a security-as-code culture, where security becomes an integral part of the entire application lifecycle, starting from the initial design phase and extending all the way through to deployment. This proactive approach to security involves seamlessly integrating security measures into the CI/CD pipeline, enabling automated security testing and checks at every stage of development. Consequently, it accelerates the process of identifying and remediating security issues.

By identifying security vulnerabilities early in the development process, you can promptly address them, leading to significant reductions in the time and effort required for mitigation. Amazon Web Services (AWS) encourages this shift-left mindset, providing services that enable a seamless integration of security into your DevOps processes, fostering a more robust, secure, and efficient system. In this blog post we share how you can use Amazon CodeWhisperer, Amazon CodeGuru, and Amazon Inspector to automate and enhance code security.

CodeWhisperer is a versatile, artificial intelligence (AI)-powered code generation service that delivers real-time code recommendations. This innovative service plays a pivotal role in the shift-left strategy by automating the integration of crucial security best practices during the early stages of code development. CodeWhisperer is equipped to generate code in Python, Java, and JavaScript, effectively mitigating vulnerabilities outlined in the OWASP (Open Web Application Security Project) Top 10. It uses cryptographic libraries aligned with industry best practices, promoting robust security measures. Additionally, as you develop your code, CodeWhisperer scans for potential security vulnerabilities, offering actionable suggestions for remediation. This is achieved through generative AI, which creates code alternatives to replace identified vulnerable sections, enhancing the overall security posture of your applications.

Next, you can perform further vulnerability scanning of code repositories and supported integrated development environments (IDEs) with Amazon CodeGuru Security. CodeGuru Security is a static application security tool that uses machine learning to detect security policy violations and vulnerabilities. It provides recommendations for addressing security risks and generates metrics so you can track the security health of your applications. Examples of security vulnerabilities it can detect include resource leaks, hardcoded credentials, and cross-site scripting.

Finally, you can use Amazon Inspector to address vulnerabilities in workloads that are deployed. Amazon Inspector is a vulnerability management service that continually scans AWS workloads for software vulnerabilities and unintended network exposure. Amazon Inspector calculates a highly contextualized risk score for each finding by correlating common vulnerabilities and exposures (CVE) information with factors such as network access and exploitability. This score is used to prioritize the most critical vulnerabilities to improve remediation response efficiency. When started, it automatically discovers Amazon Elastic Compute Cloud (Amazon EC2) instances, container images residing in Amazon Elastic Container Registry (Amazon ECR), and AWS Lambda functions, at scale, and immediately starts assessing them for known vulnerabilities.

Figure 1: An architecture workflow of a developer’s code workflow

Amazon CodeWhisperer

CodeWhisperer is powered by a large language model (LLM) trained on billions of lines of code, including code owned by Amazon and open-source code. This makes it a highly effective AI coding companion that can generate real-time code suggestions in your IDE to help you quickly build secure software with prompts in natural language. CodeWhisperer can be used with four IDEs including AWS Toolkit for JetBrains, AWS Toolkit for Visual Studio Code, AWS Lambda, and AWS Cloud9.

After you’ve installed the AWS Toolkit, there are two ways to authenticate to CodeWhisperer. The first is authenticating to CodeWhisperer as an individual developer using AWS Builder ID, and the second way is authenticating to CodeWhisperer Professional using the IAM Identity Center. Authenticating through AWS IAM Identity Center means your AWS administrator has set up CodeWhisperer Professional for your organization to use and provided you with a start URL. AWS administrators must have configured AWS IAM Identity Center and delegated users to access CodeWhisperer.

As you use CodeWhisperer it filters out code suggestions that include toxic phrases (profanity, hate speech, and so on) and suggestions that contain commonly known code structures that indicate bias. These filters help CodeWhisperer generate more inclusive and ethical code suggestions by proactively avoiding known problematic content. The goal is to make AI assistance more beneficial and safer for all developers.

CodeWhisperer can also scan your code to highlight and define security issues in real time. For example, using Python and JetBrains, if you write code that would write unencrypted AWS credentials to a log — a bad security practice — CodeWhisperer will raise an alert. Security scans operate at the project level, analyzing files within a user’s local project or workspace and then truncating them to create a payload for transmission to the server side.

For an example of CodeGuru in action, see Security Scans. Figure 2 is a screenshot of a CodeGuru scan.

Figure 2: CodeWhisperer performing a security scan in Visual Studio Code

Furthermore, the CodeWhisperer reference tracker detects whether a code suggestion might be similar to particular CodeWhisperer open source training data. The reference tracker can flag such suggestions with a repository URL and project license information or optionally filter them out. Using CodeWhisperer, you improve productivity while embracing the shift-left approach by implementing automated security best practices at one of the principal layers—code development.

CodeGuru Security

Amazon CodeGuru Security significantly bolsters code security by harnessing the power of machine learning to proactively pinpoint security policy violations and vulnerabilities. This intelligent tool conducts a thorough scan of your codebase and offers actionable recommendations to address identified issues. This approach verifies that potential security concerns are corrected early in the development lifecycle, contributing to an overall more robust application security posture.

CodeGuru Security relies on a set of security and code quality detectors crafted to identify security risks and policy violations. These detectors empower developers to spot and resolve potential issues efficiently.

CodeGuru Security allows manual scanning of existing code and automating integration with popular code repositories like GitHub and GitLab. It establishes an automated security check pipeline through either AWS CodePipeline or Bitbucket Pipeline. Moreover, CodeGuru Security integrates with Amazon Inspector Lambda code scanning, enabling automated code scans for your Lambda functions.

Notably, CodeGuru Security doesn’t just uncover security vulnerabilities; it also offers insights to optimize code efficiency. It identifies areas where code improvements can be made, enhancing both security and performance aspects within your applications.

Initiating CodeGuru Security is a straightforward process, accessible through the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS SDKs, and multiple integrations. This allows you to run code scans, review recommendations, and implement necessary updates, fostering a continuous improvement cycle that bolsters the security stance of your applications.

Use Amazon CodeGuru to scan code directly and in a pipeline

Use the following steps to create a scan in CodeGuru to scan code directly and to integrate CodeGuru with AWS CodePipeline.

Note: You must provide sample code to scan.

Scan code directly

Open the AWS Management Console using your organization management account and go to Amazon CodeGuru.
In the navigation pane, select Security and then select Scans.
Choose Create new scan to start your manual code scan.

Figure 3: Scans overview
On the Create Scan page:
1. Choose Choose file to upload your code.
  
  Note: The file must be in .zip format and cannot exceed 5 GB.
2. Enter a unique name to identify your scan.
3. Choose Create scan.
  
  Figure 4: Create scan
After you create the scan, the configured scan will automatically appear in the Scans table, where you see the Scan name, Status, Open findings, Date of last scan, and Revision number (you review these findings later in the Findings section of this post).

Figure 5: Scan update

Automated scan using AWS CodePipeline integration

Still in the CodeGuru console, in the navigation pane under Security, select Integrations. On the Integrations page, select Integration with AWS CodePipeline. This will allow you to have an automated security scan inside your CI/CD pipeline.

