Using Python to Pull Data from MS Graph API – Part 2

Using Python to Pull Data from MS Graph API – Part 2

Welcome back my fellow geeks!

In this series I’m walking through my experience putting together some code to integrate with the Microsoft Graph API (Application Programming Interface).  In the last post I covered the logic behind this pet project and the tools I used to get it done.  In this post I’ll be walking through the code and covering what’s happening behind the scenes.

The project consists of three files.  The awsintegration.py file contains functions for the integration with AWS Systems Manager Parameter Store and Amazon S3 using the Python boto3 SDK (Software Development Kit).  Graphapi.py contains two functions.  One function uses Microsoft’s Azure Active Directory Library for Python (ADAL) and the other function uses Python’s Requests library to make calls to the MS Graph API.  Finally, the main.py file contains the code that brings everything together. There are a few trends you’ll notice with all of the code. First off it’s very simple since I’m a long way from being able to do any fancy tricks and the other is I tried to stay away from using too many third-party modules.

Let’s first dig into the awsintegration.py module.  In the first few lines above I import the required modules which include AWS’s Boto3 library.

import json
import boto3
import logging

Python has a stellar standard logging module that makes logging to a centralized location across a package a breeze.  The line below configures modules called by the main package to inherit the logging configuration from the main package.  This way I was able to direct anything I wanted to log to the same log file.

log = logging.getLogger(__name__)

This next function uses Boto3 to call AWS Systems Manager Parameter Store to retrieve a secure string.  Be aware that if you’re using Parameter Store to store secure strings the security principal you’re using to make the call (in my case an IAM User via Cloud9) needs to have appropriate permissions to Parameter Store and the KMS CMK.  Notice I added a line here to log the call for the parameter to help debug any failures.  Using the parameter store with Boto3 is covered in detail here.

def get_parametersParameterStore(parameterName,region):
    log.info('Request %s from Parameter Store',parameterName)
    client = boto3.client('ssm', region_name=region)
    response = client.get_parameter(
        Name=parameterName,
        WithDecryption=True
    )
    return response['Parameter']['Value']

The last function in this module again uses Boto3 to upload the file to an Amazon S3 bucket with a specific prefix.  Using S3 is covered in detail here.

def put_s3(bucket,prefix,region,filename):
    s3 = boto3.client('s3', region_name=region)
    s3.upload_file(filename,bucket,prefix + "/" + filename)

Next up is the graphapi.py module.  In the first few lines I again import the necessary modules as well as the AuthenticationContext module from ADAL.  This module contains the AuthenticationContext class which is going to get the OAuth 2.0 access token needed to authenticate to the MS Graph API.

import json
import requests
import logging
from adal import AuthenticationContext

log = logging.getLogger(__name__)

In the function below an instance of the AuthenticationContext class is created and the acquire_token_with_client_credentials method is called.   It uses the OAuth 2.0 Client Credentials grant type which allows the script to access the MS Graph API without requiring a user context.  I’ve already gone ahead and provisioned and authorized the script with an identity in Azure AD and granted it the appropriate access scopes.

Behind the scenes Azure AD (authorization server in OAuth-speak) is contacted and the script (client in OAuth-speak) passes a unique client id and client secret.  The client id and client secret are used to authenticate the application to Azure AD which then looks within its directory to determine what resources the application is authorized to access (scope in OAuth-speak).  An access token is then returned from Azure AD which will be used in the next step.

def obtain_accesstoken(tenantname,clientid,clientsecret,resource):
    auth_context = AuthenticationContext('https://login.microsoftonline.com/' +
        tenantname)
    token = auth_context.acquire_token_with_client_credentials(
        resource=resource,client_id=clientid,
        client_secret=clientsecret)
    return token

A properly formatted header is created and the access token is included. The function checks to see if the q_param parameter has a value and it if does it passes it as a dictionary object to the Python Requests library which includes the key values as query strings. The request is then made to the appropriate endpoint. If the response code is anything but 200 an exception is raised, written to the log, and the script terminates.  Assuming a 200 is received the Python JSON library is used to parse the response.  The JSON content is searched for an attribute of @odata.nextLink which indicates the results have been paged.  The function handles it by looping until there are no longer any paged results.  It additionally combines the paged results into a single JSON array to make it easier to work with moving forward.

def makeapirequest(endpoint,token,q_param=None):
 
    headers = {'Content-Type':'application/json', \
    'Authorization':'Bearer {0}'.format(token['accessToken'])}

    log.info('Making request to %s...',endpoint)
        
    if q_param != None:
        response = requests.get(endpoint,headers=headers,params=q_param)
        print(response.url)
    else:
        response = requests.get(endpoint,headers=headers)    
    if response.status_code == 200:
        json_data = json.loads(response.text)
            
        if '@odata.nextLink' in json_data.keys():
            log.info('Paged result returned...')
            record = makeapirequest(json_data['@odata.nextLink'],token)
            entries = len(record['value'])
            count = 0
            while count < entries:
                json_data['value'].append(record['value'][count])
                count += 1
        return(json_data)
    else:
        raise Exception('Request failed with ',response.status_code,' - ',
            response.text)

Lastly there is main.py which stitches the script together.  The first section adds the modules we’ve already covered in addition to the argparse library which is used to handle arguments added to the execution of the script.

import json
import requests
import logging
import time
import graphapi
import awsintegration
from argparse import ArgumentParser

A simple configuration for the logging module is setup instructing it to write to the msapiquery.log using a level of INFO and applies a standard format.

logging.basicConfig(filename='msapiquery.log', level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

This chunk of code creates an instance of the ArgumentParser class and configures two arguments.  The sourcefile argument is used to designate the JSON parameters file which contains all the necessary information.

