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06-10-2022 01:08 AM
Hello guys,
I'm currently trying to create a Playbook that auto-categorize already analyzed phishing email, let me explain :
Here is the current process :
1. An analyst tags an email as Phishing using Outlook categories in the main Email box
2. Thanks to a macro, the email is being put in a phishing email folder in outlook
Now, I'd like Cortex XSOAR to :
3. Checks the Outlook phishing folder and get Sender Email Address + Subject
4. Auto-tag as phishing the emails in the main email box that have the same Sender Email Address + Subject that XSOAR got in step 3.
Could you please guys help to achieve this (if doable), I'm kinda lost right now and don't really know where to start, looking at the docs here are the ideas I have : Use microsoft graphs mails to get sender email and subject, then create a rule in office for each... Not sure if the best..
Thank you guys
06-10-2022 01:59 AM
While it's probably possible to get it to work the way you're describing, you're going to run into some issues like the lack of email tagging support in the O365 integrations.
Your best bet is probably to create incidents for all incoming emails to the main inbox, and then:
* Use a data collection/manual conditional task to get the verdict from your analyst on if the incident raised off an email is phishing or not.
* Store the verdict, along with the sender/subject in the incident fields, as we'll need them later.
* If it is phishing, use msgraph-mail-move-email to move the email to the phishing inbox
* Use pre-processing rules to check emails against existing incidents with duplicate sender+subject
* If detected, move the new email to the phishing inbox (either with a script, or with "Link" then a playbook which detects the linking and self-closes once the mail is moved)
This means you have to replicate some of the existing Outlook logic for email moving on the Outlook side, but it should be simpler to implement overall.
06-10-2022 02:03 AM
Hello Chrking, thank you so much for your very quick and complete answer, I deeply appreciate.
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