--- name: cyber-risk-quantifier description: "Converts a described cyber threat scenario into an estimated financial risk range using FAIR (Factor Analysis of Information Risk) and Annualized Loss Expectancy (ALE = ARO × SLE), with a defensible calculation walkthrough, three-point loss estimates, and a control ROI comparison. Use when translating technical cyber risk into dollar exposure to prioritize security spending, justify budget, or brief leadership and the board." argument-hint: "[threat-scenario] [asset-value]" allowed-tools: ["Read"] --- # Cyber Risk Quantifier ## Purpose Turn a described cyber threat scenario into a defensible financial exposure range — using FAIR decomposition and the ALE model — so a security leader can prioritize investments, justify budget, and communicate risk to non-technical executives in the language of dollars, not CVSS scores. ## When to Use Invoke this skill when the user asks to: - Estimate the financial impact or dollar exposure of a specific cyber threat - Quantify risk for ransomware, data breach, business email compromise, insider threat, or a similar scenario - Justify a security investment or compare the cost of a control to the risk it reduces - Build a FAIR analysis or calculate Annualized Loss Expectancy (ALE) - Prioritize a set of risks or controls by financial impact - Prepare a risk figure for a board deck, budget request, or cyber insurance discussion ## Inputs Required Before starting, collect: - **Threat scenario**: What could happen — the threat actor, method, and asset affected (e.g., "ransomware encrypts our production ERP") - **Asset value / impact drivers**: Revenue at risk, records held, systems affected, or replacement cost - **Threat frequency context**: How often this has happened to the org or peers, industry incident rates, or a qualitative sense (rare / occasional / frequent) - **Existing controls**: What's already in place that reduces likelihood or impact (backups, MFA, EDR, segmentation) - **Organization profile**: Industry, approximate revenue or size, and regulatory regime (HIPAA, PCI-DSS, GDPR, etc.) - **Decision context**: What the number is for — budget request, control comparison, insurance, or board briefing - **Control under consideration** (if comparing): The proposed investment and its annual cost If frequency or loss data is missing, use published industry benchmarks (e.g., IBM Cost of a Data Breach, Verizon DBIR ranges) and state every assumption explicitly. ## Instructions ### Step 1: Frame the Risk Scenario Restate the scenario in FAIR terms so the math is auditable: - **Asset at risk**: The specific asset (data, system, revenue stream) - **Threat community**: Who or what triggers the loss (external cybercriminal, malicious insider, nation-state, accidental) - **Threat effect**: Confidentiality, integrity, or availability - **Loss event**: The single event being quantified (one scenario per analysis — do not blend ransomware and data breach into one number) If the user described a broad concern, narrow it to one discrete loss event and note that other scenarios should be run separately. ### Step 2: Estimate Loss Event Frequency (LEF) Decompose how often the loss occurs per year: - **Threat Event Frequency (TEF)**: How often the threat actor attempts this — anchor to industry base rates and the org's exposure - **Vulnerability**: The probability an attempt succeeds given current controls (0–100%) - **LEF = TEF × Vulnerability** For ALE, express this as **Annualized Rate of Occurrence (ARO)** — the expected number of successful events per year (e.g., 0.25 = once every four years). State the reasoning: which controls lowered vulnerability, which benchmarks set frequency. ### Step 3: Estimate Loss Magnitude (Three-Point Estimate) Estimate the **Single Loss Expectancy (SLE)** — the dollar loss per event — as a Low / Most Likely / High range, not a single point. Break loss into FAIR's six loss forms and include only those that apply: - **Productivity loss**: Downtime × loaded cost per hour × affected headcount or revenue/hour - **Response costs**: Incident response, forensics, legal, overtime - **Replacement costs**: Rebuilding systems, restoring data, hardware - **Fines & judgments**: Regulatory penalties (scale to the applicable regime and record count) - **Competitive advantage loss**: Lost IP or market position (where relevant) - **Reputation damage**: Churn, lost deals, customer notification, credit monitoring Sum the applicable forms at each of the three points to produce Low / Most Likely / High SLE. ### Step 4: Calculate Annualized Loss Expectancy Compute the headline figure: - **ALE = ARO × SLE** - Produce three ALE figures — **Low, Most Likely, High** — by applying ARO to each SLE point - Present the range as the primary output; the "Most Likely" figure is the recommended planning number - Show the arithmetic explicitly so any reviewer can reproduce it ### Step 5: Control ROI Comparison (if a control is under consideration) Quantify the risk reduction of the proposed control: - **Residual ALE**: Recalculate ARO or SLE assuming the control is in place (state which factor it reduces and by how much) - **Risk reduction = Current ALE − Residual ALE** - **Control ROI = (Risk reduction − Annual control cost) ÷ Annual control cost**, expressed as a percentage - Flag whether the control is justified: risk reduction should meaningfully exceed its cost, and note when a control is over-engineered relative to the exposure ### Step 6: Prioritization & Recommendation Close with a clear position: - State the recommended planning figure and the confidence level (High / Medium / Low) based on data quality - If multiple risks or controls were provided, rank them by ALE or by risk-reduction-per-dollar - Give one direct recommendation on where to spend next — do not hedge ## Output Format - **Structure**: Scenario framing → LEF/ARO → Loss magnitude table → ALE calculation → Control ROI (if applicable) → Recommendation - **Length**: 700–1,000 words - **Format**: Prose reasoning with a three-point loss magnitude table and a clearly boxed ALE range; calculations shown inline - **Sections**: Risk Scenario → Frequency Estimate (ARO) → Loss Magnitude (Low/Likely/High) → Annualized Loss Expectancy → Control ROI Analysis → Recommendation & Confidence - **Tone**: Risk quantification analyst briefing an executive — precise, assumption-transparent, financially literate; no fear-mongering ## Quality Checklist Before delivering output, verify: - [ ] Exactly one discrete loss event is quantified — scenarios are not blended - [ ] ARO and SLE are both stated as numbers, and ALE = ARO × SLE is shown with visible arithmetic - [ ] Loss magnitude is a three-point estimate (Low / Most Likely / High), not a single figure - [ ] Every assumption and benchmark source is stated explicitly — no unsourced numbers - [ ] If a control was provided, Control ROI is calculated as a percentage with residual ALE shown - [ ] A single recommended planning figure and confidence level are named, and the recommendation does not hedge ## Notes - FAIR is an estimation model, not a precision instrument — the value is a defensible, reproducible range that ranks risks against each other, not a guarantee of the loss. - Ranges beat point estimates with executives: showing Low/Likely/High signals rigor and prevents the number from being challenged as false precision. - Anchor frequency to real base rates where possible (DBIR, sector ISAC data, the org's own incident history) — the frequency estimate is where most FAIR analyses go wrong. - Run separate analyses for materially different scenarios (ransomware vs. insider data theft) rather than forcing one blended number; then rank the resulting ALEs to drive the investment conversation. --- ## How to use this skill This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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