Post Syndicated from Bryan Liston original https://aws.amazon.com/blogs/compute/saml-for-your-serverless-javascript-application-part-ii/

Contributors: Richard Threlkeld, Gene Ting, Stefano Buliani

The full code for both scenarios—including SAM templates—can be found at the samljs-serverless-sample GitHub repository. We highly recommend you use the SAM templates in the GitHub repository to create the resources, opitonally you can manually create them.

This is the second part of a two part series for using SAML providers in your application and receiving short-term credentials to access AWS Services. These credentials can be limited with IAM roles so the users of the applications can perform actions like fetching data from databases or uploading files based on their level of authorization. For example, you may want to build a JavaScript application that allows a user to authenticate against Active Directory Federation Services (ADFS). The user can be granted scoped AWS credentials to invoke an API to display information in the application or write to an Amazon DynamoDB table.

Part I of this series walked through a client-side flow of retrieving SAML claims and passing them to Amazon Cognito to retrieve credentials. This blog post will take you through a more advanced scenario where logic can be moved to the backend for a more comprehensive and flexible solution.

Prerequisites

As in Part I of this series, you need ADFS running in your environment. The following configurations are used for reference:

1. ADFS federated with the AWS console. For a walkthrough with an AWS CloudFormation template, see Enabling Federation to AWS Using Windows Active Directory, ADFS, and SAML 2.0.
2. Verify that you can authenticate with user example\bob for both the ADFS-Dev and ADFS-Production groups via the sign-in page.
3. Create an Amazon Cognito identity pool.

Scenario Overview

The scenario in the last blog post may be sufficient for many organizations but, due to size restrictions, some browsers may drop part or all of a query string when sending a large number of claims in the SAMLResponse. Additionally, for auditing and logging reasons, you may wish to relay SAML assertions via POST only and perform parsing in the backend before sending credentials to the client. This scenario allows you to perform custom business logic and validation as well as putting tracking controls in place.

In this post, we want to show you how these requirements can be achieved in a Serverless application. We also show how different challenges (like XML parsing and JWT exchange) can be done in a Serverless application design. Feel free to mix and match, or swap pieces around to suit your needs.

This scenario uses the following services and features:

• Cognito for unique ID generation and default role mapping
• S3 for static website hosting
• API Gateway for receiving the SAMLResponse POST from ADFS
• Lambda for processing the SAML assertion using a native XML parser
• DynamoDB conditional writes for session tracking exceptions
• STS for credentials via Lambda
• KMS for signing JWT tokens
• API Gateway custom authorizers for controlling per-session access to credentials, using JWT tokens that were signed with KMS keys
• JavaScript-generated SDK from API Gateway using a service proxy to DynamoDB
• RelayState in the SAMLRequest to ADFS to transmit the CognitoID and a short code from the client to your AWS backend

At a high level, this solution is similar to that of Scenario 1; however, most of the work is done in the infrastructure rather than on the client.

• ADFS still uses a POST binding to redirect the SAMLResponse to API Gateway; however, the Lambda function does not immediately redirect.
• The Lambda function decodes and uses an XML parser to read the properties of the SAML assertion.
• If the user’s assertion shows that they belong to a certain group matching a specified string (“Prod” in the sample), then you assign a role that they can assume (“ADFS-Production”).
• Lambda then gets the credentials on behalf of the user and stores them in DynamoDB as well as logging an audit record in a separate table.
• Lambda then returns a short-lived, signed JSON Web Token (JWT) to the JavaScript application.
• The application uses the JWT to get their stored credentials from DynamoDB through an API Gateway custom authorizer.

The architecture you build in this tutorial is outlined in the following diagram.

First, a user visits your static website hosted on S3. They generate an ephemeral random code that is transmitted during redirection to ADFS, where they are prompted for their Active Directory credentials.

Upon successful authentication, the ADFS server redirects the SAMLResponse assertion, along with the code (as the RelayState) via POST to API Gateway.

The Lambda function parses the SAMLResponse. If the user is part of the appropriate Active Directory group (AWS-Production in this tutorial), it retrieves credentials from STS on behalf of the user.

The credentials are stored in a DynamoDB table called SAMLSessions, along with the short code. The user login is stored in a tracking table called SAMLUsers.