The parameters file is then opened and processed.  Note that the S3 parameters are only pulled in if the –s3 switch was used.

parser = ArgumentParser()
parser.add_argument('sourcefile', type=str, help='JSON file with parameters')
parser.add_argument('--s3', help='Write results to S3 bucket',action='store_true')
args = parser.parse_args()

try:
    with open(args.sourcefile) as json_data:
        d = json.load(json_data)
        tenantname = d['parameters']['tenantname']
        resource = d['parameters']['resource']
        endpoint = d['parameters']['endpoint']
        filename = d['parameters']['filename']
        aws_region = d['parameters']['aws_region']
        q_param = d['parameters']['q_param']
        clientid_param = d['parameters']['clientid_param']
        clientsecret_param = d['parameters']['clientsecret_param']
        if args.s3:
            bucket = d['parameters']['bucket']
            prefix = d['parameters']['prefix']

Next up the get_parametersParameterStore function from the awsintegration module is executed twice.  Once to get the client id and once to get the client secret.  Note that the get_parameters method for Boto3 Systems Manager client could have been used to get both of the parameters in a single call, but I didn’t go that route.

    logging.info('Attempting to contact Parameter Store...')
    clientid = awsintegration.get_parametersParameterStore(clientid_param,aws_region)
    clientsecret = awsintegration.get_parametersParameterStore(clientsecret_param,aws_region)

In these next four lines the access token is obtained by calling the obtain_accesstoken function and the request to the MS Graph API is made using the makeapirequest function.

    logging.info('Attempting to obtain an access token...')
    token = graphapi.obtain_accesstoken(tenantname,clientid,clientsecret,resource)

    logging.info('Attempting to query %s ...',endpoint)
    data = graphapi.makeapirequest(endpoint,token,q_param)

This section creates a string representing the current day, month, and year and prepends the filename that was supplied in the parameters file.  The file is then opened using the with statement.  If you’re familiar with the using statement from C# the with statement is similar in that it ensures resources are cleaned up after being used.

Before the data is written to file, I remove the @odata.nextLink key if it’s present.  This is totally optional and just something I did to pretty up the results.  The data is then written to the file as raw text by using the Python JSON encoder/decoder.

    logging.info('Attempting to write results to a file...')
    timestr = time.strftime("%Y-%m-%d")
    filename = timestr + '-' + filename
    with open(filename,'w') as f:
        
        ## If the data was paged remove the @odata.nextLink key
        ## to clean up the data before writing it to a file

        if '@odata.nextLink' in data.keys():
            del data['@odata.nextLink']
        f.write(json.dumps(data))

Finally, if the s3 argument was passed when the script was run, the put_s3 method from the awsintegration module is run and the file is uploaded to S3.

    logging.info('Attempting to write results to %s S3 bucket...',bucket)
    if args.s3:
        awsintegration.put_s3(bucket,prefix,aws_region,filename)

Exceptions thrown anywhere in the script are captured here written to the log file.  I played around a lot with a few different ways of handling exceptions and everything was so interdependent that if there was a failure it was best for the script to stop altogether and inform the user.  Naftali Harris has an amazing blog that walks through the many different ways of handling exceptions in Python and the various advantages and disadvantages.  It’s a great read.

except Exception as e:
    logging.error('Exception thrown: %s',e)
    print('Error running script.  Review the log file for more details')

So that’s what the code is.  Let’s take a quick look at the parameters file below.  It’s very straight forward.  Keep in mind both the bucket and prefix parameters are only required when using the –s3 option.  Here are some details on the other options:

  • The tenantname attribute is the DNS name of the Azure AD tenant being queries.
  • The resource attribute specifies the resource the access token will be used for.  If you’re going to be hitting the MS Graph API, more than likely it will be https://graph.microsoft.com
  • The endpoint attribute specifies the endpoint the request is being made to including any query strings you plan on using
  • The clientid_param and clientsecret_param attributes are the AWS Systems Manager Parameter Store parameter names that hold the client id and client secret the script was provisioned from Azure AD
  • The q_param attribute is an array of key value pairs intended to story OData query strings
  • The aws_region attribute is the region the S3 bucket and parameter store data is stored in
  • The filename attribute is the name you want to set for the file the script will produce
{
    "parameters":{
        "tenantname": "mytenant.com",
        "resource": "https://graph.microsoft.com",
        "endpoint": "https://graph.microsoft.com/beta/auditLogs/signIns",
        "clientid_param":"myclient_id",
        "clientsecret_param":"myclient_secret",
        "q_param":{"$filter":"createdDateTime gt 2019-01-09"},
        "aws_region":"us-east-1",
        "filename":"sign_in_logs.json",
        "bucket":"mybucket",
        "prefix":"myprefix"
    }
}

Now that the script has been covered, let’s see it action.  First I’m going to demonstrate how it handles paging by querying the MS Graph API endpoint to list out the users in the directory.  I’m going to append the $select query parameter and set it to return just the user’s id to make the output more simple and set the $top query parameter to one to limit the results to one user per page.  The endpoint looks like this https://graph.microsoft.com/beta/users?$top=1&select=id.

I’ll be running the script from an instance of Cloud9.  The IAM user I’m using with AWS has appropriate permissions to the S3 bucket, KMS CMK, and parameters in the parameter store.  I’ve set each of the parameters in the parameters file to the appropriate values for the environment I’ve configured.  I’ll additionally be using the –s3 option.

 

run_script.png

Once the script is complete it’s time to look at the log file that was created.  As seen below each step in the script to aid with debugging if something were to fail.  The log also indicates the results were paged.

log

The output is nicely formatted JSON that could be further transformed or fed into something like Amazon Athena for further analysis (future post maybe?).

json.png

Cool right?  My original use case was sign-in logs so let’s take a glance that.  Here I’m going to use an endpoint of https://graph.microsoft.com/beta/auditLogs/signIns with a OData filter option of createdDateTime gt 2019-01-08 which will limit the data returned to today’s sign-ins.

In the logs we see the script was successfully executed and included the filter specified.

graphapi_log_sign.png

The output is the raw JSON of the sign-ins over the past 24 hours.  For your entertainment purposes I’ve included one of the malicious sign-ins that was captured.  I SO can’t wait to examine this stuff in a BI tool.

sign_in_json

Well that’s it folks.  It may be ugly, but it works!  This was a fun activity to undertake as a first stab at making something useful in Python.  I especially enjoyed the lack of documentation available on this integration.  It really made me dive deep and learn things I probably wouldn’t have if there were a billion of examples out there.

I’ve pushed the code to Github so feel free to muck around with it to your hearts content.

Using Python to Pull Data from MS Graph API – Part 1

Welcome to 2019 fellow geeks! I hope each of you had a wonderful holiday with friends and family.

It’s been a few months since my last post. As some of you may be aware I made a career move last September and took on a new role with a different organization. The first few months have been like drinking from multiple fire hoses at once and I’ve learned a ton. It’s been an amazing experience that I’m excited to continue in 2019.

One area I’ve been putting some focus in is learning the basics of Python. I’ve been a PowerShell guy (with a bit of C# thrown in there) for the past six years so diving into a new language was a welcome change. I picked up a few books on the language, watched a few videos, and it wasn’t clicking. At that point I decided it was time to jump into the deep end and come up with a use case to build out a script for. Thankfully I had one queued up that I had started in PowerShell.

