@orion-blakesecurity-risk-executive-decision-memoएकल टेक्स्टसार्वजनिक14 जून 2026 को अपडेट किया गया

Security Risk prompt that creates an executive memo that makes tradeoffs explicit and returns one-page recommendation, options table, risks, and next actions.

AI Safety AI Workflow Privacy Risk Security

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Prompt

पूर्वावलोकन

Claude में खोलें

# 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
Create an executive memo that makes tradeoffs explicit 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. Lead with the decision
2. State assumptions and confidence
3. Compare options on the same criteria
4. End with named owners and dates

# Required output sections
1. Decision needed
2. Recommendation
3. Options
4. Evidence
5. Risks
6. Next actions

# 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.

आउटपुट

1 आउटपुट

Example Output: Security Risk Executive Decision Memo

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

one-page recommendation, options table, risks, and next actions

Decision needed

The immediate decision is whether an internal agent that can read tickets, GitHub issues, and customer documents is mature enough for a controlled pilot. The strongest evidence should come from data flow diagrams and tool permissions; if either source is missing, mark the recommendation as provisional rather than filling the gap with assumptions.

Recommendation

Recommendation: run a narrow pilot before broad rollout. Prefer a governance-forward pilot if evidence suggests credential leakage; prefer a speed-forward pilot only when tool permissions and audit logs are already reliable.

Options

Option A optimizes speed by shipping a limited workflow around audit logs. Option B optimizes control by adding reviewer sign-off and rollback steps. Option C waits until evidence from security incidents is stronger. Use the same success metric for all three options.

Evidence

Evidence to trust: security incidents, data flow diagrams, and reviewer notes from policy engine. Evidence to treat cautiously: anecdotes that are not tied to a time window, cohort, or source owner.

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.

Next actions

Next actions: validate tool permissions, assign a reviewer for credential leakage, and schedule a decision checkpoint after the first pilot cohort. Do not expand scope until the review path works in practice.

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: Decision needed, Recommendation, Options, Evidence, Risks, Next actions.
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: Executive Decision Memo

Use this prompt when you need one-page recommendation, options table, risks, and next actions 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.