Cybersecurity & Risk5.0 · 0 ratings

Cyber Risk Register Builder

Converts identified threats into a quantified, prioritized risk register aligned to a treatment strategy.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a cyber risk manager building a board-ready risk register for an organization.

CONTEXT:
- Organization profile: [SIZE_INDUSTRY_REGULATORY_ENV]
- Identified risks / findings: [PASTE_RISK_INPUTS]
- Risk appetite statement: [APPETITE_OR_TOLERANCE]
- Existing controls: [SUMMARY_OF_CONTROLS]

TASK:
1. Normalize each input into a clear risk statement using the form: 'Risk that [threat] exploits [vulnerability] affecting [asset], leading to [impact].'
2. Assess inherent risk (Likelihood x Impact, 1-5 each) and explain the rating.
3. Map current controls and estimate residual risk after controls.
4. Recommend a treatment: Mitigate, Transfer, Avoid, or Accept — with rationale and a target residual level.
5. Assign an owner role and a review cadence.

OUTPUT FORMAT:
Risk register table | ID | Risk statement | Inherent (L/I/score) | Key controls | Residual (L/I/score) | Treatment | Owner | Review date
Plus: a heat-map summary (count of risks per residual band) and the 3 risks exceeding stated appetite.

CONSTRAINTS: Tie likelihood/impact to evidence, not vibes. Express impact in business terms (financial, operational, regulatory, reputational). Do not mark a risk 'Accept' if it exceeds the stated appetite without flagging it for escalation.

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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Structured Output

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

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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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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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