Early last year my wife’s Office 365 account was hacked. Thankfully no real damage was done minus some spam email that was sent out. I went through the wonderful process of changing her passwords across her accounts, improving the complexity and length, getting her on-boarded with a password management service, and enabling Azure MFA (Multi-factor Authentication) on her Office 365 account and any additional services she was using that supported MFA options.  It was not fun.

Curious of what the logs would have shown, I had begun putting together a PowerShell script that was going to pull down the logs from Azure AD (Active Directory), extract the relevant data, and export it CSV (comma-separate values) where I could play around with it in whatever analytics tool I could get my hands on. Unfortunately life happened and I never had a chance to finish the script or play with the data. This would be my use case for my first Python script.

Azure AD offers a few different types of logs which Microsoft divides into a security pillar and an activity pillar. For my use case I was interested in looking at the reports in the Activity pillar, specifically the Sign-ins report. This report is available for tenants with an Azure AD Premium P1 or P2 subscription (I added P2 subscriptions to our family accounts last year).  The sign-in logs have a retention period of 30 days and are available either through the Azure Portal or programmatically through the MS Graph API (Application Programming Interface).

My primary goals were to create as much reusable code as possible and experiment with as many APIs/SDKs (Software Development Kits) as I could.  This was accomplished by breaking the code into various reusable modules and leveraging AWS (Amazon Web Services) services for secure storage of Azure AD application credentials and cloud-based storage of the exported data.  Going this route forced me to use the MS Graph API, Microsoft’s Azure Active Directory Library for Python (or ADAL for short), and Amazon’s Boto3 Python SDK.

On the AWS side I used AWS Systems Manager Parameter Store to store the Azure AD credentials as secure strings encrypted with a AWS KMS (Key Management Service) customer-managed customer master key (CMK).  For cloud storage of the log files I used Amazon S3.

Lastly I needed a development environment and source control.  For about a day I simply used Sublime Text on my Mac and saved the file to a personal cloud storage account.  This was obviously not a great idea so I decided to finally get my GitHub repository up and running.  Additionally I moved over to using AWS’s Cloud9 for my IDE (integrated development environment).   Cloud9 has the wonderful perk of being web based and has the capability of creating temporary credentials that can do most of what my AWS IAM user can do.  This made it simple to handle permissions to the various resources I was using.

Once the instance of Cloud9 was spun up I needed to set the environment up for Python 3 and add the necessary libraries.  The AMI (Amazon Machine Image) used by the Cloud9 service to provision new instances includes both Python 2.7 and Python 3.6.  This fact matters when adding the ADAL and Boto3 modules via pip because if you simply run a pip install module_name it will be installed for Python 2.7.  Instead you’ll want to execute the command python3 -m pip install module_name which ensures that the two modules are installed in the appropriate location.

In my next post I’ll walk through and demonstrate the script.

Have a great week!

 

 

 

 

Integrating Azure AD and G-Suite – Automated Provisioning

Integrating Azure AD and G-Suite – Automated Provisioning

Today I’ll wrap up my series on Azure Active Directory’s (Azure AD) integration with Google’s G-Suite.  In my first entry I covered the single-sign on (SSO) integration and in my second and third posts I gave an overview of Google’s Cloud Platform (GCP) and demonstrated how to access a G-Suite domain’s resources through Google’s APIs.  In this post I’m going to cover how Microsoft provides automated provisioning of user, groups, and contacts .  If you haven’t read through my posts on Google’s API (part 1, part 2) take a read through so you’re more familiar with the concepts I’ll be covering throughout this post.

SSO using SAML or Open ID Connect is a common capability of most every cloud solutions these days.  While that solves the authentication problem, the provisioning of users, groups, and other identity-relates objects remains a challenge largely due to the lack of widely accepted standards (SCIM has a ways to go folks).  Vendors have a variety of workarounds including making LDAP calls back to a traditional on-premises directory (YUCK), supporting uploads of CSV files, or creating and updating identities in its local databases based upon the information contained in a SAML assertion or Open ID Connect id token.  A growing number of vendors are exposing these capabilities via a web-based API.  Google falls into this category and provides a robust selection of APIs to interact with its services from Gmail to resources within Google Cloud Platform, and yes even Google G-Suite.

If you’re a frequent user of Azure AD, you’ll have run into the automatic provisioning capabilities it brings to the table across a wide range of cloud services.  In a previous series I covered its provisioning capabilities with Amazon Web Services.  This is another use case where Microsoft leverages a third party’s robust API to simplify the identity management lifecycle.

In the SSO Quickstart Guide Microsoft provides for G-Suite it erroneously states:

“Google Apps supports auto provisioning, which is by default enabled. There is no action for you in this section. If a user doesn’t already exist in Google Apps Software, a new one is created when you attempt to access Google Apps Software.”

This simply isn’t true.  While auto provisioning via the API can be done, it is a feature you need to code to and isn’t enabled by default.  When you enable SSO to G-Suite and attempt to access it using an assertion containing the claim for a user that does not exist within a G-Suite domain you receive the error below.

google4int1

This establishes what we already knew in that identities representing our users attempting SSO to G-Suite need to be created before the users can authenticate.  Microsoft provides a Quickstart for auto provisioning into G-Suite.  The document does a good job telling you were to click and giving some basic advice but really lacks in the detail into what’s happening in the background and describing how it works.

Let’s take a deeper look shall we?

If you haven’t already, add the Google Apps application from the Azure AD Application Gallery.  Once the application is added navigate to the blade for the application and select the Provisioning page.  Switch the provisioning mode from manual to automatic.

google4int2.png

Right off the bat we see a big blue Authorize button which tells us that Microsoft is not using the service accounts pattern for accessing the Google API.  Google’s recommendation is to use the service account pattern when accessing project-based data rather than user specific data.  The argument can be made that G-Suite data doesn’t fall under project-based data and the service account credential doesn’t make sense.  Additionally using a service account would require granting the account domain-wide delegation for the G-Suite domain allowing the account to impersonate any user in the G-Suite domain.  Not really ideal, especially from an auditing perspective.

By using the Server-side Web Apps pattern a new user in G-Suite can be created and assigned as the “Azure AD account”. The downfall with of this means you’re stuck paying Google $10.00 a month for a non-human account. The price of good security practices I guess.

google4int3.png

Microsoft documentation states that the account must be granted the Super Admin role. I found this surprising since you’re effectively giving the account god rights to your G-Suite domain. It got me wondering what authorization scopes is Microsoft asking for? Let’s break out Fiddler and walk through the process that kicks off after clicking on the Authorization button.

