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.
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Ed Bellis is the cofounder and CTO of Kenna Security, now a part of Cisco. He is a security industry veteran and expert. Known in security circles as “the father of risk-based vulnerability management,” Bellis founded Kenna Security to deliver a data-driven, risk-based approach to vulnerability management and help IT teams prioritize their actions to meaningfully reduce cybersecurity risk. Ed is the former CISO of Orbitz and the former Vice President, Corporate Information Security at Bank of America before that. Ed is a frequent speaker at industry conferences including RSA and Black Hat, and a cybersecurity contributor to Forbes, Dark Reading, SC Magazine, and other publications. Michael Roytman is the Chief Data Scientist of Kenna Security, now a part of Cisco. His work focuses on cybersecurity data science and Bayesian algorithms, and he served on the boards for the Society of Information Risk Analysts, Cryptomove, and Social Capital. He was the cofounder and executive chair of Dharma Platform (acquired, BAO Systems), for which he landed on the 2017 Forbes 30 under 30 list. He currently serves on Forbes Technology Council. He holds an M.S. in Operations Research from Georgia Tech, and has recently turned his home roasting operation into a Chicago south side cafe, Sputnik Coffee
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Hardback. Condizione: New. 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. Codice articolo LU-9781630819385
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