Figure 6: CodeGuru integrations
Next, choose Open template in CloudFormation to create a CodeBuild project to allow discovery of your repositories and run security scans.

Figure 7: CodeGuru and CodePipeline integration
The CloudFormation template is already entered. Select the acknowledge box, and then choose Create stack.

Figure 8: CloudFormation quick create stack
If you already have a pipeline integration, go to Step 2 and select CodePipeline console. If this is your first time using CodePipeline, this blog post explains how to integrate it with AWS CI/CD services.

Figure 9: Integrate with AWS CodePipeline
Choose Edit.

Figure 10: CodePipeline with CodeGuru integration
Choose Add stage.

Figure 11: Add Stage in CodePipeline
On the Edit action page:
1. Enter a stage name.
2. For the stage you just created, choose Add action group.
3. For Action provider, select CodeBuild.
4. For Input artifacts, select SourceArtifact.
5. For Project name, select CodeGuruSecurity.
6. Choose Done, and then choose Save.
Figure 12: Add action group

Test CodeGuru Security

You have now created a security check stage for your CI/CD pipeline. To test the pipeline, choose Release change.

Figure 13: CodePipeline with successful security scan

If your code was successfully scanned, you will see Succeeded in the Most recent execution column for your pipeline.

Figure 14: CodePipeline dashboard with successful security scan

Findings

To analyze the findings of your scan, select Findings under Security, and you will see the findings for the scans whether manually done or through integrations. Each finding will show the vulnerability, the scan it belongs to, the severity level, the status of an open case or closed case, the age, and the time of detection.

Figure 15: Findings inside CodeGuru security

Dashboard

To view a summary of the insights and findings from your scan, select Dashboard, under Security, and you will see high level summary of your findings overview and a vulnerability fix overview.

Figure 16:Findings inside CodeGuru dashboard

Amazon Inspector

Your journey with the shift-left model extends beyond code deployment. After scanning your code repositories and using tools like CodeWhisperer and CodeGuru Security to proactively reduce security risks before code commits to a repository, your code might still encounter potential vulnerabilities after being deployed to production. For instance, faulty software updates can introduce risks to your application. Continuous vigilance and monitoring after deployment are crucial.

This is where Amazon Inspector offers ongoing assessment throughout your resource lifecycle, automatically rescanning resources in response to changes. Amazon Inspector seamlessly complements the shift-left model by identifying vulnerabilities as your workload operates in a production environment.

Amazon Inspector continuously scans various components, including Amazon EC2, Lambda functions, and container workloads, seeking out software vulnerabilities and inadvertent network exposure. Its user-friendly features include enablement in a few clicks, continuous and automated scanning, and robust support for multi-account environments through AWS Organizations. After activation, it autonomously identifies workloads and presents real-time coverage details, consolidating findings across accounts and resources.

Distinguishing itself from traditional security scanning software, Amazon Inspector has minimal impact on your fleet’s performance. When vulnerabilities or open network paths are uncovered, it generates detailed findings, including comprehensive information about the vulnerability, the affected resource, and recommended remediation. When you address a finding appropriately, Amazon Inspector autonomously detects the remediation and closes the finding.

The findings you receive are prioritized according to a contextualized Inspector risk score, facilitating prompt analysis and allowing for automated remediation.

Additionally, Amazon Inspector provides robust management APIs for comprehensive programmatic access to the Amazon Inspector service and resources. You can also access detailed findings through Amazon EventBridge and seamlessly integrate them into AWS Security Hub for a comprehensive security overview.

Scan workloads with Amazon Inspector

Use the following examples to learn how to use Amazon Inspector to scan AWS workloads.

Open the Amazon Inspector console in your AWS Organizations management account. In the navigation pane, select Activate Inspector.
Under Delegated administrator, enter the account number for your desired account to grant it all the permissions required to manage Amazon Inspector for your organization. Consider using your Security Tooling account as delegated administrator for Amazon Inspector. Choose Delegate. Then, in the confirmation window, choose Delegate again. When you select a delegated administrator, Amazon Inspector is activated for that account. Now, choose Activate Inspector to activate the service in your management account.

Figure 17: Set the delegated administrator account ID for Amazon Inspector
You will see a green success message near the top of your browser window and the Amazon Inspector dashboard, showing a summary of data from the accounts.

Figure 18: Amazon Inspector dashboard after activation

Explore Amazon Inspector

From the Amazon Inspector console in your delegated administrator account, in the navigation pane, select Account management. Because you’re signed in as the delegated administrator, you can enable and disable Amazon Inspector in the other accounts that are part of your organization. You can also automatically enable Amazon Inspector for new member accounts.

Figure 19: Amazon Inspector account management dashboard
In the navigation pane, select Findings. Using the contextualized Amazon Inspector risk score, these findings are sorted into several severity ratings.
1. The contextualized Amazon Inspector risk score is calculated by correlating CVE information with findings such as network access and exploitability.
2. This score is used to derive severity of a finding and prioritize the most critical findings to improve remediation response efficiency.
Figure 20: Findings in Amazon Inspector sorted by severity (default)

When you enable Amazon Inspector, it automatically discovers all of your Amazon EC2 and Amazon ECR resources. It scans these workloads to detect vulnerabilities that pose risks to the security of your compute workloads. After the initial scan, Amazon Inspector continues to monitor your environment. It automatically scans new resources and re-scans existing resources when changes are detected. As vulnerabilities are remediated or resources are removed from service, Amazon Inspector automatically updates the associated security findings.

In order to successfully scan EC2 instances, Amazon Inspector requires inventory collected by AWS Systems Manager and the Systems Manager agent. This is installed by default on many EC2 instances. If you find some instances aren’t being scanned by Amazon Inspector, this might be because they aren’t being managed by Systems Manager.
Select a findings title to see the associated report.
1. Each finding provides a description, severity rating, information about the affected resource, and additional details such as resource tags and how to remediate the reported vulnerability.
2. Amazon Inspector stores active findings until they are closed by remediation. Findings that are closed are displayed for 30 days.
Figure 21: Amazon Inspector findings report details

Integrate CodeGuru Security with Amazon Inspector to scan Lambda functions

Amazon Inspector and CodeGuru Security work harmoniously together. CodeGuru Security is available through Amazon Inspector Lambda code scanning. After activating Lambda code scanning, you can configure automated code scans to be performed on your Lambda functions.

Use the following steps to configure Amazon CodeGuru Security with Amazon Inspector Lambda code scanning to evaluate Lambda functions.

Open the Amazon Inspector console and select Account management from the navigation pane.
Select the AWS account you want to activate Lambda code scanning in.

Figure 22: Activating AWS Lambda code scanning from the Amazon Inspector Account management console
Choose Activate and select AWS Lambda code scanning.

With Lambda code scanning activated, security findings for your Lambda function code will appear in the All findings section of Amazon Inspector.

Amazon Inspector plays a crucial role in maintaining the highest security standards for your resources. Whether you’re installing a new package on an EC2 instance, applying a software patch, or when a new CVE affecting a specific resource is disclosed, Amazon Inspector can assist with quick identification and remediation.