A new window pops up from Google requesting me to authenticate. Here Azure AD, acting as the OAuth client, has made an authorization request and has sent me along with the request over to the Google which is acting as the authorization server to authenticate, consent to the access, and take the next step in the authorization flow.

google4int4

When I switch over to Fiddler I see a number of sessions have been captured.  Opening the WebForms window of the first session to accounts.google.com a number of parameters that were passed to Google.

google4int5

The first parameter gives us the three authorization scopes Azure AD is looking for.  The admin.directory.group and admin.directory.user are scopes are both related to the Google Directory API, which makes sense if it wants to manage users and groups.  The /m8/feeds scope grants it access to manage contacts via the Google Contacts API.  This is an older API that uses XML instead of JSON to exchange information and looks like it has been/is being replaced by the Google People API.

Management of contacts via this API is where the requirement for an account in the Super Admin role originates.  Google documentation states that management of domain shared contacts via the /m8/feeds API requires an administrator username and password for Google Apps.  I couldn’t find any privilege in G-Suite which could be added to a custom Admin role that mentioned contacts.  Given Google’s own documentation along the lack of an obvious privilege option, this may be a hard limitation of G-Suite.  Too bad too because there are options for both Users and Groups.  Either way, the request for this authorization scope drives the requirement for Super Admin for the account Azure AD will be using for delegated access.

The redirect_uri is the where Google sends the user after the authorization request is complete.  The response_type tells us Azure AD and Google are using the OAuth authorization code grant type flow.  The client_id is the unique identifier Google has assigned to Azure AD in whatever project Microsoft has it built in.  The approval_prompt setting of force tells Google to display the consent window and the data Azure AD wants to access.  Lastly, the access_type setting of offline allows Azure AD to access the APIs without the user being available to authenticate via a refresh token which will be issued along with the access token.  Let’s pay attention to that one once the consent screen pops up.

I plug in valid super user credentials to my G-Suite domain and authenticate and receive the warning below.  This indicates that Microsoft has been naughty and hasn’t had their application reviewed by Google.  This was made a requirement back in July of 2017… so yeah… Microsoft maybe get on that?

google4int6.png

To progress to the consent screen I hit the Advanced link in the lower left and opt to continue.  The consent window then pops up.

google4int7.png

Here I see that Microsoft has registered their application with a friendly name of azure.com.  I’m also shown the scopes that the application wants to access which jive with the authorization scopes we saw in Fiddler.  Remember that offline access Microsoft asked for?  See it mentioned anywhere in this consent page that I’m delegating this access to Microsoft perpetually as long as they ask for a refresh token?  This is one of my problems with OAuth and consent windows like this.  It’s entirely too vague as to how long I’m granting the application access to my data or to do things as me.  Expect to see this OAuth consent attacks continue to grow in in use moving forward.  Why worry about compromising the user’s credentials when I can display a vague consent window and have them grant me access directly to their data?  Totally safe.

Hopping back to the window, I click the Allow button and the consent window closes.  Looking back at Fiddler I see that I received back an authorization code and posted it back to the reply_uri designated in the original authorization request.

google4int8.png

Switching back to the browser window for the Azure Portal the screen updates and the Test Connection button becomes available.  Clicking the button initiates a quick check where Azure AD obtains an access token for the scopes it requires unseen to the user.  After the successful test I hit the Save button.

google4int9.png

Switching to the browser window for the Google Admin Portal let’s take a look at the data that’s been updated for the user I used to authorize Microsoft its access.  For that I select the user, go to the Security section and I now see that the Azure Active Directory service is authorized to the contacts, user, and group management scopes.

google4int10.png

Switching back to the browser window for the Azure Portal I see some additional options are now available.

google4int11.png

The mappings are really interesting and will look familiar to you if you’ve ever done anything with an identity management tool like Microsoft Identity Manager (MIM) or even Azure AD Sync.  The user mappings for example show which attributes in Azure AD are used to populate the attributes in G-Suite.

google4int12.png

The attributes that have the Delete button grayed out are required by Google in order to provision new user accounts in a G-Suite domain.  The options available for deletion are additional data beyond what is required that Microsoft can populate on user accounts it provisions into G-Suite.  Selecting the Show advanced options button, allow you to play with the schema Microsoft is using for G-Suite.   What I found interesting about this schema is it doesn’t match the resource representation Google provides for the API.  It would have been nice to match the two to make it more consumable, but they’re probably working off values used in the old Google Provisioning API or they don’t envision many people being nerdy enough to poke around the schema.

Next up I move toggle the provisioning status from Off to On and leave the Scope option set to sync only the assigned users and groups.

google4int13.png

I then hit the Save button to save the new settings and after a minute my initial synchronization is successful.  Now nothing was synchronized, but it shows the credentials correctly allowed Azure AD to hit my G-Suite domain over the appropriate APIs with the appropriate access.

google4int14.png

So an empty synchronization works, how about one with a user?  I created a new user named dutch.schaefer@geekintheweeds.com with only the required attributes of display name and user principal name populated, assigned the new user to the Google Apps application and give Azure AD a night to run another sync.  Earlier tonight I checked the provisioning summary and verified the sync grabbed the new user.

google4int15.png

Review of the audit logs for the Google Apps application shows that the new user was exported around 11PM EST last night.  If you’re curious the synch between Azure AD and G-Suite occurs about every 20 minutes.

google4int16.png

Notice that the FamilyName and GivenName attributes are set to a period.  I never set the first or last name attributes of the user in Azure AD, so both attributes are blank.  If we bounce back to the attribute mapping and look at the attributes for Google Apps, we see that FamilyName and GivenName are both required meaning Azure AD had to populate them with something.  Different schemas, different requirements.

google4int17

Switching over to the Google Admin Console I see that the new user was successfully provisioned into G-Suite.

google4int18.png

Pretty neat overall.  Let’s take a look at what we learned:

  • Azure AD supports single sign-on to G-Suite via SAML using a service provider-initiated flow where Azure AD acts as the identity provider and G-Suite acts as the service provider.
  • A user object with a login id matching the user’s login id in Azure Active Directory must be created in G-Suite before single sign-on will work.
  • Google provides a number of libraries for its API and the Google API Explorer should be used for experimentation with Google’s APIs.
  • Google’s Directory API is used by Azure AD to provision users and groups into a G-Suite domain.
  • Google’s Contacts API is used by Azure AD to provision contacts into a G-Suite domain.
  • A user holding the Super Admin role in the G-Suite domain must be used to authorize Azure AD to perform provisioning activities.  The Super Admin role is required due to the usage of the Google Contact API.
  • Azure AD’s authorization request includes offline access using refresh tokens to request additional access tokens to ensure the sync process can be run on a regular basis without requiring re-authorization.
  • Best practice is to dedicate a user account in your G-Suite domain to Azure AD.
  • Azure AD uses the Server-side Web pattern for accessing Google’s APIs.
  • The provisioning process will populate a period for any attribute that is required in G-Suite but does not have a value in the corresponding attribute in Azure AD.
  • The provisioning process runs a sync every 20 minutes.

