Monitor Gmail or Outlook, analyze emails with ChatGPT, classify threats, and auto-create Jira tickets with evidence.
The AI agent monitors Gmail or Outlook for new messages, extracts content and headers, and converts the body to a screenshot for clear review. It uses ChatGPT to assess phishing indicators and determine risk, producing a structured verdict. Finally, it creates Jira tickets with detailed analysis and attachments, enabling auditable, rapid incident response.
A concise, action-focused summary of the steps of the AI agent.
Ingests emails from Gmail or Outlook into the analysis path.
Extracts and organizes email content, metadata, and headers.
Converts the email body to a screenshot for review.
Uses ChatGPT to evaluate phishing indicators and risk.
Classifies each email as malicious or benign with supporting context.
Creates Jira tickets with the analysis, attachments, and evidence.
The AI agent eliminates manual triage by standardizing analysis, evidence capture, and ticket creation. It enables faster containment with auditable decisions and consistent workflows across teams.
A simple 3-step flow that non-technical users can follow.
The AI agent retrieves new messages from Gmail or Outlook, preserving headers and attachments, and streams them into the analysis pipeline.
The AI agent uses ChatGPT to evaluate body content and headers for phishing indicators, risk signals, and intent, returning a verdict and supporting evidence.
The AI agent labels the email (malicious or benign), creates a Jira ticket with analysis, artifacts, and evidence, and stores results for audit.
A realistic scenario showing timing, tasks, and outcomes.
Scenario: During a phishing campaign, the AI agent processes 120 inbound emails in 60 minutes. It flags 9 messages as potentially malicious, captures body screenshots, and creates 9 Jira tickets with detailed analysis and attachments. The dashboard provides an overall incident view with artifacts for leadership review.
Which roles gain from automated email threat analysis.
Receives prioritized, verified threat tickets with complete artifacts.
Gets timely, auditable case records suitable for post-incident review.
Gains consolidated visibility across campaigns and risk levels.
Delivers standardized phishing triage to multiple clients with consistent tooling.
Reduces manual triage workload and accelerates remediation workflows.
Obtains documented evidence and ticket history for regulatory audits.
Connects with email, screenshot, AI, and issue-tracking tools.
Fetches new messages, extracts subject and headers, and passes data to the AI agent for analysis.
Ingests inbound emails from Outlook, preserving metadata and attachments for analysis.
Renders the email body into a screenshot attached to Jira tickets for review.
Runs the phishing-detection prompt to produce a verdict, rationale, and risk score.
Automates ticket creation and updates with analysis, evidence, and links to artifacts.
Concrete scenarios where this AI agent adds measurable value.
Common questions and practical answers.
Yes. Attachments and images are preserved and stored alongside the analysis in each Jira ticket. The agent ensures sensitive content is handled according to your security policies. You can configure which attachments to include and how to redact content. The process maintains a clear audit trail and links artifacts to the corresponding email.
All data in transit uses TLS 1.2+ encryption, and at-rest data is encrypted with strong keys managed by your cloud provider. Access is restricted via RBAC, and audit logs track actions. The AI analysis results are stored in Jira with permissions and version history. If needed, the agent can run within on-prem or private clouds to meet data residency requirements.
Yes. You can map different email sources to separate Jira projects or issue types, and tailor ticket fields for each client or domain. The agent supports per-tenant prompts and per-project templates to enforce consistent reporting. Changes apply to new tickets without impacting existing cases. You can also define custom transitions and statuses.
The model provides a confidence score and justification for each decision, enabling analysts to review and adjust as needed. You can tune prompts to affect sensitivity and include feedback loops. Periodic evaluation against known datasets helps recalibrate risk thresholds. Analysts retain final authority in Jira with a clear audit trail.
Yes. The agent supports Gmail and Outlook sources and can be extended to other providers. You can configure per-domain policies, preserve domain-level metadata, and apply domain-specific prompts. The system scales with volume while maintaining security controls.
You configure email credentials in your automation tool, provide API keys for the AI service and the HTML-to-image service, and define Jira project mappings. The agent prompts can be customized to fit your threat-model criteria. A test run validates email ingestion, analysis, and ticket creation before going live.
Yes. The prompts are modular and editable, allowing you to refine indicators, risk scoring, and confidence thresholds. You can run dry-runs against historical emails to validate behavior. Regular prompt reviews help adapt to evolving phishing tactics. Changes apply to new analyses and can be version-controlled.
Monitor Gmail or Outlook, analyze emails with ChatGPT, classify threats, and auto-create Jira tickets with evidence.
