A collection of awesome projects, blog posts, books, and talks on quantifying risk

Interactive CRQ Monte Carlo simulation tool for quantifying cybersecurity risk using FAIR methodology. Built for EU SMBs, vCISOs, and security practitioners.

FAIR cyber risk quantification toolkits, agent-based control simulation (FAIR-CAM), threat event frequency estimator (PyPI), LLM classification validator (PyPI), Monte Carlo risk engine with IRIS benchmarks.

Reusable decision-science utilities for security: Monte Carlo, Bayes, Survival, VoI, light causal helpers.

Evidence-governed quantitative cyber risk — a trustworthy CLI and scenario engine where every number traces to a reviewed public source.

Bayesian risk modelling and quantification notebooks for cybersecurity

Cybersecurity risk intelligence dashboard analyzing CVE vulnerabilities, CVSS risk scores, and financial exposure using Power BI.

Local-first quantitative cyber risk platform built on the FAIR methodology: Monte Carlo simulation, portfolio aggregation, and executive reporting. No cloud, no telemetry. (Beta)

Simple risk quantification framework with scoring model and executive summary examples.

Bayesian-inspired Impact Forecast Algorithm (IFA) for quantifying material impact risk

Threat modeling case study applying PASTA (7-stage) and FAIR (Monte Carlo) to quantify ransomware risk in a HIPAA-regulated SaaS environment. Includes control investment ROI analysis and presentation talking points.

Open-source data breach cost predictor & cyber-risk quantification engine — IBM benchmarks + DPDP/GDPR penalties + Monte Carlo + security-investment ROI

FAIR Monte Carlo cyber risk quantification: translates technical vulnerabilities into probable financial loss distributions, then has Claude draft the board narrative. Next.js + Recharts + Trigger.dev + Supabase.

Vulnerability Financial Impact Engine — FAIR-lite Monte Carlo risk quantification that translates security findings into dollar-denominated expected loss

What does a bad year cost — and can you prove the number? Which shared dependency drags everything down at once? Did an AI agent just touch a tool it never should have? How fast do you really detect? Three working tools answer — from seeded, sealed data you can re-check in your browser.

Agentic, controls-as-code GRC engine: one SCF-mapped control set → every framework. OSCAL-validated, FAIR-quantified, policy-as-code, human-gated AI. CI proves it.

Vulnerability Financial Impact Engine — FAIR-lite Monte Carlo risk quantification that translates security findings into dollar-denominated expected loss

Offline, self-contained HTML tools for calibrated probability estimation training: practice trainer with Brier scoring, a nine-module course, and a verified question bank.

Cyberwrite is an AI-powered cyber insurance intelligence platform, founded in 2017, that turns raw data into explainable, defensible financial decisions for the cyber insurance market.

Industrial cybersecurity risk quantification platform for OT attacks and financial exposure built with LangGraph.