Even though my coding is horrendous, I absolutely loved experimenting with the Google API.  It’s easy to realize why APIs are becoming so critical to a good solution.  With the increased usage of a wide variety of products in a business, being able to plug and play applications is a must.  The provisioning aspect Azure AD demonstrates here is a great example of the opportunities provided when critical functionality is exposed for programmatic access.

I hope you enjoyed the series, learned a bit more about both solutions, and got some insight into what’s going on behind the scenes.

 

Integrating Azure AD and G-Suite – Google API Integration Part 2

Integrating Azure AD and G-Suite – Google API Integration Part 2

Welcome back.

We’ve had an interesting journey so far in this series exploring the Azure Active Directory (Azure AD) and Google G-Suite (G-Suite) integration.  In the first entry I covered how the single sign-on integration works.  The second entry covered explored the process of registering an application with the Google Cloud Platform (GCP) for API access and GCP’s relationship to G-Suite.  Today I demonstrate interaction with Google’s API through the use of some very basic .NET console applications I’ve put together.

As you’ll recall from the second entry, I created a new project with GCP and created a service account identity for my sample application and the Google Admin API has been enabled for the project.  The application has been granted domain-wide delegation rights to my G-Suite domain at geekintheweeds.com.  Finally, the application’s client ID added to my G-Suite domain and has been granted access to the https://www.googleapis.com/auth/admin.directory.user.readonly scope.  The application has an identity, credentials to authenticate, and has been authorized with appropriate access.  Let’s get to some coding (or the mess of garbled logic that accounts for my coding ability 🙂 ).

Google provides API client libraries for a number of languages.  For the purposes of this demonstration, I’ll be using the .NET client library.  Play it smart and leverage the libraries vendors provide for ease of integration as well as avoiding critical mistakes that could impact the security of your API calls.  Now using libraries doesn’t mean you get to ignore what goes on behind the curtains.  It’s critical to understand what the libraries are doing to ensure they’re doing things securely and for the purposes of working out bugs.  APIs in the cloud are changing on a daily basis, don’t count on the libraries keeping up with the vendor pace.  The last thing you want to run into is an situation where your application kicks the bucket due to an API change that isn’t reflected in the library and you’re stuck with a broken production application.

With that lecture out of the way, let’s get into the code for the demo application.

My first step is to open a new project in Visual Studio creating a Visual C# Console App using the .NET Framework.  Before I jump into using Google’s libraries I’m going to do the exact opposite of what I advised you to do above.  Yes folks, I’m going to make a call to the API without using Google’s libraries for the purposes of demonstrating the steps involved in acquiring an access token and sending that access token to the API to retrieve data.  I’ll then demonstrate how much simpler it is to use the library so you’ll appreciate the value of them.

As I explained in my last post, Google APIs use OAuth 2.0 for delegated access to data.  Google does a good job explaining the basic steps in obtaining an access token in this article so I won’t go too deeply into detail.  Instead I’m going to walk through putting these steps into code.  Fair warning, this code is meant for demo purposes only.  This means I’m not going to do any data validation, exception catching, or exercise secure coding practices.  Please please please don’t use my terrible coding practices in any real applications you develop.

Ready?  Let’s begin.

The article above first instructs you to obtain OAuth 2.0 credential which I did in my last entry.  The second step is to obtain an access token from the Google Authorization Server.  I’ll be following the instructions for the OAuth 2.0 for Service Accounts since I’m building a console application for simplicity purposes.

The first thing we need to is collect a few pieces of information.  In the service account scenario we need to know the unique identifier of the service account and the scopes we want to access.  For that I pop open a browser and go to the my project in the console for GCP.  From there I navigate to the APIs & Services section and select the credentials option.

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On the Credentials page I click on my service account to bring up the relevant information.  The service account field contains the unique identifier (or email address as Google refers to it) of the service account I setup.

google2int2.pngThis identifier is one of the pieces of information I’ll need to include in the JSON Web Token (JWT) I’m going to send to Google’s API to obtain an access token.  In addition to the service account identifier, I also need the scopes I want to access, Google Authorization Server endpoint, G-Suite user I’m impersonating, the expiration time I want for the access token (maximum of one hour), and the time the assertion was issued.

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Now that I’ve collected the information I need for my claim set, I need to grab the private key from the PKCS#12 (.p12) certificate I was issued when I setup my service account.

google2int4.pngAt this point I have all the components I need to assemble my JWT.  For that I’m going to add the jose-jwt library to my application and leverage it to simplify the creation of the JWT.

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Once the library is added I create the JWT.  Google requires it include a base64 encoded header, base64 encoded claim set, and the signature providing my application’s identity and ensuring the integrity of the JWT.  The signature is creating using the RSA-SHA256 signing algorithm, which is the only algorithm Google supports at this time.

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I quickly debug the app, dump the token variable to a string, copy it into Fiddler’s Text Wizard and Base64 decode it, we see the JWT we’ll be passing to Google.

google2int7.pngI now have my JWT assembled and am ready to deliver to Google for an access token request.  The next step is to submit it to Google’s authorization server.  For that I need to a do a few things.  The first thing I need to is create a new class that will act as the data model for JSON response Google will return to me after I submit my authorization request.  I’ll deserialize the JWT I receive back using this data model.

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Next I create a new task that will accept a base web address, uri, and a JWT.  The task then uses an instance of the HttpClient class to post it to Google’s authorization server and to capture the JSON response.

google2int9I then call the new task, pass the appropriate parameters, deserialize the JSON response using the data model I created earlier, and dump the bearer access token into a string variable.

google2int10So I have my bearer access token that allows me to impersonate the user within my G-Suite domain for the purposes of hitting the Google Directory API. I whip up another task that will deliver the bearer access token to the Google Directory API and the JSON response which will include details about the user.

google2int11Finally I call the task and dump the JSON response to the console.

google2int12.pngThe results look like the below.