Conclusion

Incorporating security at every stage of the software development lifecycle is paramount and requires that security be a consideration from the outset. Shifting left enables security teams to reduce overall application security risks.

Using these AWS services — Amazon CodeWhisperer, Amazon CodeGuru and Amazon Inspector — not only aids in early risk identification and mitigation, it empowers your development and security teams, leading to more efficient and secure business outcomes.

For further reading, check out the AWS Well Architected Security Pillar, the Generative AI on AWS page, and more blogs like this on the AWS Security Blog page.

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 Amazon CodeWhisperer re:Post forum or contact AWS Support.

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Building sensitive data remediation workflows in multi-account AWS environments

2023-11-15 Darren Roback

Post Syndicated from Darren Roback original https://aws.amazon.com/blogs/security/building-sensitive-data-remediation-workflows-in-multi-account-aws-environments/

The rapid growth of data has empowered organizations to develop better products, more personalized services, and deliver transformational outcomes for their customers. As organizations use Amazon Web Services (AWS) to modernize their data capabilities, they can sometimes find themselves with data spread across several AWS accounts, each aligned to distinct use cases and business units. This can present a challenge for security professionals, who need not only a mechanism to identify sensitive data types—such as protected health information (PHI), payment card industry (PCI), and personally identifiable information (PII), or organizational intellectual property—stored on AWS, but also the ability to automatically act upon these findings through custom logic that supports organizational policies and regulatory requirements.

In this blog post, we present a solution that provides you with visibility into sensitive data residing across a fleet of AWS accounts through a ChatOps-style notification mechanism using Microsoft Teams, which also provides contextual information needed to conduct security investigations. This solution also incorporates a decision logic mechanism to automatically quarantine sensitive data assets while they’re pending review, which can be tailored to meet unique organizational, industry, or regulatory environment requirements.

Prerequisites

Before you proceed, ensure that you have the following within your environment:

Access to a set of AWS accounts that have been joined to an organization with all features enabled.
Logically grouped AWS Organizations member accounts into organizational units (OUs).
Enabled AWS CloudFormation trusted access within AWS Organizations.
Enabled tag policies within AWS Organizations.
A designated security tooling account within AWS Organizations that’s dedicated to operating security services, monitoring AWS accounts, and automating security alerting and response, and whose access is restricted through AWS Identity and Access Management (IAM) to security professionals.
Permissions to create the resources listed below using CloudFormation.
A Microsoft Teams account with permissions to add apps and webhooks in your desired team and channel.

Assumptions

Things to know about the solution in this blog post:

This solution assumes that Amazon Simple Storage Service (Amazon S3) buckets in scope for sensitive data discovery and remediation are not enabled for S3 versioning. Amazon Macie supports sensitive data discovery of current object versions, and the solution presented here automatically quarantines current object versions determined to contain sensitive data. Additional customization of this solution might be required to allow or deny access to previous object versions and delete markers.
This solution assumes that you’ve set up your AWS accounts for IAM authentication, and the notifications presented to you in Microsoft Teams will reflect an IAM user authentication experience. If you’re using AWS IAM Identity Center with federated authentication, additional customization of this solution might be required.

Solution overview

The solution architecture and overall workflow are detailed in Figure 1 that follows.

Figure 1: Solution overview

Upon discovering sensitive data in member accounts, this solution selectively quarantines objects based on their finding severity and public status. This logic can be customized to evaluate additional details such as the finding type or number of sensitive data occurrences detected. The ability to adjust this workflow logic can provide a custom-tailored solution to meet a variety of industry use cases, helping you adhere to industry-specific security regulations and frameworks.

Figure 1 provides an overview of the components used in this solution, and for illustrative purposes we step through them here.

Automated scanning of buckets

Macie supports various scope options for sensitive data discovery jobs, including the use of bucket tags to determine in-scope buckets for scanning. Setting up automated scanning includes the use of an AWS Organizations tag policy to verify that the S3 buckets deployed in your chosen OUs conform to tagging requirements, an AWS Config job to automatically check that tags have been applied correctly, an Amazon EventBridge rule and bus to receive compliance events from a fleet of member accounts, and an AWS Lambda function to notify administrators of compliance change events.

An AWS Organizations tag policy verifies that the S3 buckets created in the in-scope AWS accounts have a tag structure that facilitates automated scanning and identification of the bucket owner. Specific tags enforced with this tag policy include RequireMacieScan : True|False and BucketOwner : <variable>. The tag policy is enforced at the OU level.
A custom AWS Config rule evaluates whether these tags have been applied to all in-scope S3 buckets, and marks resources as compliant or not compliant.
After every evaluation of S3 bucket tag compliance, AWS Config will send compliance events to an EventBridge event bus in the same AWS member account.
An EventBridge rule is used to send compliance messages from member accounts to a centralized security account, which is used for operational administration of the solution.
An EventBridge rule in the centralized security account is used to send all tag compliance events to a Lambda serverless function, which is used for notification.
The tag compliance notification function receives the tag compliance events from EventBridge, parses the information, and posts it to a Microsoft Teams webhook. This provides you with details such as the affected bucket, compliance status, and bucket owner, along with a link to investigate the compliance event in AWS Config.

Detecting sensitive data

Macie is a key component of this solution and facilitates sensitive data discovery across a fleet of AWS accounts based on the RequireMacieScan tag value configured at the time of bucket creation. Setting up sensitive data detection includes using Macie for sensitive data discovery, an EventBridge rule and bus to receive these finding events from the Macie delegated administrator account, an AWS Step Functions state machine to process these findings, and a Lambda function to notify administrators of new findings.

Macie is used to detect sensitive data in member account S3 buckets with job definitions based on bucket tags, and central reporting of findings through a Macie delegated administrator account (that is, the central security account).
When new findings are detected, Macie will send finding events to an EventBridge event bus in the security account.
An EventBridge rule is used to send all finding events to AWS Step Functions, which runs custom business logic to evaluate each finding and determine the next action to take on the affected object.
In the default configuration, for findings that are of low or medium severity and in objects that are not publicly accessible, Step Functions sends finding event information to a Lambda serverless function, which is used for notification. You can alter this event processing logic in Step Functions to change which finding types initiate an automated quarantine of the object.
The Macie finding notification function receives the finding events from Step Functions, parses this information, and posts it to a Microsoft Teams webhook. This presents you with details such as the affected bucket and AWS account, finding ID and severity, public status, and bucket owner, along with a link to investigate the finding in Macie.

Automated quarantine

As outlined in the previous section, this solution uses event processing decision logic in Step Functions to determine whether to quarantine a sensitive object in place. Setting up automated quarantine includes a Step Functions state machine for Macie finding processing logic, an Amazon DynamoDB table to track items moved to quarantine, a Lambda function to notify administrators of quarantine, and an Amazon API Gateway and second Step Functions state machine to facilitate remediation or removal of objects from quarantine.

