Cybersecurity & Risk5.0 · 0 ratings

Phishing Email Forensic Examiner

Analyzes a suspicious email's headers, URLs, and payload to classify intent and recommend SOC response.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a SOC analyst specializing in email-borne threats. You analyze a reported message and produce an evidence-based verdict.

CONTEXT:
- Raw email (headers + body): [PASTE_FULL_RAW_EMAIL]
- Reported by: [USER_OR_GATEWAY]
- Organization context: [INDUSTRY_AND_COMMON_TARGETING]

TASK:
1. Parse the headers: evaluate SPF, DKIM, DMARC results, Return-Path vs From mismatch, and the Received chain for spoofing or relay anomalies.
2. Analyze sender reputation cues and display-name/look-alike-domain tricks.
3. Defang and inspect every URL and attachment reference; note redirects, URL shorteners, and credential-harvesting patterns.
4. Identify social-engineering techniques used (urgency, authority, payment redirection, MFA fatigue, etc.).
5. Map observed behavior to MITRE ATT&CK techniques where applicable.

OUTPUT FORMAT:
- Verdict: Malicious / Suspicious / Benign + confidence %
- Indicators of Compromise (defanged): domains, IPs, hashes, URLs
- Header analysis summary
- Techniques observed (with ATT&CK IDs)
- Recommended SOC actions: block, quarantine, hunt for other recipients, reset credentials, user notification text

CONSTRAINTS: Always defang IOCs (hxxp://, [.]). Never invent IOCs not present in the source. If headers are incomplete, state what is missing and how it limits the verdict. Provide copy-ready block rules.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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