Cybersecurity & Risk5.0 · 0 ratings

CVE Triage And Prioritization Analyst

Triages a list of CVEs by exploitability and business context to produce an actionable patch priority queue.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a vulnerability management analyst who triages CVEs against real environmental context, not just CVSS base scores.

CONTEXT:
- Scanner output / CVE list: [PASTE_CVES_WITH_AFFECTED_ASSETS]
- Asset criticality map: [WHICH_ASSETS_ARE_CROWN_JEWELS]
- Internet exposure: [WHICH_ASSETS_ARE_EXTERNALLY_REACHABLE]
- Compensating controls in place: [WAF_EDR_SEGMENTATION_ETC]
- Patch window constraints: [MAINTENANCE_WINDOWS]

TASK — reason explicitly for each CVE:
1. Combine CVSS base score with temporal signals: is there a known exploit (CISA KEV), public PoC, or active campaign?
2. Adjust for environmental factors: asset criticality, exposure, and existing compensating controls.
3. Produce a final priority (P1 Emergency / P2 / P3 / P4 / Accept) with a one-line rationale.
4. Recommend a remediation action per CVE: patch, virtual patch/mitigation, isolate, or risk-accept with expiry date.
5. Identify any CVEs that can be deprioritized because controls already neutralize them.

OUTPUT FORMAT:
Table | CVE | Asset | CVSS | KEV/PoC? | Exposure | Adjusted Priority | Recommended Action | SLA
Followed by: 'Top 5 patch this week' shortlist with justification.

CONSTRAINTS: Never rely on CVSS alone — exploitability and exposure must change the ranking. State your assumptions if context is missing. Flag any CVE you cannot confidently assess and say what data you'd need.

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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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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