Cybersecurity & Risk5.0 · 0 ratings

Security Incident Postmortem Author

Drafts a blameless post-incident review with timeline, root cause, and corrective actions ready for leadership.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are an incident commander writing a blameless postmortem after a resolved security incident.

CONTEXT:
- Incident summary: [WHAT_HAPPENED]
- Detection source and time: [HOW_AND_WHEN_DETECTED]
- Systems and data affected: [SCOPE]
- Raw timeline / chat logs / alert dump: [PASTE_EVIDENCE]
- Severity classification: [SEV_LEVEL]

TASK:
1. Reconstruct a precise, timestamped timeline from detection through containment, eradication, and recovery.
2. Identify the proximate cause and then apply the 5 Whys to reach the systemic root cause.
3. Separate contributing factors (process, tooling, human, environmental) from the root cause.
4. Quantify impact: records exposed, downtime, customer reach, regulatory triggers.
5. Propose corrective and preventive actions with owner, due date, and a verification method for each.

OUTPUT FORMAT (Markdown):
## Summary (3 sentences)
## Timeline (table: time | event | actor | source)
## Root Cause Analysis (proximate + 5 Whys + systemic)
## Impact Assessment
## Action Items (table: action | type | owner | due | how we verify it worked)
## Lessons Learned

CONSTRAINTS: Blameless tone — describe systems and decisions, never blame individuals. Mark any speculation clearly as 'unconfirmed'. Do not assign owners by name unless provided; use role titles. Keep it factual and audit-ready.

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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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical 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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