The Lambda function generates a JWT token, with a 30-second expiration time signed with KMS, then redirects the client back to the static website along with this token.

The client then makes a call to an API Gateway resource acting as a DynamoDB service proxy that retrieves the credentials via a DeleteItem call. To make this call, the client passes the JWT in the authorization header.

A custom authorizer runs to validate the token using the KMS key again as well as the original random code.

Now that the client has credentials, it can use these to access AWS resources.

Tutorial: Backend processing and audit tracking

Before you walk through this tutorial you will need the source code from the samljs-serverless-sample Github Repository. You should use the SAM template provided in order to streamline the process but we’ll outline how you you would manually create resources too. There is a readme in the repository with instructions for using the SAM template. Either way you will still perform the manual steps of KMS key configuration, ADFS enablement of RelayState, and Amazon Cognito Identity Pool creation. The template will automate the process in creating the S3 website, Lambda functions, API Gateway resources and DynamoDB tables.

We walk through the details of all the steps and configuration below for illustrative purposes in this tutorial calling out the sections that can be omitted if you used the SAM template.

KMS key configuration

To sign JWT tokens, you need an encrypted plaintext key, to be stored in KMS. You will need to complete this step even if you use the SAM template.

1. In the IAM console, choose Encryption Keys, Create Key.
2. For Alias, type sessionMaster.
3. For Advanced Options, choose KMS, Next Step.
4. For Key Administrative Permissions, select your administrative role or user account.
5. For Key Usage Permissions, you can leave this blank as the IAM Role (next section) will have individual key actions configured. This allows you to perform administrative actions on the set of keys while the Lambda functions have rights to just create data keys for encryption/decryption and use them to sign JWTs.
6. Take note of the Key ID, which is needed for the Lambda functions.

IAM role configuration

You will need an IAM role for executing your Lambda functions. If you are using the SAM template this can be skipped. The sample code in the GitHub repository under Scenario2 creates separate roles for each function, with limited permissions on individual resources when you use the SAM template. We recommend separate roles scoped to individual resources for production deployments. Your Lambda functions need the following permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "Stmt1432927122000",
            "Effect": "Allow",
            "Action": [
                "dynamodb:PutItem",
                “dynamodb:GetItem”,
                “dynamodb:DeleteItem”,
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "kms:GenerateDataKey*",
                “kms:Decrypt”
            ],
            "Resource": [
                "*"
            ]
        }
    ]
}

Lambda function configuration

If you are not using the SAM template, create the following three Lambda functions from the GitHub repository in /Scenario2/lambda using the following names and environment variables. The Lambda functions are written in Node.js.

• GenerateKey_awslabs_samldemo
• ProcessSAML_awslabs_samldemo
• SAMLCustomAuth_awslabs_samldemo

The functions above are built, packaged, and uploaded to Lambda. For two of the functions, this can be done from your workstation (the sample commands for each function assume OSX or Linux). The third will need to be built on an AWS EC2 instance running the current Lambda AMI.

GenerateKey_awslabs_samldemo

This function is only used one time to create keys in KMS for signing JWT tokens. The function calls GenerateDataKey and stores the encrypted CipherText blob as Base64 in DynamoDB. This is used by the other two functions for getting the PlainTextKey for signing with a Decrypt operation.

This function only requires a single file. It has the following environment variables:

• KMS_KEY_ID: Unique identifier from KMS for your sessionMaster Key
• SESSION_DDB_TABLE: SAMLSessions
• ENC_CONTEXT: ADFS (or something unique to your organization)
• RAND_HASH: us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX

Navigate into /Scenario2/lambda/GenerateKey and run the following commands:

zip –r generateKey.zip .

aws lambda create-function --function-name GenerateKey_awslabs_samldemo --runtime nodejs4.3 --role LAMBDA_ROLE_ARN --handler index.handler --timeout 10 --memory-size 512 --zip-file fileb://generateKey.zip --environment Variables={SESSION_DDB_TABLE=SAMLSessions,ENC_CONTEXT=ADFS,RAND_HASH=us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX,KMS_KEY_ID=<kms key="KEY" id="ID">}

SAMLCustomAuth_awslabs_samldemo

This is an API Gateway custom authorizer called after the client has been redirected to the website as part of the login workflow. This function calls a GET against the service proxy to DynamoDB, retrieving credentials. The function uses the KMS key signing validation of the JWT created in the ProcessSAML_awslabs_samldemo function and also validates the random code that was generated at the beginning of the login workflow.

