Manage incident reporting in PagerDuty and CrateDB

This n8n workflow automates the analysis of email messages received in a Microsoft Outlook inbox to identify indicators of compromise (IOCs), specifically suspicious URLs. It can be triggered manually or scheduled to run daily at midnight. The workflow begins by retrieving up to 100 read email messages from the Outlook inbox. However, there seems to be a configuration issue as it should retrieve unread messages, not read ones. It then marks these messages as read to avoid processing them again in the future. The messages are then split into individual items using the Split In Batches node for sequential processing. For each email, the workflow analyzes its content to find URLs, which are considered potential IOCs. If URLs are found, the workflow proceeds to check these URLs for potential threats using two services, URLScan.io and VirusTotal, in parallel. In the first path, URLScan.io scans each URL, and if there are no errors, the results from URLScan.io and VirusTotal are merged. If there are errors, the workflow waits 1 minute before attempting to retrieve the URLScan results again. The loop then continues for the next email. In the second path, VirusTotal is used to scan the URLs, and the results are retrieved. Finally, the workflow checks if the data field is not empty, filtering out items where no data was found. It then sends a summarized Slack message to report details about the analyzed email, including the subject, sender, date, URLScan report URL, and VirusTotal verdict for URLs that were reported as malicious. Potential issues during setup include configuring the Outlook node to retrieve unread messages, resolving a configuration issue in the VirusTotal node, and handling authentication and API keys for both URLScan.io and VirusTotal nodes. Additionally, proper error handling and testing with various email content types and URLs are essential to ensure the workflow accurately identifies IOCs and reports them to the Slack channel.

With workflow, you analyze Email with TheHive/Cortex https://github.com/V1D1AN/S1EM/wiki/Soar-guide

This n8n workflow serves as a powerful cybersecurity and threat intelligence tool to look up URLs or IP addresses through industry standard threat intelligence vendors. It starts with either a form submission or a webhook trigger, allowing users to input data, URLs or IPs that require analysis. The workflow then splits into two paths depending on whether the input data is an IP or URL. If an IP was given, it sets the ip variable to the IP; however if a URL was given the workflow will perform a DNS lookup using Google Public DNS and sets the ip variable based on the results from Google. The workflow then checks the obtained IP addresses against GreyNoise services, with one branch utilizing GreyNoise RIOT IP Lookup to assess IP reputation and association with known benign services, and the other using GreyNoise IP Context to evaluate potential threats. The results from both GreyNoise services are merged to create a comprehensive analysis which includes the IP, classification (benign, malicious, or unknown), IP location, tags to identify activity or malware, category, and trust level. In parallel, a VirusTotal scan is initiated for the URL/IP to identify if it is malicious. A 5-second wait ensures proper processing, and the workflow subsequently polls the scan result to determine when the analysis is complete. The workflow then summarizes the analysis including the overall security vendor analysis results, blockList analysis, OpenPhish analysis, the URL, and the IP. Finally, the workflow combines the summarized intelligence from both GreyNoise and VirusTotal to provide a thorough analysis of the URL/IP. This summarized intelligence can then be emailed to the user that filled out the form via Gmail or it can be sent to the user via a Slack message. Setting up this workflow may require proper configuration of the form submission or webhook trigger, and ensuring that the GreyNoise and VirusTotal API credentials are correctly integrated. Users should also consider the potential volume of data and API rate limits, as excessive requests could lead to issues. Proper documentation and validation of input data are crucial to ensure accurate and meaningful results in the final report.