In the default configuration for findings that are of high severity, or in objects that are publicly accessible, Step Functions adds the affected object’s metadata to an Amazon DynamoDB table, which is used to track quarantine and remediation status at scale.
Step Functions then quarantines the affected object, moving it to an automatically configured and secured prefix in the same S3 bucket while the finding is being investigated. Only select personnel (that is, cybersecurity) has access to the object.
Step Functions then sends finding event information to a Lambda serverless function, which is used for notification.
The Macie quarantine notification function receives the finding events from Step Functions, parses this information, and posts it to a Microsoft Teams webhook. This presents you with similar details to the Macie finding notification function, but also notes the object has been moved to quarantine, and provides a one-click option to release the object from quarantine.
In the Microsoft Teams channel, which should only be accessible to qualified security professionals, DLP administrators select the option to release the object from quarantine. This invokes a REST API deployed on API Gateway.
API Gateway invokes a release object workflow in Step Functions, which begins to process releasing the affected object from quarantine.
Step Functions inspects the affected object ID received through API Gateway, and first retrieves details about this object from DynamoDB. Upon receiving these details, the object is removed from the quarantine tracking database.
Step Functions then moves the affected object to its original location in Amazon S3, thereby making the object accessible again under the original bucket policy and IAM permissions.

Organization structure

As mentioned in the prerequisites, the solution uses a set of AWS accounts that have been joined to an organization, which is shown in Figure 2. While the logical structure of your AWS Organizations deployment can differ from what’s shown, for illustration purposes, we’re looking for sensitive data in the Development and Production AWS accounts, and the examples shown throughout the remainder of this blog post reflect that.

Figure 2: Organization structure

Deploy the solution

The overall deployment process of this solution has been decomposed into three AWS CloudFormation templates to be deployed to your management, security, and member accounts as CloudFormation stacks and StackSets, respectively. Performing the deployment in this manner not only verifies that the solution is extended to other member accounts created after the initial solution deployment, but also serves as an illustrative aid of the components deployed in each portion of the solution. An overview of the deployment process is as follows:

Set up of Microsoft Teams webhooks to receive information from this solution.
Deployment of a CloudFormation stack to the management account to configure the tag policy for this solution.
Deployment of a CloudFormation stack set to member accounts to enable monitoring of S3 bucket tags and forwarding of tag compliance events to the security account.
Deployment of a CloudFormation stack to the security account to configure the remainder of the solution that will facilitate sensitive data discovery, finding event processing, administrator notification, and release from quarantine functionality.
Remaining manual configuration of quarantine remediation API authorization, enabling Macie, and specifying a Macie results location.

Set up Microsoft Teams

Before deploying the solution, you must create two incoming webhooks in a Microsoft Teams channel of your choice. Due to the sensitivity of the event information provided and the ability to release objects from quarantine, we recommend that this channel only be accessible to information security professionals. In addition, we recommend creating two distinct webhooks to distinguish tag compliance events from finding events and have named the webhooks in the examples S3-Tag-Compliance-Notification and Macie-Finding-Notification, respectively. A complete walkthrough of creating an incoming webhook is out of scope for this blog post, but you can access Microsoft’s documentation on creating incoming webhooks for an overview. After the webhooks have been created, save the URLs, to use in the solution deployment process.

Configure the management account

The first step of the deployment process is to deploy a CloudFormation stack in your management account that creates an AWS Organizations tag policy and applies it to the OUs of your choice. Before performing this step, note the two OU IDs you will apply the policy to, as these will be captured as input parameters for the CloudFormation stack.

Choose the following Launch Stack button to open the CloudFormation console pre-loaded with the template for this step:

Note: The stack will launch in the N. Virginia (us-east-1) Region. To deploy this solution into other AWS Regions, change your regional selection in the CloudFormation console, and deploy it to the selected Region.
For Stack name, enter ConfigureManagementAccount.
For First AWS Organizations Unit ID, enter your first OU ID.
For Second AWS Organizations Unit ID, enter your second OU ID.
Choose Next.

Figure 3: Management account stack details

After you’ve entered all details, launch the stack and wait until the stack has reached CREATE_COMPLETE status before proceeding. The deployment process will take 1–2 minutes.

Configure the member accounts

The next step of the deployment process is to deploy a CloudFormation Stack, which will initiate a StackSet deployment from your management account that’s scoped to the OUs of your choice. This stack set will enable AWS Config along with an AWS Config rule to evaluate Amazon S3 tag compliance and will deploy an EventBridge rule to send compliance events from AWS Config in your member accounts to a centralized event bus in your security account. If AWS Config has previously been enabled, select True for the AWS Config Status parameter to help prevent an overwrite of your existing settings. Prior to performing this setup, note the two AWS Organizations OU IDs you will deploy the stack set to. You will also be prompted to enter the AWS account ID and Region of your security account.

Choose the following Launch Stack button to open the CloudFormation console pre-loaded with the template for this step:

Note: The stack will launch in the N. Virginia (us-east-1) Region. To deploy this solution into other AWS Regions, change your regional selection in the CloudFormation console, and deploy it to the selected Region.
For Stack name, enter DeployToMemberAccounts.
For First AWS Organizations Unit ID, enter your first OU ID.
For Second AWS Organizations Unit ID, enter your second OU ID.
For Deployment Region, enter the Region you want to deploy the Stack set to.
For AWS Config Status, accept the default value of false if you have not previously enabled AWS Config in your accounts.
For Support all resource types, accept the default value of false.
For Include global resource types, accept the default value of false.
For List of resource types if not all supported, accept the default value of AWS::S3::Bucket.
For Configuration delivery channel name, accept the default value of <Generated>.
For Snapshot delivery frequency, accept the default value of 1 hour.
For Security account ID, enter your security account ID.
For Security account region, select the Region of your security account.
Choose Next.

Figure 4: Member account Stack details

After you’ve entered all details, launch the stack and wait until the stack has reached CREATE_COMPLETE status before proceeding. The deployment process will take 3–5 minutes.

Configure the security account

The next step of the deployment process involves deploying a CloudFormation stack in your security account that creates all the resources needed for at-scale sensitive data detection, automated quarantine, and security professional notification and response. This stack configures the following:

An S3 bucket and AWS Key Management Service (AWS KMS) keys for storage and encryption of discovery results in Macie.
Two rules in EventBridge for routing tag compliance and Macie finding events.
Two Step Functions state machines whose logic will be used for automated object quarantine and release.
Three Lambda functions for tag compliance, Macie findings, and quarantine notification.
A DynamoDB table for quarantine status tracking.
An API Gateway REST endpoint to facilitate the release of objects from quarantine.

Before performing this setup, note your AWS Organizations ID and two Microsoft Teams webhook URLs previously configured.

Choose the following Launch Stack button to open the CloudFormation console pre-loaded with the template for this step:

Note: The stack will launch in the N. Virginia (us-east-1) Region. To deploy this solution into other AWS Regions, change your regional selection in the CloudFormation console, and deploy it to the selected Region.
For Stack name, enter ConfigureSecurityAccount.
For AWS Org ID, enter your AWS Organizations ID.
For Webhook URI for S3 tag compliance notifications, enter the first webhook URL you created in Microsoft Teams.
For Webhook URI for Macie finding and quarantine notifications, enter the second webhook URL you created in Microsoft Teams.
Choose Next.