You must install the dependencies before zipping this function up. It has the following environment variables:

• SESSION_DDB_TABLE: SAMLSessions
• ENC_CONTEXT: ADFS (or whatever was used in GenerateKey_awslabs_samldemo)
• ID_HASH: us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX

Navigate into /Scenario2/lambda/CustomAuth and run:

npm install

zip –r custom_auth.zip .

aws lambda create-function --function-name SAMLCustomAuth_awslabs_samldemo --runtime nodejs4.3 --role LAMBDA_ROLE_ARN --handler CustomAuth.handler --timeout 10 --memory-size 512 --zip-file fileb://custom_auth.zip --environment Variables={SESSION_DDB_TABLE=SAMLSessions,ENC_CONTEXT=ADFS,ID_HASH= us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX }

ProcessSAML_awslabs_samldemo

This function is called when ADFS sends the SAMLResponse to API Gateway. The function parses the SAML assertion to select a role (based on a simple string search) and extract user information. It then uses this data to get short-term credentials from STS via AssumeRoleWithSAML and stores this information in a SAMLSessions table and tracks the user login via a SAMLUsers table. Both of these are DynamoDB tables but you could also store the user information in another AWS database type, as this is for auditing purposes. Finally, this function creates a JWT (signed with the KMS key) which is only valid for 30 seconds and is returned to the client as part of a 302 redirect from API Gateway.

This function needs to be built on an EC2 server running Amazon Linux. This function leverages two main external libraries:

• nJwt: Used for secure JWT creation for individual client sessions to get access to their records
• libxmljs: Used for XML XPath queries of the decoded SAMLResponse from AD FS

Libxmljs uses native build tools and you should run this on EC2 running the same AMI as Lambda and with Node.js v4.3.2; otherwise, you might see errors. For more information about current Lambda AMI information, see Lambda Execution Environment and Available Libraries.

After you have the correct AMI launched in EC2 and have SSH open to that host, install Node.js. Ensure that the Node.js version on EC2 is 4.3.2, to match Lambda. If your version is off, you can roll back with NVM.

After you have set up Node.js, run the following command:

yum install -y make gcc*

Now, create a /saml folder on your EC2 server and copy up ProcessSAML.js and package.json from /Scenario2/lambda/ProcessSAML to the EC2 server. Here is a sample SCP command:

cd ProcessSAML/

ls

package.json    ProcessSAML.js

scp -i ~/path/yourpemfile.pem ./* [email protected]:/home/ec2-user/saml/

Then you can SSH to your server, cd into the /saml directory, and run:

npm install

A successful build should look similar to the following:

Finally, zip up the package and create the function using the following AWS CLI command and these environment variables. Configure the CLI with your credentials as needed.

• SESSION_DDB_TABLE: SAMLSessions
• ENC_CONTEXT: ADFS (or whatever was used in GenerateKeyawslabssamldemo)
• PRINCIPAL_ARN: Full ARN of the AD FS IdP created in the IAM console
• USER_DDB_TABLE: SAMLUsers
• REDIRECT_URL: Endpoint URL of your static S3 website (or CloudFront distribution domain name if you did that optional step)
• ID_HASH: us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX

zip –r saml.zip .

aws lambda create-function --function-name ProcessSAML_awslabs_samldemo --runtime nodejs4.3 --role LAMBDA_ROLE_ARN --handler ProcessSAML.handler --timeout 10 --memory-size 512 --zip-file fileb://saml.zip –environment Variables={USER_DDB_TABLE=SAMLUsers,SESSION_DDB_TABLE= SAMLSessions,REDIRECT_URL=<your S3 bucket and test page path>,ID_HASH=us-east-1:XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX,ENC_CONTEXT=ADFS,PRINCIPAL_ARN=<your ADFS IdP ARN>}

If you built the first two functions on your workstation and created the ProcessSAML_awslabs_samldemo function separately in the Lambda console before building on EC2, you can update the code after building on with the following command:

aws lambda update-function-code --function-name ProcessSAML_awslabs_samldemo --zip-file fileb://saml.zip

Role trust policy configuration

This scenario uses STS directly to assume a role. You will need to complete this step even if you use the SAM template. Modify the trust policy, as you did before when Amazon Cognito was assuming the role. In the GitHub repository sample code, ProcessSAML.js is preconfigured to filter and select a role with “Prod” in the name via the selectedRole variable.

