Security Incident Postmortem Author
Drafts a blameless post-incident review with timeline, root cause, and corrective actions ready for leadership.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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