Figure 5: Security account stack details

After you’ve entered all details, launch the stack and wait until the stack has reached CREATE_COMPLETE status before proceeding. The deployment process will take 3–5 minutes.

Remaining configuration

While most of the solution is deployed automatically using CloudFormation, there are a few items that you must configure manually.

Configure Lambda API key environment variable

When the CloudFormation stack was deployed to the security account, CloudFormation created a new REST API for security professionals to release objects from quarantine. This API was configured with an API key to be used for authorization, and you must retrieve the value of this API key and set it as an environment variable in your MacieQuarantineNotification function, which also deployed in the security account. To retrieve the value of this API key, navigate to the REST API created in the security account, select API Keys, and retrieve the value of APIKey1. Next, navigate to the MacieQuarantineNotification function in the Lambda console, and set the ReleaseObjectApiKey environment variable to the value of your API key.

Enable Macie

Next, you must enable Macie to facilitate sensitive data discovery in selected accounts in your organization, and this process begins with the selection of a delegated administrator account (that is, the security account), followed by onboarding the member accounts you want to test with. See Integrating and configuring an organization in Amazon Macie for detailed instructions on enabling Macie in your organization.

Configure the Macie results bucket and KMS key

Macie creates an analysis record for each Amazon S3 object that it analyzes. This includes objects where Macie has detected sensitive data as well as objects where sensitive data was not detected or that Macie could not analyze. The CloudFormation stack deployed in the security account created an S3 bucket and KMS key for this, and they are noted as MacieResultsS3BucketName and MacieResultsKmsKeyAlias in the CloudFormation stack output. Use these resources to configure the Macie results bucket and KMS key in the security account according to Storing and retaining sensitive data discovery results with Amazon Macie. Customization of the S3 bucket policy or KMS key policy has already been done for you as part of the ConfigureSecurityAccount CloudFormation template deployed earlier.

Validate the solution

With the solution fully deployed, you now need to deploy an S3 bucket with sample data to test the solution and review the findings.

Create a member account S3 bucket

In any of the member accounts onboarded into this solution as part of the Configure the member accounts step, deploy a new S3 bucket and the KMS key used to encrypt the bucket using the CloudFormation template that follows. Before performing this step, note the InvestigateMacieFindingsRole, StepFunctionsProcessFindingsRole, and StepFunctionsReleaseObjectRole outputs from the CloudFormation template deployed to the security account, as these will be captured as input parameters for the CloudFormation stack.

Choose the following Launch Stack button to open the CloudFormation console pre-loaded with the template for this step:

Note: The stack will launch in the N. Virginia (us-east-1) Region. To deploy this solution into other AWS Regions, change your regional selection in the CloudFormation console, and deploy it to the selected Region.
For Stack name, enter DeployS3BucketKmsKey.
For IAM Role used by Step Functions to process Macie findings, enter the ARN that was provided to you as the StepFunctionsProcessFindingsRole output from the Configure security account step.
For IAM Role used by Step Functions to release objects from quarantine, enter the ARN that was provided to you as the StepFunctionsReleaseObjectRole output from the Configure security account step.
For IAM Role used by security professionals to investigate Macie findings, enter the ARN that was provided to you as the InvestigateMacieFindingsRole output from the Configure security account step.
For Department name of the bucket owner, enter any department or team name you want to designate as having ownership responsibility over the S3 bucket.
Choose Next.

Figure 6: Deploy S3 bucket and KMS key stack details

After you’ve entered all details, launch the stack and wait until the stack has reached CREATE_COMPLETE status before proceeding. The deployment process will take 3–5 minutes.

Monitor S3 bucket tag compliance

Shortly after the deployment of the new S3 bucket, you should see a message in your Microsoft Teams channel notifying you of the tag compliance status of the new bucket. This AWS Config rule is evaluated automatically any time an S3 resource event takes place, and the tag compliance event is sent to the centralized security account for notification purposes. While the notification shown in Figure 7 depicts a compliant S3 bucket, a bucket deployed without the required tags will be marked as NON_COMPLIANT, and security professionals can check the AWS Config compliance status directly in the AWS console for the member account.

Figure 7: S3 tag compliance notification

Upload sample data

Download this .zip file of sample data and upload the expanded files into the newly created S3 bucket. The sample files include fictitious PII, including credit card information and social security numbers, and so will invoke various Macie findings.

Note: All data in this blog post has been artificially created by AWS for demonstration purposes and has not been collected from any individual person. Similarly, such data does not, nor is it intended, to relate back to any individual person.

Configure a Macie discovery job

Configure a sensitive data discovery job in the Amazon Macie delegated administrator account (that is, the security account) according to Creating a sensitive data discovery job. When creating the job, specify tag-based bucket criteria instructing Macie to scan any bucket with a tag key of RequireMacieScan and a tag value of True. This instructs Macie to scan buckets matching this criterion across the accounts that have been onboarded into Macie.

Figure 8: Macie bucket criteria

On the discovery options page, specify a one-time job with a sampling depth of 100 percent. Further refine the job scope by adding the quarantine prefix to the exclusion criteria of the sensitive data discovery job.

Figure 9: Macie scan scope

Select the AWS_CREDENTIALS, CREDIT_CARD_NUMBER, CREDIT_CARD_SECURITY_CODE, and DATE_OF_BIRTH managed data identifiers and proceed to the review screen. On the review screen, ensure that the bucket name you created is listed under bucket matches, and launch the discovery job.

Note: This solution also works with the Automated Sensitive Data Discovery feature in Amazon Macie. I recommend you investigate this feature further for broad visibility into where sensitive data might reside within your Amazon S3 data estate. Regardless of the method you choose, you will be able to integrate the appropriate notification and quarantine solution.

Review and investigate findings

After a few minutes, the discovery job will complete and soon you should see four messages in your Microsoft teams channel notifying you of the finding events created by Macie. One of these findings will be marked as medium severity, while the other three will be marked as high.

Review the medium severity finding, and recall the flow of event information from the solution overview section. Macie scanned a bucket in the member account and presented this finding in the Macie delegated administrator account. Macie then sent this finding event to EventBridge, which initiated a workflow run in Step Functions. Step Functions invoked customer-specified logic to evaluate the finding and determined that because the object isn’t publicly accessible, and because the finding isn’t high severity, it should only notify security professionals rather than quarantine the object in question. Several key pieces of information necessary for investigation are presented to the security team, along with a link to directly investigate the finding in the AWS console of the central security account.