This is an example of business logic you can alter in your organization later, such as a callout to an external mapping database for other rules matching. In this tutorial, it corresponds to the ADFS-Production role that was created.

1. In the IAM console, choose Roles and open the ADFS-Production Role.

2. Edit the Trust Permissions field and replace the content with the following:

   {
     "Version": "2012-10-17",
     "Statement": [
       {
         "Effect": "Allow",
         "Principal": {
           "Federated": [
             "arn:aws:iam::ACCOUNTNUMBER:saml-provider/ADFS"
   ]
         },
         "Action": "sts:AssumeRoleWithSAML"
       }
     ]
   }

If you end up using another role (or add more complex filtering/selection logic), ensure that those roles have similar trust policy configurations. Also note that the sample policy above purposely uses an array for the federated provider matching the IdP ARN that you added. If your environment has multiple SAML providers, you could list them here and modify the code in ProcessSAML.js to process requests from different IdPs and grant or revoke credentials accordingly.

DynamoDB table creation

If you are not using the SAM template, create two DynamoDB tables:

• SAMLSessions: Temporarily stores credentials from STS. Credentials are removed by an API Gateway Service Proxy to the DynamoDB DeleteItem call that simultaneously returns the credentials to the client.
• SAMLUsers: This table is for tracking user information and the last time they authenticated in the system via ADFS.

The following AWS CLI commands creates the tables (indexed only with a primary key hash, called identityHash and CognitoID respectively):

aws dynamodb create-table \
    --table-name SAMLSessions \
    --attribute-definitions \
        AttributeName=group,AttributeType=S \
    --key-schema AttributeName=identityhash,KeyType=HASH \
    --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5

aws dynamodb create-table \
    --table-name SAMLUsers \
    --attribute-definitions \
        AttributeName=CognitoID,AttributeType=S \
    --key-schema AttributeName=CognitoID,KeyType=HASH \
    --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5

After the tables are created, you should be able to run the GenerateKey_awslabs_samldemo Lambda function and see a CipherText key stored in SAMLSessions. This is only for convenience of this post, to demonstrate that you should persist CipherText keys in a data store and never persist plaintext keys that have been decrypted. You should also never log plaintext keys in your code.

API Gateway configuration

If you are not using the SAM template, you will need to create API Gateway resources. If you have created resources for Scenario 1 in Part I, then the naming of these resources may be similar. If that is the case, then simply create an API with a different name (SAMLAuth2 or similar) and follow these steps accordingly.

1. In the API Gateway console for your API, choose Authorizers, Custom Authorizer.

2. Select your region and enter SAMLCustomAuth_awslabs_samldemo for the Lambda function. Choose a friendly name like JWTParser and ensure that Identity token source is method.request.header.Authorization. This tells the custom authorizer to look for the JWT in the Authorization header of the HTTP request, which is specified in the JavaScript code on your S3 webpage. Save the changes.

   

Now it’s time to wire up the Lambda functions to API Gateway.

1. In the API Gateway console, choose Resources, select your API, and then create a Child Resource called SAML. This includes a POST and a GET method. The POST method uses the ProcessSAML_awslabs_samldemo Lambda function and a 302 redirect, while the GET method uses the JWTParser custom authorizer with a service proxy to DynamoDB to retrieve credentials upon successful authorization.



2. Create a POST method. For Integration Type, choose Lambda and add the ProcessSAML_awslabs_samldemo Lambda function. For Method Request, add headers called RelayState and SAMLResponse.

   

3. Delete the Method Response code for 200 and add a 302. Create a response header called Location. In the Response Models section, for Content-Type, choose application/json and for Models, choose Empty.

   

4. Delete the Integration Response section for 200 and add one for 302 that has a Method response status of 302. Edit the response header for Location to add a Mapping value of integration.response.body.location.

