Название: Modern Vulnerability Management: Predictive CybersecurityАвтор: Michael Roytman, Ed Bellis
Издательство: Artech House
Год: 2023
Страниц: 237
Язык: английский
Формат: pdf (true)
Размер: 13.9 MB
This book comprehensively covers the principles of Risk-based vulnerability management (RBVM) – one of the most challenging tasks in cybersecurity -- from the foundational mathematical models to building your own decision engine to identify, mitigate, and eventually forecast the vulnerabilities that pose the greatest threat to your organization. You will learn: how to structure data pipelines in security and derive and measure value from them; where to procure open-source data to better your organization’s pipeline and how to structure it; how to build a predictive model using vulnerability data; how to measure the return on investment a model in security can yield; which organizational structures and policies work best, and how to use Data Science to detect when they are not working in security; and ways to manage organizational change around Data Science implementation.
You’ll also be shown real-world examples of how to mature an RBVM program and will understand how to prioritize remediation efforts based on which vulnerabilities pose the greatest risk to your organization. The book presents a fresh approach, rooted in risk management, and taking advantage of rich data and Machine Learning, helping you focus more on what matters and ultimately make your organization more secure with a system commensurate to the scale of the threat. This is a timely and much-needed book for security managers and practitioners who need to evaluate their organizations and plan future projects and change. Students of cybersecurity will also find this a valuable introduction on how to use their skills in the enterprise workplace to drive change.
Using the many sources of data we have and the mathematical modeling we use to compensate for mathematical scale, randomness, and uncertainty, Machine Learning (ML) offers a quick and efficient way to better prioritize decisions and remediate the vulnerabilities that pose the greatest risk. ML is a classification of artificial intelligence in which algorithms automatically improve over time by observing patterns in data and applying those patterns to subsequent actions. ML allows us to better predict the risk of vulnerabilities and exploits, and continually update recommendations in real time as situations evolve and new threats arise. ML algorithms can adapt based on patterns and changes in the data they analyze, allowing you to create a self-updating system that evolves with the threat landscape as well as your organization.
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