Capturing and Visualizing Office 365 Security Logs – Part 1

Welcome back again my fellow geeks!

I’ve been busy over the past month nerding out on some pet projects.  I thought it would be fun to share one of those pet projects with you.  If you had a chance to check out my last series, I walked through my first Python experiment which was to write a re-usable tool that could be used to pull data from Microsoft’s Graph API (Microsoft Graph).

For those of you unfamiliar with Microsoft Graph, it’s the Restful API (application programming interface) that is used to interact with Microsoft cloud offerings such as Office 365 and Azure.  You’ve probably been interacting with it without even knowing it if through the many PowerShell modules Microsoft has released to programmatically interact with those services.

One of the many resources which can be accessed through Microsoft Graph are Azure AD (Active Directory) security and audit reports.  If you’re using Office 365, Microsoft Azure, or simply Azure AD as an identity platform for SSO (single sign-on) to third-party applications like SalesForce, these reports provide critical security data.  You’re going to want to capture them, store them, and analyze them.  You’re also going to have to account for the window that Microsoft makes these logs available.

The challenge is they are not available via the means logs have traditionally been captured on-premises by using syslogd, installing an SIEM agent, or even Windows Event Log Forwarding.  Instead you’ll need to take a step forward in evolving the way you’re used to doing things. This is what moving to the cloud is all about.

Microsoft allows you to download the logs manually via the Azure Portal GUI (graphical user interface) or capture them by programmatically interacting with Microsoft Graph.  While the former option may work for ad-hoc use cases, it doesn’t scale.  Instead we’ll explore the latter method.

If you have an existing enterprise-class SIEM (Security Information and Event Management) solution such as Splunk, you’ll have an out of box integration.  However, what if you don’t have such a platform, your organization isn’t yet ready to let that platform reach out over the Internet, or you’re interested in doing this for a personal Office 365 subscription?  I fell into the last category and decided it would be an excellent use case to get some experience with Python, Microsoft Graph, and take advantage of some of the data services offered by AWS (Amazon Web Services).   This is the use case and solution I’m going to cover in this post.

Last year I had a great opportunity to dig into operational and security logs to extract useful data to address some business problems.  It was my first real opportunity to examine large amounts of data and to create different visualizations of that data to extract useful trends about user and application behavior.  I enjoyed the hell out of it and thought it would be fun to experiment with my own data.

I decided that my first use case would be Office 365 security logs.  As I covered in my last series my wife’s Office 365 account was hacked.  The damage was minor as she doesn’t use the account for much beyond some crafting sites (she’s a master crocheter as you can see from the crazy awesome Pennywise The Clown she made me for Christmas).

img_4301

The first step in the process was determining an architecture for the solution.  I gave myself a few requirements:

  1. The solution must not be dependent on my home lab infrastructure
  2. Storage for the logs must be cheap and readily available
  3. The credentials used in my Python code needs to be properly secured
  4. The solution must be automated and notify me of failures
  5. The data needs to be available in a form that it can be examined with an analytics solution

Based upon the requirements I decided to go the serverless (don’t hate me for using that tech buzzword ūüôā ) route.  My decisions were:

  • AWS Lambda would run my code
  • Amazon CloudWatch Events would be used to trigger the Lambda once a day to download the last 24 hours of logs
  • Amazon S3 (Simple Storage Service) would store the logs
  • AWS Systems Manager Parameter Store would store the parameters my code used leveraging AWS KMS (Key Management Service) to encrypt the credentials used to interact with Microsoft Graph
  • Amazon Athena would hold the schema for the logs and make the data queryable via SQL
  • Amazon QuickSight would be used to visualize the data by querying Amazon Athena

The high level architecture is pictured below.

untitled

I had never done a Lambda before so I spent a few days looking at some examples and doing the typical Hello World that we all do when we’re learning something new.  From there I took the framework of Python code I put together for general purpose queries to the Microsoft Graph, and adapted it into two Lambdas.  One Lambda would pull Sign-In logs while the other would pull Audit Logs.  I also wanted a repeatable way to provision the Lambdas to share with others and get some CloudFormation practice and brush up on my very dusty Bash scripting.   The results are located here in one of my Github repos.

I’m going to stop here for this post because we’ve covered a fair amount of material.  Hopefully after reading this post you understand that you have to take a new tact with getting logs for cloud-based services such as Azure AD.  Thankfully the cloud has brought us a whole new toolset we can use to automate the extraction and storage of those logs in a simple and secure manner.

In my next post I’ll walk through how I used Athena and QuickSight to put together some neat dashboards to satisfy my nerdy interests and get better insight into what’s happening on a daily basis with my Office 365 subscription.

See you next post and go Pats!

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!