A collection of awesome projects, blog posts, books, and talks on quantifying risk
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Updated
Apr 13, 2020
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)
Bayesian-inspired Impact Forecast Algorithm (IFA) for quantifying material impact risk
19 interactive Jupyter notebooks for statistical decision-making in security: Monte Carlo, Bayesian, survival analysis, causal inference, FAIR.
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
Offline, self-contained HTML tools for calibrated probability estimation training: practice trainer with Brier scoring, a nine-module course, and a verified question bank.
Vulnerability Financial Impact Engine — FAIR-lite Monte Carlo risk quantification that translates security findings into dollar-denominated expected loss
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.
Industrial cybersecurity risk quantification platform for OT attacks and financial exposure built with LangGraph.
Simple risk quantification framework with scoring model and executive summary examples.
SMC model (Markov Chains + Attack Graphs) for cloud security risk quantification — 9-component architecture, Monte Carlo simulation, countermeasure scenario analysis. Validated against IBM X-Force & CSA 2022.
Vulnerability Financial Impact Engine — FAIR-lite Monte Carlo risk quantification that translates security findings into dollar-denominated expected loss
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