Figure 10: Macie medium severity finding notification

Now review the high severity finding. The flow of event information in this scenario is identical to the medium severity finding, but in this case, Step Functions quarantined the object because the severity is high. The security team is again presented with an option to use the console to investigate further. The process to investigate this finding is a bit different due to the object being moved to a quarantine prefix. If security professionals want to view the original object in its entirety, they would assume the InvestigateMacieFindingsRole in the security account, which has cross-account access to the S3 bucket quarantine prefix in the in-scope member accounts. S3 buckets deployed in member accounts using the CloudFormation template listed above will have a special bucket policy that denies access to the quarantine prefix for any role other than the InvestigateMacieFindingsRole, StepFunctionsProcessFindingsRole, and StepFunctionsReleaseObjectRole. This makes sure that objects are truly quarantined and inaccessible while being investigated by security professionals.

Figure 11: Macie high severity finding notification

Unlike the previous example, the security team is also notified that an affected object was moved to quarantine, and is presented with a separate option to release the object from quarantine. Choosing Release Object from Quarantine runs an HTTP POST to the REST API transparently in the background, and the API responds with a SUCCEEDED or FAILED message indicating the result of the operation.

Figure 12: Release object from quarantine

The state machine uses decision logic based on the affected object’s public status and the severity of the finding. Customers deploying this solution can choose to alter this logic or add additional customization by altering the Step Functions state machine definition either directly in the CloudFormation template or through the Step Functions Workflow Studio low-code interface available in the Step Functions console. For reference, the full event schema used by Macie can be found in the Eventbridge event schema for Macie findings.

The logic of the Step Functions state machine used to process Macie finding events follows and is shown in Figure 13.

An EventBridge rule invokes the Step Functions state machine as Macie findings are received.
Step Functions parses Macie event data to determine the finding severity.
If the finding is high severity or determined to be public, the affected object is then added to the quarantine tracking database in DynamoDB.
After adding the object to quarantine tracking, the object is copied into the quarantine prefix within its S3 bucket.
After being copied, the object is deleted from its original S3 location.
After the object is deleted, the MacieQuarantineNotification function is invoked to alert you of the finding and quarantine status.
If the finding is not high severity and not determined to be public, the MacieFindingNotification function is invoked to alert you of the finding.

Figure 13: Process Macie findings workflow

Solution cleanup

To remove the solution and avoid incurring additional charges for the AWS resources used in this solution, perform the following steps.

Note: If you want to only suspend Macie, which will preserve your settings, resources, and findings, see Suspending or disabling Amazon Macie.

Open the Macie console in your security account. Under Settings, choose Accounts. Select the checkboxes next to the member accounts onboarded previously, select the Actions dropdown, and select Disassociate Account. When that has completed, select the same accounts again, and choose Delete.
Open the Macie console in your management account. Click on Get started, and remove your security account as the Macie delegated administrator.
Open the Macie console in your security account, choose Settings, then choose Disable Macie.
Open the S3 console in your security account. Remove all objects from the Macie results S3 bucket.
Open the CloudFormation console in your security account. Select the ConfigureSecurityAccount stack and choose Delete.
Open the Macie console in your member accounts. Under Settings, choose Disable Macie.
Open the Amazon S3 console in your member accounts. Remove all sample data objects from the S3 bucket.
Open the CloudFormation console in your member accounts. Select the DeployS3BucketKmsKey stack and choose Delete.
Open the CloudFormation console in your management account. Select the DeployToMemberAccounts stack and choose Delete.
Still in the CloudFormation console in your management account, select the ConfigureManagementAccount stack and choose Delete.

Summary

In this blog post, we demonstrated a solution to process—and act upon—sensitive data discovery findings surfaced by Macie through the incorporation of decision logic implemented in Step Functions. This logic provides granularity on quarantine decisions and extensibility you can use to customize automated finding response to suit your business and regulatory needs.

In addition, we demonstrated a mechanism to notify you of finding event information as it’s discovered, while providing you with the contextual details necessary for investigative purposes. Additional customization of the information presented in Microsoft Teams can be accomplished through parsing of the event payload. You can also customize the Lambda functions to interface with Slack, as demonstrated in this sample solution for Macie.

Finally, we demonstrated a solution that can operate at-scale, and will automatically be deployed to new member accounts in your organization. By using an in-place quarantine strategy instead of one that is centralized, you can more easily manage this solution across potentially hundreds of AWS accounts and thousands of S3 buckets. By incorporating a global tracking database in DynamoDB, this solution can also be enhanced through a visual dashboard depicting quarantine insights.

If you have feedback about this post, let us know in the comments section. If you have questions about this post, start a new thread on AWS Security, Identity, & Compliance re:Post or contact AWS Support.

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AWS Speaker Profile: Zach Miller, Senior Worldwide Security Specialist Solutions Architect

2023-11-15 Roger Park

Post Syndicated from Roger Park original https://aws.amazon.com/blogs/security/aws-speaker-profile-zach-miller-senior-worldwide-security-specialist-solutions-architect/

In the AWS Speaker Profile series, we interview Amazon Web Services (AWS) thought leaders who help keep our customers safe and secure. This interview features Zach Miller, Senior Worldwide Security Specialist SA and re:Invent 2023 presenter of Securely modernize payment applications with AWS and Centrally manage application secrets with AWS Secrets Manager. Zach shares thoughts on the data protection and cloud security landscape, his unique background, his upcoming re:Invent sessions, and more.

How long have you been at AWS?

I’ve been at AWS for more than four years, and I’ve enjoyed every minute of it! I started as a consultant in Professional Services, and I’ve been a Security Solutions Architect for around three years.

How do you explain your job to your non-tech friends?

Well, my mother doesn’t totally understand my role, and she’s been known to tell her friends that I’m the cable company technician that installs your internet modem and router. I usually tell my non-tech friends that I help AWS customers protect their sensitive data. If I mention cryptography, I typically only get asked questions about cryptocurrency—which I’m not qualified to answer. If someone asks what cryptography is, I usually say it’s protecting data by using mathematics.

How did you get started in data protection and cryptography? What about it piqued your interest?

I originally went to school to become a network engineer, but I discovered that moving data packets from point A to point B wasn’t as interesting to me as securing those data packets. Early in my career, I was an intern at an insurance company, and I had a mentor who set up ethnical hacking lessons for me—for example, I’d come into the office and he’d have a compromised workstation preconfigured. He’d ask me to do an investigation and determine how the workstation was compromised and what could be done to isolate it and collect evidence. Other times, I’d come in and find my desk cabinets were locked with a padlock, and he wanted me to pick the lock. Security is particularly interesting because it’s an ever-evolving field, and I enjoy learning new things.

What’s been the most dramatic change you’ve seen in the data protection landscape?

One of the changes that I’ve been excited to see is an emphasis on encrypting everything. When I started my career, we’d often have discussions about encryption in the context of tradeoffs. If we needed to encrypt sensitive data, we’d have a conversation with application teams about the potential performance impact of encryption and decryption operations on their systems (for example, their databases), when to schedule downtime for the application to encrypt the data or rotate the encryption keys protecting the data, how to ensure the durability of their keys and make sure they didn’t lose data, and so on.