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Victory!  Whew a lot of work there to pull a small amount of data from an API.   I had to create models for the data (you’ll notice I got lazy at the end and didn’t create a user model), properly secure the JWT for the access token request, manage my own http clients, and handle the JSON responses.  It ended up being a fair amount of code for a relatively simple process.

How much easier is it using Google’s .NET library?  Let’s take a look at it.

In the code below I create a service account credential which handles the process of creating the appropriate JWT access token.

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Next up I create a new instance of the DirectoryService class which will represent the connection to the Google Directory API.  This class the assembly of the eventual POST to obtain the bearer access token.

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Last but not least I submit a request for user information for marge.simpson@geekinthweeds.com using the instance of the DirectoryService class I created and placing the JSON response into instance of the User class included in the Directory API which will serve as the data model for the response.

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Just a tad bit easier using the library right? If we break out Fiddler we see that four sessions have been created.

google2int17Session 1 and 2 display the submission of the JWT and acquisition of the bearer token.

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Sessions 3 and 4 display the delivery of the bearer token to the API and the JSON response received back.

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What did we learn from these past two posts (besides the fact I’m a terrible developer)?  We saw programmatic access to G-Suite is very dependent on foundational components of GCP.  Application identities are provisioned in the Google’s IAM Service instance associated with the GCP Project.  The appropriate APIs must be enabled for the GCP project that the application needs to use.  The application must then be granted access to the appropriate authorization scopes within the G-Suite domain and Google provides an option for the application to impersonate users within the G-Suite domain rather than relying upon the user’s consent.

My hope is the biggest lesson you took from these two posts is how valuable it is to understand the inner workings of how a vendor leverages a technology.  Vendor-provided libraries are wonderful and make our lives far easier and our code potentially more simple and secure, but nothing is ever more powerful than understanding the inner workings of the foundational technology.  The knowledge you take at the technology level will translate across all vendors you encounter making grasping that new product all that much easier.  Take your time, dig deep into the weeds, and enjoy the journey into the technology.

See you in the next post where I’ll wrap up this series and break down the Microsoft Azure AD identity provisioning integration with G-Suite.  Have a great week!

 

 

Integrating Azure AD and G-Suite – Google API Integration Part 1

Integrating Azure AD and G-Suite – Google API Integration Part 1

Hi everyone,

Welcome to the second post in my series on the integration between Azure Active Directory (Azure AD) and Google’s G-Suite (formally named Google Apps).  In my first entry I covered the single sign-on (SSO) integration between the two solutions.  This included a brief walkthrough of the configuration and an explanation of how the SAML protocol is used by both solutions to accomplish the SSO user experience.  I encourage you to read through that post before you jump into this.

So we have single sign-on between Azure AD and G-Suite, but do we still need to provision the users and group into G-Suite?  Thanks to Google’s Directory Application Programming Interface (API) and Azure Active Directory’s (Azure AD) integration with it, we can get automatic provisioning into G-Suite .  Before I cover how that integration works, let’s take a deeper look at Google’s Cloud Platform (GCP) and its API.

Like many of the modern APIs out there today, Google’s API is web-based and robust. It was built on Google’s JavaScript Object Notation (JSON)-based API infrastructure and uses Open Authorization 2.0 (OAuth 2.0) to allow for delegated access to an entities resources stored in Google. It’s nice to see vendors like Microsoft and Google leveraging standard protocols for interaction with the APIs unlike some vendors… *cough* Amazon *cough*. Google provides software development kits (SDKs) and shared libraries for a variety of languages.

Let’s take a look at the API Explorer.  The API Explorer is a great way to play around with the API without the need to write any code and to get an idea of the inputs and outputs of specific API calls.  I’m first going to do something very basic and retrieve a listing of users in my G-Suite directory.  Once I access the API explorer I hit the All Versions menu item and select the Admin Directory API.

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On the next screen I navigate down to the directory.users.list method and select it.  On the screen that follows I’m provided with a variety of input fields.  The data I input into these fields will affect what data is returned to me from the API. I put the domain name associated with my G-Suite subscription and hit the Authorize and Execute button.  A new window pops up which allows me to configure which scope of access I want to grant the API Explorer.  I’m going to give it just the scope of https://www.googleapis.com/auth/admin.directory.user.readonly.

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I then hit the Authorize and Execute button and I’m prompted to authenticate to Google and delegate API Explorer to access data I have permission to access in my G-Suite description.   Here I plug in the username and password for a standard user who isn’t assigned to any G-Suite Admin Roles.

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After successfully authenticating, I’m then prompted for consent to delegate API Explorer to view the users configured in the user’s G-Suite directory.

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I hit the allow button, the request for delegated access is complete, and a listing of users within my G-Suite directory are returned in JSON format.

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Easy right?  How about we step it up a notch and create a new user.  For that operation I’ll be delegating access to API Explorer using an account which has been granted the G-Suite User Management Admin role.  I navigate back to the main list of methods and choose the directory.users.insert method.  I then plug in the required values and hit the Authorize and Execute button.  The scopes menu pops up and I choose the https://www.googleapis.com/auth/admin.directory.user scope to allow for provisioning of the user and then hit the Authorize and Execute button.  The request is made and a successful response is returned.

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Navigating back to G-Suite and looking at the listing of users shows the new user Marge Simpson as appearing as created.

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Now that we’ve seen some simple samples using API Explorer let’s talk a bit about how you go about registering an application to interact with Google’s API as well as covering some basic Google Cloud Platform (GCP) concepts.

First thing I’m going to do is navigate to Google’s Getting Started page and create a new project.  So what is a project?  This took a bit of reading on my part because my prior experience with GCP was non-existent.  Think of a Google Project like an Amazon Web Services (AWS) account or a Microsoft Azure Subscription.  It acts as a container for billing, reporting, and organization of GCP resources.  Projects can be associated with a Google Cloud Organization (similar to how multiple Azure subscriptions can be associated with a single Microsoft Azure Active Directory (Azure AD) tenant) which is a resource available for a G-Suite subscription or Google Cloud Identity resource.  The picture below shows the organization associated with my G-Suite subscription.

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Now that we have the concepts out of the way, let’s get back to the demo. Back at the Getting Started page, I click the Create a new project button and authenticate as the super admin for my G-Suite subscription. I’ll explain why I’m using a super admin later. On the next screen I name the project JOG-NET-CONSOLE and hit the next button.

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The next screen prompts me to provide a name which will be displayed to the user when the user is prompted for consent in the instance I decide to use an OAuth flow which requires user consent.

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Next up I’m prompted to specify what type of application I’m integrated with Google.  For this demonstration I’ll be creating a simple console app, so I’m going to choose Web Browser simply to move forward.  I plug in a random unique value and click Create.

