@orion-blakesecurity-risk-experiment-launch-planTextÖffentlichAktualisiert am 14.06.2026

Security Risk prompt that turns an idea into a measurable pilot or launch experiment and returns hypothesis, audience, variants, instrumentation, and decision rule.

AI Safety AI Workflow Privacy Risk Security

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Prompt

Vorschau

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# Role
You are a pragmatic security architect who finds exploitable risk without blocking useful shipping. 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
Turn an idea into a measurable pilot or launch experiment for AI system threat modeling, prompt-injection review, data exposure risk, and incident readiness.

# Inputs
- Project context: an internal agent that can read tickets, GitHub issues, and customer documents
- Target audience: security engineers, platform owners, privacy teams
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: threat model template, SIEM, secret scanner, policy engine
- Constraints: prioritize exploitability; map each risk to a control; respect privacy boundaries
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- data flow diagrams
- tool permissions
- audit logs
- security incidents

# Known risks to control
- prompt injection
- credential leakage
- cross-tenant access
- unreviewed tool writes

# Method
1. Tie each variant to a mechanism
2. Choose one primary metric
3. Define guardrail metrics
4. Specify how to interpret inconclusive results

# Required output sections
1. Hypothesis
2. Target segment
3. Variants
4. Instrumentation
5. Risks
6. Decision rule

# 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: Security Risk Experiment Launch Plan

Inputs used

Project context: an internal agent that can read tickets, GitHub issues, and customer documents
Target audience: security engineers, platform owners, privacy teams
Success metric: activation, quality, and risk reduction
Available tools and data: threat model template, SIEM, secret scanner, policy engine
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

hypothesis, audience, variants, instrumentation, and decision rule

Hypothesis

Hypothesis: improving how the workflow handles data flow diagrams will reduce prompt injection. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Target segment

Use tool permissions as evidence, apply the constraint "map each risk to a control", and explicitly note how the plan reduces credential leakage. The output should be ready for a practitioner to act on without a follow-up explanation.

Variants

Hypothesis: improving how the workflow handles audit logs will reduce cross-tenant access. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Instrumentation

Hypothesis: improving how the workflow handles security incidents will reduce unreviewed tool writes. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Risks

Treat prompt injection as a launch blocker until there is a control that can be verified. The minimum control is: map each risk to a control, plus reviewer sign-off for ambiguous outputs.

Decision rule

Hypothesis: improving how the workflow handles tool permissions will reduce credential leakage. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Recommended Decision

Proceed with a narrow pilot focused on data flow diagrams and tool permissions. Treat prompt injection as the primary launch blocker. The first milestone should prove that the workflow produces a usable risk register, mitigations, and verification checklist with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI system threat modeling, prompt-injection review, data exposure risk, and incident readiness.
It includes the required sections: Hypothesis, Target segment, Variants, Instrumentation, Risks, Decision rule.
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: prompt injection.

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

Security Risk: Experiment Launch Plan

Use this prompt when you need hypothesis, audience, variants, instrumentation, and decision rule for AI system threat modeling, prompt-injection review, data exposure risk, and incident readiness.

Best for

security engineers, platform owners, privacy teams
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.