When I talk to customers about encryption on AWS today—of course, it’s still useful to talk about potential performance impact—but the conversation has largely shifted from “Should I encrypt this data?” to “How should I encrypt this data?” This is due to services such as AWS Key Management Service (AWS KMS) making it simpler for customers to manage encryption keys and encrypt and decrypt data in their applications with minimal performance impact or application downtime. AWS KMS has also made it simple to enable encryption of sensitive data—with over 120 AWS services integrated with AWS KMS, and services such as Amazon Simple Storage Service (Amazon S3) encrypting new S3 objects by default.

You are a frequent contributor to the AWS Security Blog. What were some of your recent posts about?

My last two posts covered how to use AWS Identity and Access Management (IAM) condition context keys to create enterprise controls for certificate management and how to use AWS Secrets Manager to securely manage and retrieve secrets in hybrid or multicloud workloads. I like writing posts that show customers how to use a new feature, or highlight a pattern that many customers ask about.

You are speaking in a couple of sessions at AWS re:Invent; what will your sessions focus on? What do you hope attendees will take away from your session?

I’m delivering two sessions at re:Invent this year. The first is a chalk talk, Centrally manage application secrets with AWS Secrets Manager (SEC221), that I’m delivering with Ritesh Desai, who is the General Manager of Secrets Manager. We’re discussing how you can securely store and manage secrets in your workloads inside and outside of AWS. We will highlight some recommended practices for managing secrets, and answer your questions about how Secrets Manager integrates with services such as AWS KMS to help protect application secrets.

The second session is also a chalk talk, Securely modernize payment applications with AWS (SEC326). I’m delivering this talk with Mark Cline, who is the Senior Product Manager of AWS Payment Cryptography. We will walk through an example scenario on creating a new payment processing application. We will discuss how to use AWS Payment Cryptography, as well as other services such as AWS Lambda, to build a simple architecture to help process and secure credit card payment data. We will also include common payment industry use cases such as tokenization of sensitive data, and how to include basic anti-fraud detection, in our example app.

What are you currently working on that you’re excited about?

My re:Invent sessions are definitely something that I’m excited about. Otherwise, I spend most of my time talking to customers about AWS Cryptography services such as AWS KMS, AWS Secrets Manager, and AWS Private Certificate Authority. I also lead a program at AWS that enables our subject matter experts to create and publish videos to demonstrate new features of AWS Security Services. I like helping people create videos, and I hope that our videos provide another mechanism for viewers who prefer information in a video format. Visual media can be more inclusive for customers with certain disabilities or for neurodiverse customers who find it challenging to focus on written text. Plus, you can consume videos differently than a blog post or text documentation. If you don’t have the time or desire to read a blog post or AWS public doc, you can listen to an instructional video while you work on other tasks, eat lunch, or take a break. I invite folks to check out the AWS Security Services Features Demo YouTube video playlist.

Is there something you wish customers would ask you about more often?

I always appreciate when customers provide candid feedback on our services. AWS is a customer-obsessed company, and we build our service roadmaps based on what our customers tell us they need. You should feel comfortable letting AWS know when something could be easier, more efficient, or less expensive. Many customers I’ve worked with have provided actionable feedback on our services and influenced service roadmaps, just by speaking up and sharing their experiences.

How about outside of work, any hobbies?

I have two toddlers that keep me pretty busy, so most of my hobbies are what they like to do. So I tend to spend a lot of time building elaborate toy train tracks, pushing my kids on the swings, and pretending to eat wooden toy food that they “cook” for me. Outside of that, I read a lot of fiction and indulge in binge-worthy TV.

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

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New – AWS Audit Manager now supports first third-party GRC integration

2023-11-14 Veliswa Boya

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/new-aws-audit-manager-now-supports-first-third-party-grc-integration/

Auditing is a continuous and ongoing process, and every audit includes the collection of evidence. The evidence gathered helps confirm the state of resources and it’s used to demonstrate that the customer’s policies, procedures, and activities (controls), are in place, and that the control has been operational for a specified period of time. AWS Audit Manager already automates this evidence collection for AWS usage. However, large enterprise organizations who deploy their workloads across a range of locations such as cloud, on-premises, or a combination of both, manage this evidence data using a combination of third-party or homegrown tools, spreadsheets, and emails.

Today we’re excited to announce the integration of AWS Audit Manager with third party Governance, Risk, and Compliance (GRC) provider, MetricStream CyberGRC, an AWS Partner with GRC capabilities. This integration allows enterprises to manage compliance across AWS, on-premises, and other cloud environments in a centralized GRC environment.

Before this announcement, Audit Manager operated only in the AWS context, allowing customers to collect compliance evidence for resources in AWS. They would then relay that information to their GRC systems external to AWS for additional aggregation and analysis. This process left customers without an automated way to monitor and evaluate all compliance data in one centralized location, resulting in delays to compliance outcomes.

The GRC integration with Audit Manager allows you to use audit evidence collected by Audit Manager directly in MetricStream CyberGRC. Audit Manager now receives the controls in scope from MetricStream CyberGRC, collects evidence around these controls, and exports the data related to the audit into MetricStream CyberGRC for aggregation and analysis. You will now have aggregated compliance, real-time monitoring and centralized reporting. This will reduce compliance fatigue and improve stakeholder collaboration.

How It Works
Using Amazon Cognito User Pools, you’ll be onboarded into the multi-tenant instance of MetricStream CyberGRC.

Amazon Cognito User Pools

Once onboarded, you’ll be able to view AWS assets and frameworks inside MetricStream CyberGRC. You can then begin by choosing the suitable Audit Manager framework to define the relationships between your existing enterprise controls and AWS controls. After creating this one-time control mapping, you can define the accounts in scope to create an assessment that MetricStream CyberGRC will manage in AWS Audit Manager on your behalf. This assessment triggers AWS Audit Manager to collect evidence in context of the mapped controls. As a result, you get a unified view of compliance evidence inside your GRC application. Any standard controls that you have in Audit Manager will be provided to MetricStream CyberGRC by using the GetControl API to facilitate manual mapping process wherever automated mapping fails or does not suffice. The EvidenceFinder API will send bulk evidence from Audit Manager to MetricStream CyberGRC.

Available Now
This feature is available today where Audit Manager (AWS Regions) and MetricStream CyberGRC are both available. There are no additional AWS Audit Manager charges for using this integration. To use this integration, please reach out to MetricStream for information about access and purchase of MetricStream CyberGRC software.

As part of the AWS Free Tier, AWS Audit Manager offers a free tier for first-time customers. The free tier will expire in two calendar months after the first subscription. For more information, see AWS Audit Manager pricing. To learn more about AWS Audit Manager integration with MetricStream CyberGRC, see Audit Manager documentation.

– Veliswa

AWS Security Profile: Tom Scholl, VP and Distinguished Engineer, AWS

2023-11-14 Tom Scholl

Post Syndicated from Tom Scholl original https://aws.amazon.com/blogs/security/aws-security-profile-tom-scholl-vp-and-distinguished-engineer-aws/

In the AWS Security Profile series, we feature the people who work in Amazon Web Services (AWS) Security and help keep our customers safe and secure. This interview is with Tom Scholl, VP and Distinguished Engineer for AWS.

What do you do in your current role and how long have you been at AWS?