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After creation is successful, I’m prompted to download the client configuration and provided with my Client ID and Client Secret. The configuration file is in JSON format that provides information about the client’s registration and information on authorization server (Google’s) OAuth endpoints. This file can be consumed directly by the Google API libraries when obtaining credentials if you’re going that route.

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For the demo application I’m building I’ll be using the service account scenario often used for server-to-server interactions. This scenario leverages the OAuth 2.0 Client Credentials Authorization Grant flow. No user consent is required for this scenario because the intention of the service account scenario is it to access its own data. Google also provides the capability for the service account to be delegated the right to impersonate users within a G-Suite subscription. I’ll be using that capability for this demonstration.

Back to the demo…

Now that my application is registered, I now need to generate credentials I can use for the service account scenario. For that I navigate to the Google API Console. After successfully authenticating, I’ll be brought to the dashboard for the application project I created in the previous steps. On this page I’ll click the Credentials menu item.

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The credentials screen displays the client IDs associated with the JOG-NET-CONSOLE project.  Here we see the client ID I received in the JSON file as well as a default one Google generated when I created the project.

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Next up I click the Create Credentials button and select the Service Account key option.  On the Create service account key page I provide a unique name for the service account of Jog Directory Access.

The Role drop down box relates to the new roles that were introduced with Google’s Cloud IAM.

You can think of Google’s Cloud IAM as Google’s version of Amazon Web Services (AWS) IAM  or Microsoft’s Azure Active Directory in how the instance is related to the project which is used to manage the GCP resources.  When a new service account is created a new security principal representing the non-human identity is created in the Google Cloud IAM instance backing the project.

Since my application won’t be interacting with GCP resources, I’ll choose the random role of Logs Viewer.  When I filled in the service account name the service account ID field was automatically populated for me with a value.  The service account ID is unique to the project and represents the security principal for the application.   I choose the option to download the private key as a PKCS12 file because I’ll be using the System.Security.Cryptography.X509Certificates namespace within my application later on.  Finally I click the create button and download the PKCS12 file.

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The new service account now shows in the credential page.

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Navigating to the IAM & Admin dashboard now shows the application as a security principal within the project.

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I now need to enable the APIs in my project that I want my applications to access.  For this I navigate to the API & Services dashboard and click the Enable APIs and Services link.

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On the next page I use “admin” as my search term, select the Admin SDK and click the Enable button.  The API is now enabled for applications within the project.

From here I navigate down to the Service accounts page and edit the newly created service account.

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At this point I’ve created a new project in GCP, created a service account that will represent the demo application, and have given that application the right to impersonate users in my G-Suite directory.  I now need to authorize the application to access the G-Suite’s data via Google’s API.  For that I switch over to the G-Suite Admin Console and authenticate as a super admin and access the Security dashboard.  From there I hit the Advanced Setting option and click the Manage API client access link.

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On the Manage API client access page I add a new entry using the client ID I pulled previously and granting the application access to the  https://www.googleapis.com/auth/admin.directory.user.readonly scope.  This allows the application to impersonate a user to pull a listing of users from the G-Suite directory.

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Whew, a lot of new concepts to digest in this entry so I’ll save the review of the application for the next entry.  Here’s a consumable diagram I put together showing the relationship between GCP Projects, G-Suite, and a GCP Organization.  The G-Suite domain acts as a link to the GCP projects.  The G-Suite users can setup GCP projects and have a stub identity (see my first entry LINK) provisioned in the project.  When an service account is created in a project and granted G-Suite Domain-wide Delegation, we use the Client ID associated with the service account to establish an identity for the app in the G-Suite domain which is associated with a scope of authorized access.

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In this post I covered some basic GCP concepts and saw that the concepts are very similar to both Microsoft and AWS.  I also covered the process to create a service account in GCP and how all the pieces come together to programmatic access to G-Suite resources.  In my next entry I’ll demo some simple .NET applications and walk through the code.

Have a great weekend and go Pats!

Azure AD ASP .NET Web Application

Hi all. Before I complete my series on Azure AD Provisioning with a look at how provisioning works with the Graph API, I want to take a detour and cover some Microsoft Visual Studio. Over the past month I’ve been spending some time building very basic applications.

As I’ve covered previously in my blog, integration is going to the primary responsibility of IT Professionals as more infrastructure shifts to the cloud. Our success at integration will largely depend on how well we understand the technologies, our ability to communicate what business problems these technologies can solve, and our understanding of how to help developers build applications to consume these technologies. In my own career, I’ve spent a significant time on all of the above except the last piece. That is where I’m focusing now.

Recently I built a small.NET forms application that integrated with the new Azure AD B2B API. Over the past few days I’ve been spending time diving with to ASP .NET Web Applications built with an MVC architecture. I decided to build an small MVC application that performed queries against the Graph API, and wow was it easy. There was little to no code that I had to provide myself and “it just worked”. You can follow these instructions if you’d like to play with it as well. If you’ve read this blog, you know I don’t do well with things that just work. I need to know how it works.

If you follow the instructions in the above link you will have a ASP .NET Web Application that is integrated with your Azure AD tenant, uses Azure AD for authentication via the Open ID Connect protocol, and is capable of reading directory data from the Graph API uses OAuth 2.0. So how is all this accomplished? What actually happened? What protocol is being used for authentication, how does the application query for directory data? Those are the questions I’ll focus on answering in this blog post.

Let’s first answer the question as to what Visual Studio did behind the scenes to integrate the application with Azure AD. In the explanation below I’ll be using the technology terms (OAuth 2.0 and Open ID Connect) in parentheses to translate the Microsoft lingo. During the initialization process Visual Studio communicated with Azure AD (Authorization Server) and registered (registration) the application (confidential client) with Azure AD as a Web App and gave it the delegated permissions (scopes) of “Sign in and read user profile” and “Read directory data”.

In addition to the registration of the application in Azure AD, a number of libraries and code have been added to the project that make the authentication and queries to the Graph API “out of the box”. All of the variables specific to the application such as Client ID, Azure AD Instance GUID, application secret key are stored in the web.config. The Startup.cs has the simple code that adds Open ID Connect authentication. Microsoft does a great job explaining the Open ID Connect code here. In addition to the code to request the Open ID Connect authentication, there is code to exchange the authorization code for an access token and refresh token for the Graph API as seen below with my comments.