   

5. Finally, in order for Lambda to capture the SAMLResponse and RelayState values, choose Integration Request.

6. In the Body Mapping Template section, for Content-Type, enter application/x-www-form-urlencoded and add the following template:

   {
   "SAMLResponse" :"$input.params('SAMLResponse')",
   "RelayState" :"$input.params('RelayState')",
   "formparams" : $input.json('$')
   }

7. Create a GET method with an Integration Type of Service Proxy. Select the region and DynamoDB as the AWS Service. Use POST for the HTTP method and DeleteItem for the Action. This is important as you leverage a DynamoDB feature to return the current records when you perform deletion. This simultaneously allows credentials in this system to not be stored long term and also allows clients to retrieve them. For Execution role, use the Lambda role from earlier or a new role that only has IAM scoped permissions for DeleteItem on the SAMLSessions table.

   

8. Save this and open Method Request.

9. For Authorization, select your custom authorizer JWTParser. Add in a header called COGNITO_ID and save the changes.

   

10. In the Integration Request, add in a header name of Content-Type and a value for Mapped of ‘application/x-amzn-json-1.0‘ (you need the single quotes surrounding the entry).

11. Next, in the Body Mapping Template section, for Content-Type, enter application/json and add the following template:

    {
        "TableName": "SAMLSessions",
        "Key": {
            "identityhash": {
                "S": "$input.params('COGNITO_ID')"
            }
        },
        "ReturnValues": "ALL_OLD"
    }

Inspect this closely for a moment. When your client passes the JWT in an Authorization Header to this GET method, the JWTParser Custom Authorizer grants/denies executing a DeleteItem call on the SAMLSessions table.

ADF

If it is granted, then there needs to be an item to delete the reference as a primary key to the table. The client JavaScript (seen in a moment) passes its CognitoID through as a header called COGNITO_ID that is mapped above. DeleteItem executes to remove the credentials that were placed there via a call to STS by the ProcessSAML_awslabs_samldemo Lambda function. Because the above action specifies ALL_OLD under the ReturnValues mapping, DynamoDB returns these credentials at the same time.

1. Save the changes and open your /saml resource root.
2. Choose Actions, Enable CORS.
3. In the Access-Control-Allow-Headers section, add COGNITO_ID into the end (inside the quotes and separated from other headers by a comma), then choose Enable CORS and replace existing CORS headers.
4. When completed, choose Actions, Deploy API. Use the Prod stage or another stage.
5. In the Stage Editor, choose SDK Generation. For Platform, choose JavaScript and then choose Generate SDK. Save the folder someplace close. Take note of the Invoke URL value at the top, as you need this for ADFS configuration later.

Website configuration

If you are not using the SAM template, create an S3 bucket and configure it as a static website in the same way that you did for Part I.

If you are using the SAM template this will automatically be created for you however the steps below will still need to be completed:

In the source code repository, edit /Scenario2/website/configs.js.

1. Ensure that the identityPool value matches your Amazon Cognito Pool ID and the region is correct.
2. Leave adfsUrl the same if you’re testing on your lab server; otherwise, update with the AD FS DNS entries as appropriate.
3. Update the relayingPartyId value as well if you used something different from the prerequisite blog post.

Next, download the minified version of the AWS SDK for JavaScript in the Browser (aws-sdk.min.js) and place it along with the other files in /Scenario2/website into the S3 bucket.

Copy the files from the API Gateway Generated SDK in the last section to this bucket so that the apigClient.js is in the root directory and lib folder is as well. The imports for these scripts (which do things like sign API requests and configure headers for the JWT in the Authorization header) are already included in the index.html file. Consult the latest API Gateway documentation if the SDK generation process updates in the future

ADFS configuration

Now that the AWS setup is complete, modify your ADFS setup to capture RelayState information about the client and to send the POST response to API Gateway for processing. You will need to complete this step even if you use the SAM template.

If you’re using Windows Server 2008 with ADFS 2.0, ensure that Update Rollup 2 is installed before enabling RelayState. Please see official Microsoft documentation for specific download information.

1. After Update Rollup 2 is installed, modify %systemroot%\inetpub\adfs\ls\web.config. If you’re on a newer version of Windows Server running AD FS 3.0, modify %systemroot%\ADFS\Microsoft.IdentityServer.Servicehost.exe.config.
2. Find the section in the XML marked <Microsoft.identityServer.web> and add an entry for <useRelayStateForIdpInitiatedSignOn enabled="true">. If you have the proper ADFS rollup or version installed, this should allow the RelayState parameter to be accepted by the service provider.
3. In the ADFS console, open Relaying Party Trusts for Amazon Web Services and choose Endpoints.
4. For Binding, choose POST and for Invoke URL,enter the URL to your API Gateway from the stage that you noted earlier.

