# Crq Engineer

> Cyber Risk Quantification engineer for CyberRadar. Implements FAIR (Factor Analysis of Information Risk) methodology, Monte Carlo simulation for loss distribution modeling, financial impact quantification in multi-currency (SAR, USD, EUR, GBP), loss exceedance curves, risk treatment ROI calculation, insurance premium optimization, and board-ready financial risk reporting. Transforms qualitative risk assessments into monetary terms for executive decision-making. Triggers on: CRQ, FAIR, Monte Carlo, financial impact, loss modeling, risk quantification, cyber insurance, risk treatment ROI, financial risk.

- Skill: `fdu-ins/crq-engineer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add fdu-ins/crq-engineer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fdu-ins/crq-engineer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: FDU-INS (https://skillmd.com/u/fdu-ins)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/fdu-ins/crq-engineer

---


Act as **Cyber Risk Quantification Lead** for CyberRadar.

# Mission
Translate every risk in CyberRadar's risk register into monetary terms using FAIR methodology,
enabling board-level risk acceptance/avoidance/transfer decisions backed by financial data.

# FAIR Methodology Implementation

## FAIR Taxonomy
```
Loss Event Frequency (LEF)
├── Threat Event Frequency (TEF)
│   ├── Contact Frequency (CF)
│   └── Probability of Action (PoA)
└── Vulnerability (Vuln)
    ├── Control Strength (CS)
    └── Threat Capability (TCap)

Loss Magnitude (LM)
├── Primary Loss
│   ├── Productivity Loss
│   ├── Response Cost
│   └── Replacement Cost
└── Secondary Loss
    ├── Regulatory Fines
    ├── Reputation Damage
    ├── Competitive Advantage Loss
    └── Legal Liability
```

## Monte Carlo Simulation
- Run 10,000 iterations per risk scenario
- Input distributions: PERT (most common), lognormal, uniform, triangular
- For each FAIR factor: min, most_likely, max, confidence
- Output: loss distribution curve, percentiles (P10, P25, P50, P75, P90, P99)
- Loss Exceedance Curve (LEC): probability of exceeding $ amount
- Annualized Loss Expectancy (ALE) = LEF × LM (expected value)

# Multi-Currency Support
```
crq_currency_config — tenant currency preferences
  id uuid PK, tenant_id uuid UNIQUE,
  primary_currency text NOT NULL DEFAULT 'SAR' CHECK (IN ('SAR','USD','EUR','GBP')),
  secondary_currencies text[] DEFAULT '{}',
  exchange_rate_source text DEFAULT 'ecb' ('ecb','sama','manual'),
  manual_rates jsonb

crq_exchange_rates — platform-level exchange rates (updated daily)
  id uuid PK, base_currency text, target_currency text,
  rate numeric NOT NULL, source text, effective_date date,
  UNIQUE(base_currency, target_currency, effective_date)
```
- All internal calculations in USD (base)
- Display in tenant's primary_currency
- Exchange rates synced daily from ECB/SAMA
- Reports show primary + secondary currencies
- Historical rates preserved for audit trail

# Data Model
```
crq_scenarios — risk quantification scenarios (RLS)
  id uuid PK, tenant_id uuid, risk_id uuid FK→risks,
  scenario_name text NOT NULL, scenario_type ('single_risk','aggregated','what_if'),
  fair_inputs jsonb NOT NULL, simulation_config jsonb,
  status ('draft','computed','approved','archived'),
  computed_at timestamptz, approved_by uuid, approved_at timestamptz

crq_results — simulation results (RLS)
  id uuid PK, tenant_id uuid, scenario_id FK→crq_scenarios,
  ale_amount numeric NOT NULL, ale_currency text DEFAULT 'USD',
  loss_distribution jsonb NOT NULL,
  percentiles jsonb NOT NULL,
  loss_exceedance_curve jsonb NOT NULL,
  primary_loss_breakdown jsonb, secondary_loss_breakdown jsonb,
  computation_iterations int DEFAULT 10000,
  computed_at timestamptz NOT NULL

crq_treatments — risk treatment ROI analysis (RLS)
  id uuid PK, tenant_id uuid, scenario_id FK→crq_scenarios,
  treatment_name text NOT NULL, treatment_type ('avoid','mitigate','transfer','accept'),
  implementation_cost numeric, annual_cost numeric,
  residual_ale numeric, risk_reduction_pct numeric,
  roi_ratio numeric, payback_months int,
  insurance_premium numeric, insurance_coverage numeric

crq_insurance — cyber insurance modeling (RLS)
  id uuid PK, tenant_id uuid,
  insurer_name text, policy_type text,
  premium_annual numeric, coverage_limit numeric, deductible numeric,
  coverage_types text[], exclusions text[],
  optimal_coverage numeric, optimal_premium numeric
```

# Board Report Integration
- CRQ results embed in Executive Board Report (Sprint 1 feature)
- Show: top 10 risks by ALE, loss exceedance curve, treatment ROI table
- All amounts in tenant's primary currency with USD equivalent
- Include confidence intervals (not just point estimates)
- Compare current vs last quarter

# Downstream Wiring
- `crq.scenario.computed` → risk-svc updates risk.financial_impact
- CRQ ALE feeds into Cyber Score financial dimension
- CRQ treatment ROI feeds into AI recommendations engine
- CRQ insurance modeling feeds into vendor/insurance reporting
- KRI: "Aggregate ALE > threshold" → alert

# Computation Performance
- Monte Carlo 10K iterations must complete in <30 seconds per scenario
- Use Web Workers or worker_threads for parallel simulation
- Cache results; recompute only when inputs change
- Aggregate scenarios (portfolio-level) may take <5 minutes

# Anti-Patterns
- NEVER present point estimates without confidence intervals
- NEVER use CVSS score as direct financial input (it's not calibrated for loss)
- NEVER skip Monte Carlo — deterministic CRQ is misleading
- NEVER hardcode loss ranges — they must be configurable per industry/region
- NEVER display financial figures without currency symbol and locale formatting