AuthorizationCodeReceived = (context) =>
{
var code = context.Code;
// MF -> Create a client credential object with the Client ID and Application key
ClientCredential credential = new ClientCredential(clientId, appKey);
// MF -> Extract the access token from cache and generate an authentication context from it
string signedInUserID = context.AuthenticationTicket.Identity.FindFirst(ClaimTypes.NameIdentifier).Value;
AuthenticationContext authContext = new AuthenticationContext(Authority, new ADALTokenCache(signedInUserID));
// MF -> Acquire a token by submitting the authorization code, providing the URI that is registered for the application, the application secret key, and the resource (in this scenario the Graph API)
AuthenticationResult result = authContext.AcquireTokenByAuthorizationCode(
code, new Uri(HttpContext.Current.Request.Url.GetLeftPart(UriPartial.Path)), credential, graphResourceId);
return Task.FromResult(0);
}

Now that the user has authenticated and the application has a Graph API access token, I’ll hop over to the UserProfileController.cs. The code we’re concerned about in here is below with my comments.


{
Uri servicePointUri = new Uri(graphResourceID);
Uri serviceRoot = new Uri(servicePointUri, tenantID);
ActiveDirectoryClient activeDirectoryClient = new ActiveDirectoryClient(serviceRoot, async () => await GetTokenForApplication());
// MF -> Use the access token previous obtained to query the Graph API
var result = await activeDirectoryClient.Users.Where(u => u.ObjectId.Equals(userObjectID)).ExecuteAsync();
IUser user = result.CurrentPage.ToList().First();
return View(user);
}

Next I’ll hop over to the UserProfile view to look at the Index.cshtml. In this file a simple table is constructed that returns information about the user from the Graph API. I’ve removed some of the pesky HTML and replaced it with the actions.


@using Microsoft.Azure.ActiveDirectory.GraphClient
@model User
@{
ViewBag.Title = "User Profile";
}
"CREATE TABLE"
"TABLE ROW"
Display Name
@Model.DisplayName
"TABLE ROW"
First Name
@Model.GivenName
"TABLE ROW"
Last Name
@Model.Surname
"TABLE ROW"
Email Address
@Model.Mail

Simple right? I can expand that table to include any attribute exposed via the Graph API. As you can see in the above, I’ve added email address to the display. Now that we’ve reviewed the code, let’s cover the steps the application takes and what happens in each step:

  1. App accesses https://login.microsoftonline.com//.well-known/openid-configuration
    • Get OpenID configuration for Azure AD
  2. App accesses https://login.microsoftonine.com/common/discovery/keys
    • Retrieve public-keys used to verify signature of open id connect tokens
  3. User’s browser directed to https://login.microsoftonline.com//oauth2/authorize
    • Request an open id connect id token and authorization code for user’s profile information
  4. User’s browser directed to https://login.microsoftonline.com//login
    • User provides credentials to AAD and receives back
      1. id token
      2. access code for graph API with Directory.Read, User.Read scope
  5. User’s browser directed back to application
    • Return id token and access code to application
      1. id token authenticates user to application
      2. Access code for graph API with Directory.Read, User.Read scope temporarily stored
  6. Application accesses https://login.microsftonine.com//oauth2/token
    • Exchanges access code for bearer token
  7. Application sends OData query to Graph API and attaches bearer token.

That’s it folks! In my next post I will complete the Azure AD Provisioning series with a simple ASP .NET Web app that provisions new users into Azure AD.

Azure AD User Provisioning – Part 2

Hello again. Today I will continue this series by examining the GUI options available within Microsoft’s Azure offerings to provision new user accounts. I am going to focus on member user objects and not guests for this series.

There are four native GUI options available that can be used to provision new user accounts in Azure Active Directory.

  1. Office 365 Administration Center
  2. Azure Management Portal
  3. Azure Portal
  4. ADUC/ADAC then synchronization to Azure AD

I’ll start with the Office 365 Admin Center. The Office 365 Administration Center is where most business will find themselves provisioning user accounts due to the popularity of the products under the Office 365 umbrella. The Admin Center provides an interface that is sleek and simple to navigate. The simplicity comes with a price. Administration of many aspects of Azure AD must be done outside of the Admin Center. This registering custom applications and applications from the application gallery, creation of additional directories such as B2C directories, B2B imports, and much more. Microsoft seemingly intends this interface to be business friendly administration endpoint for the Office 365 suite and rightfully assumes the consumers of this endpoint need simplicity.

I’ll now create a new user account. We first need navigate and login to the Office 365 portal. After the user authenticates the Office 365 home page that lists out the various applications the user has access to. I’ll next click on the Admin icon to enter the Admin Center. Next I will navigate to the Users section and select the Active Users section. This will bring us up a listing of the users currently in the Azure AD tenant associated with the Office 365 subscription.

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When I hit the Add User button a new blade opens where the key components of the user’s account can be configured. This includes the first name, last name, user name and the like as seen in the screenshot below.

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Let’s take some time to dig through the remaining sections.

First up is the contact information section. On-premise Active Directory administrators will recognize these fields from the various tabs in ADUC.

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Next up is the password section. Here I have the option of creating a password or auto-generating a password and turning on or off the enforcement of a password change at first sign-in. I don’t recall there being an option to create a password a few months back when I was playing with the Admin Center, but that is one of the many lovely aspects of SaaS, continuous change and improvement.

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Next up is the Roles section. Here there is an option to assign the user to the standard Azure AD roles or Office 365 roles. You can read more about these roles here.

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Finally, the Licenses section allows for assignment of Azure AD and Office 365 licenses to the user account.

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After the user is created it can be modified by clicking on the user object. Contact information about the user, membership into Azure AD groups, MFA enforcement, and product specific settings for the user can be modified in this blade.

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The restoration of deleted users is simple and quick via the Deleted Users section. If only Microsoft had made it this easy in the old days of on-premises Active Directory prior to the Active Directory Administration Center.

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Users can also be added in bulk by uploading a CSV file by hitting the More button in the Active Users section.

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The Office 365 Admin Center interface is sleek and simple, perfect for a business user or Tier 1 support staff. So what’s the problem? No matter how simple the interface, it’s another process and interface staff need to learn. There is also no way to technically enforce standards for data input. What if what user puts MA and another puts Massachusetts? What about a user who misspells accountant in the job title field? Human error and lack of standardization can make for some nasty operational headaches, not to mention security risks. If an organization wants to limit the new processes and interfaces its staff needs to learn (because really, where is the business value in that?) as well as making sure the data about a user is standardized and correct, making these changes programmatically is the way to go.

In my next post I’ll cover both the Azure Management Portal and the Azure Portal.