At this point, you are ready to test out your webpage. Navigate to the S3 static website Endpoint URL and it should redirect you to the ADFS login screen. If the user login has been recent enough to have a valid SAML cookie, then you should see the login pass-through; otherwise, a login prompt appears. After the authentication has taken place, you should quickly end up back at your original webpage. Using the browser debugging tools, you see “Successful DDB call” followed by the results of a call to STS that were stored in DynamoDB.

As in Scenario 1, the sample code under /scenario2/website/index.html has a button that allows you to “ping” an endpoint to test if the federated credentials are working. If you have used the SAM template this should already be working and you can test it out (it will fail at first – keep reading to find out how to set the IAM permissions!). If not go to API Gateway and create a new Resource called /users at the same level of /saml in your API with a GET method.

For Integration type, choose Mock.

In the Method Request, for Authorization, choose AWS_IAM. In the Integration Response, in the Body Mapping Template section, for Content-Type, choose application/json and add the following JSON:

{
    "status": "Success",
    "agent": "${context.identity.userAgent}"
}

Before using this new Mock API as a test, configure CORS and re-generate the JavaScript SDK so that the browser knows about the new methods.

1. On the /saml resource root and choose Actions, Enable CORS.
2. In the Access-Control-Allow-Headers section, add COGNITO_ID into the endpoint and then choose Enable CORS and replace existing CORS headers.
3. Choose Actions, Deploy API. Use the stage that you configured earlier.
4. In the Stage Editor, choose SDK Generation and select JavaScript as your platform. Choose Generate SDK.

5. Upload the new apigClient.js and lib directory to the S3 bucket of your static website.

One last thing must be completed before testing (You will need to complete this step even if you use the SAM template) if the credentials can invoke this mock endpoint with AWS_IAM credentials. The ADFS-Production Role needs execute-api:Invoke permissions for this API Gateway resource.

1. In the IAM console, choose Roles, and open the ADFS-Production Role.

2. For testing, you can attach the AmazonAPIGatewayInvokeFullAccess policy; however, for production, you should scope this down to the resource as documented in Control Access to API Gateway with IAM Permissions.

3. After you have attached a policy with invocation rights and authenticated with AD FS to finish the redirect process, choose PING.

If everything has been set up successfully you should see an alert with information about the user agent.

Final Thoughts

We hope these scenarios and sample code help you to not only begin to build comprehensive enterprise applications on AWS but also to enhance your understanding of different AuthN and AuthZ mechanisms. Consider some ways that you might be able to evolve this solution to meet the needs of your own customers and innovate in this space. For example:

• Completing the CloudFront configuration and leveraging SSL termination for site identification. See if this can be incorporated into the Lambda processing pipeline.
• Attaching a scope-down IAM policy if the business rules are matched. For example, the default role could be more permissive for a group but if the user is a contractor (username with –C appended) they get extra restrictions applied when assumeRoleWithSaml is called in the ProcessSAML_awslabs_samldemo Lambda function.
• Changing the time duration before credentials expire on a per-role basis. Perhaps if the SAMLResponse parsing determines the user is an Administrator, they get a longer duration.
• Passing through additional user claims in SAMLResponse for further logical decisions or auditing by adding more claim rules in the ADFS console. This could also be a mechanism to synchronize some Active Directory schema attributes with AWS services.
• Granting different sets of credentials if a user has accounts with multiple SAML providers. While this tutorial was made with ADFS, you could also leverage it with other solutions such as Shibboleth and modify the ProcessSAML_awslabs_samldemo Lambda function to be aware of the different IdP ARN values. Perhaps your solution grants different IAM roles for the same user depending on if they initiated a login from Shibboleth rather than ADFS?

The Lambda functions can be altered to take advantage of these options which you can read more about here. For more information about ADFS claim rule language manipulation, see The Role of the Claim Rule Language on Microsoft TechNet.

We would love to hear feedback from our customers on these designs and see different secure application designs that you’re implementing on the AWS platform.