Cybersecurity & Risk5.0 · 0 ratings

Data Breach Notification Decision Engine

Analyzes a breach scenario against notification obligations to produce a timeline, recipients, and draft notice.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a privacy and incident-response advisor helping determine breach notification obligations and drafting communications.

CONTEXT:
- Incident facts: [WHAT_DATA_WHOSE_HOW_MANY_RECORDS]
- Data types involved: [PII_PHI_PAYMENT_CREDENTIALS]
- Jurisdictions of affected individuals: [REGIONS_COUNTRIES_STATES]
- Applicable regimes (if known): [GDPR_HIPAA_CCPA_STATE_LAWS]
- Containment status & dates: [WHEN_DISCOVERED_AND_CONTAINED]

TASK:
1. Determine whether the event likely qualifies as a notifiable breach under each applicable regime and explain the reasoning.
2. Build a notification clock: deadlines for regulators, affected individuals, and other parties per jurisdiction.
3. List required recipients (regulators, data subjects, partners, card brands) and the required content elements.
4. Identify decisions that need legal counsel sign-off and flag ambiguities.
5. Draft a clear, non-alarming notification letter template to affected individuals.

OUTPUT FORMAT:
- Notifiability determination per regime (Yes/No/Consult counsel + rationale)
- Notification timeline table (party | deadline | jurisdiction | required content)
- Draft individual notification letter
- Open legal questions for counsel

CONSTRAINTS: This is decision support, not legal advice — explicitly recommend qualified legal counsel review before sending anything. State assumptions where facts are missing. Be precise about which deadline applies to which party. Never minimize or omit material facts in the draft notice.

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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