@eli-carterpublic-policy-risk-governance-reviewTextÖffentlichAktualisiert am 14.06.2026

Public Policy prompt that reviews a workflow for operational, privacy, and safety risk and returns risk register, severity ranking, controls, and verification checklist.

AI Governance AI Workflow Civic Tech Policy Public Sector

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Prompt

Vorschau

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# Role
You are a civic technology policy analyst who writes clear, accountable AI program materials. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Review a workflow for operational, privacy, and safety risk for AI-assisted policy analysis, public service workflows, community engagement, and accountability plans.

# Inputs
- Project context: a city service chatbot pilot for multilingual resident support
- Target audience: policy teams, public sector product leads, civic technologists
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: policy library, service dashboard, public comment tracker, translation QA
- Constraints: public accountability; accessibility; plain-language communication; human appeal path
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- policy text
- constituent feedback
- service metrics
- procurement constraints

# Known risks to control
- unequal access
- opaque decisions
- procurement lock-in
- policy overreach

# Method
1. Trace data and permissions
2. Rank risk by likelihood and impact
3. Map every high risk to a concrete control
4. Identify who must approve residual risk

# Required output sections
1. System boundary
2. Data sensitivity
3. Risk register
4. Controls
5. Residual risk
6. Verification checklist

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artefakte

1 Artefakte

Example Output: Public Policy Risk Governance Review

Inputs used

Project context: a city service chatbot pilot for multilingual resident support
Target audience: policy teams, public sector product leads, civic technologists
Success metric: activation, quality, and risk reduction
Available tools and data: policy library, service dashboard, public comment tracker, translation QA
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

risk register, severity ranking, controls, and verification checklist

System boundary

Use policy text as evidence, apply the constraint "public accountability", and explicitly note how the plan reduces unequal access. The output should be ready for a practitioner to act on without a follow-up explanation.

Data sensitivity

Treat opaque decisions as a launch blocker until there is a control that can be verified. The minimum control is: accessibility, plus reviewer sign-off for ambiguous outputs.

Risk register

Treat procurement lock-in as a launch blocker until there is a control that can be verified. The minimum control is: plain-language communication, plus reviewer sign-off for ambiguous outputs.

Controls

Treat policy overreach as a launch blocker until there is a control that can be verified. The minimum control is: human appeal path, plus reviewer sign-off for ambiguous outputs.

Residual risk

Treat unequal access as a launch blocker until there is a control that can be verified. The minimum control is: public accountability, plus reviewer sign-off for ambiguous outputs.

Verification checklist

Use constituent feedback as evidence, apply the constraint "accessibility", and explicitly note how the plan reduces opaque decisions. The output should be ready for a practitioner to act on without a follow-up explanation.

Recommended Decision

Proceed with a narrow pilot focused on policy text and constituent feedback. Treat unequal access as the primary launch blocker. The first milestone should prove that the workflow produces a usable policy brief, pilot plan, and accountability checklist with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted policy analysis, public service workflows, community engagement, and accountability plans.
It includes the required sections: System boundary, Data sensitivity, Risk register, Controls, Residual risk, Verification checklist.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: unequal access.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

Public Policy: Risk Governance Review

Use this prompt when you need risk register, severity ranking, controls, and verification checklist for AI-assisted policy analysis, public service workflows, community engagement, and accountability plans.

Best for

policy teams, public sector product leads, civic technologists
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.