From Heatmaps to Histograms - A Practical Guide to Cyber Risk Quantification

Cyber risk quantification (CRQ) is the practice of measuring cybersecurity risk using numbers —not colors or guesswork. Instead of labeling risks “high,” “medium,” or “low,” CRQ uses probabilities, ranges, and impact estimates to help organizations make better, data-informed decisions about risk.

In a world where ransomware gangs operate like small businesses, every core function of an organization is digital, and Boards and regulators are demanding meaningful, defensible risk metrics, CRQ has never been more relevant than now. And thanks to AI, it’s about to scale fast.

At the same time, CRQ is often misunderstood as expensive, technical, or just “voodoo math.” People assume you need a stats degree, six-figure software, or a room full of analysts. This book is here to prove otherwise.

From Heatmaps to Histograms is a hands-on, plain-English guide written by a seasoned practitioner who’s built CRQ programs at top global companies. It’s packed with step-by-step instructions, practical tips, templates, shortcuts, AI prompts, and plenty of myth-busting to take you from CRQ skeptic to CRQ champion—even if you’ve never cracked open a statistics book.

All techniques in this book can be performed in Excel or Google Sheets—no coding required. But for readers who want to go further, you’ll find dozens of GenAI prompts that help you generate risk scenarios, clean messy data, or even “vibe-code” your way through a Monte Carlo simulation in Python or R. You'll also get guidance on when to not use AI, how to spot hallucinations, and how to integrate it responsibly into your risk practice.

CRQ is no longer optional. This is your roadmap for making it work—cheaply, ethically, and effectively.

What You Will Learn:

• A beginner-friendly introduction to the statistical foundations of CRQ, including Monte Carlo simulations, credible intervals, Bayesian reasoning, and simple methods for summarizing uncertainty—without requiring a math or coding background.


• How to gather, vet, and work with data—even when it’s scarce, messy, or missing.


• How to perform full end-to-end quantitative risk assessments using only Excel or Google Sheets.


• How to harness the power of generative AI to supercharge risk analysis workflows.


• How to apply CRQ and GenAI responsibly and ethically, with clear guidance on common pitfalls, misuse scenarios, and how to ensure transparency, fairness, and trustworthiness in your analysis and reporting.

Table des matières

Part 1: Foundations.- Chapter 1: Welcome to the Rebellion.- Chapter 2: Probability’s Plot Twist: After 300 Years, We Colored It Red.- Chapter 3: GenAI Needs Adult Supervision.- Part 2: Getting Your Risk Muscles Working.- Chapter 4: You’ve Been Taught Risk Backwards.- Chapter 5: Your First Quantitative Risk Assessment.- Chapter 6: Interpreting and Communicating Quantitative Risk Results.- Chapter 7: Scenario building.- Part 3: Solving the Data Problem.- Chapter 8: Data Foundations.- Chapter 9: External Data.- Chapter 10: Internal data.- Chapter 11: Your Secret Weapon: Subject Matter Experts.- SMEs Are Your Quiet Competitive Advantage.- Part 4: Risk Assessment in Action.- Chapter 12: Extending this to CRQ.- Chapter13: Extending this to FAIR (12 pages).- Chapter 14: CRQ Risk Assessments.- Part 5: Making it Stick.- Chapter 15: CRQ in the Org.- Chapter 16: Making Better Decisions with CRQ.- Chapter 17: Objections, Pushback, and What to Say.- Chapter 18: The Ethics of Risk Management.- Chapter 19: The Future of CRQ (And Yours Too.

A propos de l'auteur

Tony Martin-Vegue is a cybersecurity and technology risk expert with over 25 years of experience helping Fortune 500 companies build and scale quantitative risk programs. He writes and speaks prolifically on the topic of risk and decision science, and is known for his new ways of thinking about old problems.

A hands-on practitioner as much as a leader, Tony has performed an estimated 1,000 quantitative risk assessments across domains including cyber, fraud, operations, and enterprise risk. He's a frequent speaker at FAIRcon, SIRAcon, RSA, various Security BSides, and ISACA events. He also chairs the San Francisco Chapter of the FAIR Institute, a global organization dedicated to advancing risk quantification practices, and was honored with the FAIR Ambassador Award in 2020. He has been published in numerous publications like the ISACA journal, Risk.net, and regularly blogs at tonym-v.com on the topics of risk, quantification, and security economics.

Tony lives with his family on an island in the San Francisco Bay (not Alcatraz)-though he has swum from Alcatraz to San Francisco ten times.

Résumé

Cyber risk quantification (CRQ) is the practice of measuring cybersecurity risk using numbers —not colors or guesswork. Instead of labeling risks “high,” “medium,” or “low,” CRQ uses probabilities, ranges, and impact estimates to help organizations make better, data-informed decisions about risk.
In a world where ransomware gangs operate like small businesses, every core function of an organization is digital, and Boards and regulators are demanding meaningful, defensible risk metrics, CRQ has never been more relevant than now. And thanks to AI, it’s about to scale fast.
At the same time, CRQ is often misunderstood as expensive, technical, or just “voodoo math.” People assume you need a stats degree, six-figure software, or a room full of analysts. This book is here to prove otherwise.
 
From Heatmaps to Histograms is a hands-on, plain-English guide written by a seasoned practitioner who’s built CRQ programs at top global companies. It’s packed with step-by-step instructions, practical tips, templates, shortcuts, AI prompts, and plenty of myth-busting to take you from CRQ skeptic to CRQ champion—even if you’ve never cracked open a statistics book.
 
All techniques in this book can be performed in Excel or Google Sheets—no coding required. But for readers who want to go further, you’ll find dozens of GenAI prompts that help you generate risk scenarios, clean messy data, or even “vibe-code” your way through a Monte Carlo simulation in Python or R. You'll also get guidance on when to not use AI, how to spot hallucinations, and how to integrate it responsibly into your risk practice.
 
CRQ is no longer optional. This is your roadmap for making it work—cheaply, ethically, and effectively.
 
What You Will Learn:

• A beginner-friendly introduction to the statistical foundations of CRQ, including Monte Carlo simulations, credible intervals, Bayesian reasoning, and simple methods for summarizing uncertainty—without requiring a math or coding background.
• How to gather, vet, and work with data—even when it’s scarce, messy, or missing.
• How to perform full end-to-end quantitative risk assessments using only Excel or Google Sheets.
• How to harness the power of generative AI to supercharge risk analysis workflows.
• How to apply CRQ and GenAI responsibly and ethically, with clear guidance on common pitfalls, misuse scenarios, and how to ensure transparency, fairness, and trustworthiness in your analysis and reporting.

 
Who This Book is for:
Beginner/Intermediate in the cyber/technology risk management field
 

Détails du produit

• Auteurs: Tony Martin-Vegue
• Edition: Springer EN
• Langues: Anglais
• Format d'édition: Livre de poche
• Sortie: 29.06.2026
• EAN: 9798868822995
• ISBN: 979-8-8688-2299-5
• Illustrations: Approx. 250 p.
• Catégories: Sciences naturelles, médecine, informatique, technique > Informatique, ordinateurs > Informatique; Netzwerksicherheit, fair, Security, Cybersecurity, GRC, Risk, Data and Information Security, Risk assessment, Metrics, Quantitative Risk Analysis, Scenario modeling, Cyber risk quantification, Monte Carlo simulation, Technology risk management, QRA, Technology risk, Governance, risk and compliance, CRQ, Factor Analysis of Information Risk