Analyze & Sort Suspicious Email Contents with ChatGPT and Jira Who is this for? This workflow is tailored for IT security teams, managed service providers (MSPs), and organizations aiming to streamline the detection and reporting of phishing emails. It's especially useful for teams handling high email volumes and requiring quick, automated analysis. What problem is this workflow solving? Phishing emails pose a significant cybersecurity threat, and manual review processes are time-consuming and prone to human error. This workflow automates the identification of malicious emails, provides AI-driven insights, and generates structured reports, enabling faster and more efficient responses to email-based threats. What this workflow does This workflow integrates Gmail or Microsoft Outlook to monitor and capture incoming emails. It processes the email content and headers, converts the email's body to a visual screenshot for clarity, and uses ChatGPT's advanced AI to analyze the email for phishing indicators. Based on the analysis, it categorizes emails as potentially malicious or benign, creating detailed Jira tickets for each case.
Tags
Jira SoftwareOpenAIRelated workflows
See all AI Automation→
WSend Calendly meeting prep briefs with Gmail, Notion, OpenAI and Sheets
Quick Overview This workflow monitors Gmail for Calendly “New Event” emails, extracts meeting details with OpenAI, enriches the invitee with Notion CRM data, Google Sheets STR records, and SerpApi building research, then generates and schedules a meeting-prep brief email to send 30 minutes before the meeting. How it works Triggers every minute when a new Gmail message arrives and continues only if it is a Calendly notification for a new event. Uses OpenAI to extract the invitee email and meeting date/time from the email and calculates a send time 30 minutes before the meeting. Looks up the lead in a Notion database by invitee email and pulls building research via SerpApi, then uses OpenAI to extract building details from the AI Overview results. Loads STR registration data from Google Sheets and matches it to the building address to flag whether the property appears in STR records. Searches recent Gmail history in two accounts for messages involving the lead’s email address and checks whether any history exists. If email history exists, uses OpenAI to summarize the relationship and generate a concise meeting brief, then formats it as an HTML email. If no email history exists, uses
Verify loan documents and score eligibility with Claude and Google Sheets
Quick overview This workflow receives loan applications via webhook, uses Anthropic Claude to verify document text and generate an underwriting recommendation, calculates an explainable risk score in code, logs each application to Google Sheets, and sends decision emails through SendGrid before returning a JSON response. How it works Receives a loan application via a POST webhook, including applicant details and extracted text from ID, income proof, and bank statement documents. Uses Anthropic Claude to extract key fields from the documents and cross-checks them against the applicant’s declared name, employer, and income to produce a verification score and inconsistency list. Stops early and returns a “Documents Insufficient” webhook response when the verification score is below the threshold. Calculates verified income, estimated monthly installment, debt-to-income and loan-to-income ratios, and a weighted 0–100 risk score and tier from credit score, ratios, employment tenure, bank health, and document consistency. Waits briefly, then uses Anthropic Claude to generate an eligibility recommendation (Approve, Conditional Approval, Manual Review, or Decline) with reasoning, condition
Triage supplier capacity risk with Groq, Gmail, Slack, and Google Sheets
Quick overview This workflow runs daily to pull supplier performance data from an HTTP endpoint, calculate fulfillment and delay risk metrics, use Groq (Llama 3.3) to classify supplier risk and generate mitigation actions, then notify via Gmail and Slack and log outcomes to Google Sheets. How it works Runs every day at the scheduled time. Fetches supplier performance data from an HTTP API and stops if the supplier has no orders. Normalizes the supplier fields and calculates fulfillment rate, delay, rule-based flags, and a risk score. Sends the metrics to Groq (Llama 3.3) to classify risk (Low/Medium/High) with a reason and confidence, then merges the AI result with the computed metrics (falling back to the rule-based classification if parsing fails). If the AI risk is Medium, Groq generates three mitigation actions and the workflow fetches backup supplier options from an HTTP API, then combines both into a single alert payload. Sends the mitigation actions by Gmail email and Slack message, then appends a “high risk” audit row to Google Sheets. If the AI risk is not Medium, appends a “no alert sent” audit row to Google Sheets with the assessed risk details. Setup Add Groq API creden