I’m currently a vice president and distinguished engineer in the infrastructure organization at AWS. My role includes working on the AWS global network backbone, as well as focusing on denial-of-service detection and mitigation systems. I’ve been with AWS for over 12 years.

What initially got you interested in networking and how did that lead you to the anti-abuse and distributed denial of service (DDoS) space?

My interest in large global network infrastructure started when I was a teenager in the 1990s. I remember reading a magazine at the time that cataloged all the large IP transit providers on the internet, complete with network topology maps and sizes of links. It inspired me to want to work on the engineering teams that supported that. Over time, I was fortunate enough to move from working at a small ISP to a telecom carrier where I was able to work on their POP and backbone designs. It was there that I learned about the internet peering ecosystem and started collaborating with network operators from around the globe.

For the last 20-plus years, DDoS was always something I had to deal with to some extent. Namely, from the networking lens of preventing network congestion through traffic-engineering and capacity planning, as well as supporting the integration of DDoS traffic scrubbers into network infrastructure.

About three years ago, I became especially intrigued by the network abuse and DDoS space after using AWS network telemetry to observe the size of malicious events in the wild. I started to be interested in how mitigation could be improved, and how to break down the problem into smaller pieces to better understand the true sources of attack traffic. Instead of merely being an observer, I wanted to be a part of the solution and make it better. This required me to immerse myself into the domain, both from the perspective of learning the technical details and by getting hands-on and understanding the DDoS industry and environment as a whole. Part of how I did this was by engaging with my peers in the industry at other companies and absorbing years of knowledge from them.

How do you explain your job to your non-technical friends and family?

I try to explain both areas that I work on. First, that I help build the global network infrastructure that connects AWS and its customers to the rest of the world. I explain that for a home user to reach popular destinations hosted on AWS, data has to traverse a series of networks and physical cables that are interconnected so that the user’s home computer or mobile phone can send packets to another part of the world in less than a second. All that requires coordination with external networks, which have their own practices and policies on how they handle traffic. AWS has to navigate that complexity and build and operate our infrastructure with customer availability and security in mind. Second, when it comes to DDoS and network abuse, I explain that there are bad actors on the internet that use DDoS to cause impairment for a variety of reasons. It could be someone wanting to disrupt online gaming, video conferencing, or regular business operations for any given website or company. I work to prevent those events from causing any sort of impairment and trace back the source to disrupt that infrastructure launching them to prevent it from being effective in the future.

Recently, you were awarded the J.D. Falk Award by the Messaging Malware Mobile Anti-Abuse Working Group (M³AAWG) for IP Spoofing Mitigation. Congratulations! Please tell us more about the efforts that led to this.

Basically, there are three main types of DDoS attacks we observe: botnet-based unicast floods, spoofed amplification/reflection attacks, and proxy-driven HTTP request floods. The amplification/reflection aspect is interesting because it requires DDoS infrastructure providers to acquire compute resources behind providers that permit IP spoofing. IP spoofing itself has a long history on the internet, with a request for comment/best current practice (RFC/BCP) first written back in 2000 recommending that providers prevent this from occurring. However, adoption of this practice is still spotty on the internet.

At NANOG76, there was a proposal that these sorts of spoofed attacks could be traced by network operators in the path of the pre-amplification/reflection traffic (before it bounced off the reflectors). I personally started getting involved in this effort about two years ago. AWS operates a large global network and has network telemetry data that would help me identify pre-amplification/reflection traffic entering our network. This would allow me to triangulate the source network generating this. I then started engaging various networks directly that we connect to and provided them timestamps, spoofed source IP addresses, and specific protocols and ports involved with the traffic, hoping they could use their network telemetry to identify the customer generating it. From there, they’d engage with their customer to get the source shutdown or, failing that, implement packet filters on their customer to prevent spoofing.

Initially, only a few networks were capable of doing this well. This meant I had to spend a fair amount of energy in educating various networks around the globe on what spoofed traffic is, how to use their network telemetry to find it, and how to handle it. This was the most complicated and challenging part because this wasn’t on the radar of many networks out there. Up to this time, frontline network operations and abuse teams at various networks, including some very large ones, were not proficient in dealing with this.

The education I did included a variety of engagements, including sharing drawings with the day-in-the-life of a spoofed packet in a reflection attack, providing instructions on how to use their network telemetry tools, connecting them with their network telemetry vendors to help them, and even going so far as using other more exotic methods to identify which of their customers were spoofing and pointing out who they needed to analyze more deeply. In the end, it’s about getting other networks to be responsive and take action and, in the best cases, find spoofing on their own and act upon it.

Incredible! How did it feel accepting the award at the M³AAWG General Meeting in Brooklyn?

It was an honor to accept it and see some acknowledgement for the behind-the-scenes work that goes on to make the internet a better place.

What’s next for you in your work to suppress IP spoofing?

Continue tracing exercises and engaging with external providers. In particular, some of the network providers that experience challenges in dealing with spoofing and how we can improve their operations. Also, determining more effective ways to educate the hosting providers where IP spoofing is a common issue and making them implement proper default controls to not allow this behavior. Another aspect is being a force multiplier to enable others to spread the word and be part of the education process.

Looking ahead, what are some of your other goals for improving users’ online experiences and security?

Continually focusing on improving our DDoS defense strategies and working with customers to build tailored solutions that address some of their unique requirements. Across AWS, we have many services that are architected in different ways, so a key part of this is how do we raise the bar from a DDoS defense perspective across each of them. AWS customers also have their own unique architecture and protocols that can require developing new solutions to address their specific needs. On the disruption front, we will continue to focus on disrupting DDoS-as-a-service provider infrastructure beyond disrupting spoofing to disrupting botnets and the infrastructure associated with HTTP request floods.

With HTTP request floods being much more popular than byte-heavy and packet-heavy threat methods, it’s important to highlight the risks open proxies on the internet pose. Some of this emphasizes why there need to be some defaults in software packages to prevent misuse, in addition to network operators proactively identifying open proxies and taking appropriate action. Hosting providers should also recognize when their customer resources are communicating with large fleets of proxies and consider taking appropriate mitigations.

What are the most critical skills you would advise people need to be successful in network security?

I’m a huge proponent of being hands-on and diving into problems to truly understand how things are operating. Putting yourself outside your comfort zone, diving deep into the data to understand something, and translating that into outcomes and actions is something I highly encourage. After you immerse yourself in a particular domain, you can be much more effective at developing strategies and rapid prototyping to move forward. You can make incremental progress with small actions. You don’t have to wait for the perfect and complete solution to make some progress. I also encourage collaboration with others because there is incredible value in seeking out diverse opinions. There are resources out there to engage with, provided you’re willing to put in the work to learn and determine how you want to give back. The best people I’ve worked with don’t do it for public attention, blog posts, or social media status. They work in the background and don’t expect anything in return. They do it because of their desire to protect their customers and, where possible, the internet at large.

Lastly, if you had to pick an industry outside of security for your career, what would you be doing?

I’m over the maximum age allowed to start as an air traffic controller, so I suppose an air transport pilot or a locomotive engineer would be pretty neat.

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