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Identity analyticsanalytics for iden...

Bhoyar, Nilesh.

Identity analyticsanalytics for identity and access management /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identity analyticsby Nilesh Bhoyar.
Reminder of title:	analytics for identity and access management /
Author:	Bhoyar, Nilesh.
Published:	Berkeley, CA :Apress :2025.
Description:	xviii, 235 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	ComputersAccess control.
Online resource:	https://doi.org/10.1007/979-8-8688-1745-8
ISBN:	9798868817458$q(electronic bk.)

Identity analyticsanalytics for identity and access management /
Bhoyar, Nilesh.

Identity analyticsanalytics for identity and access management /[electronic resource] :by Nilesh Bhoyar. - Berkeley, CA :Apress :2025. - xviii, 235 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Identity and Access Management -- Chapter 2: Fundamentals of Identity Analytics. Chapter 3: Data Preparation -- Chapter 4: Risk Aware Metrics -- Chapter 5: Risk Based Access Management -- Chapter 6: Identity Threat Detection and Response Chapter 7: Analytics for Cloud Access Management Chapter 8:Analytics For Regulatory Reporting Chapter 9: Machine Learning Techniques in Identity Analytics Chapter 10: GenAI for IAM Chapter 11:Analytics For Zero Trust.

Misconfigured identities are the leading cause of cyber incidents. Organizations are investing significant resources to address these vulnerabilities, but these investments often prioritize meeting compliance requirements set by regulators. However, the controls and monitoring systems implemented are not designed for real-time threat detection and risk mitigation. Consequently, organizations often resort to purchasing multiple expensive vendor products, which then require extensive reconfiguration due to the varied identity landscapes of each organization. In this book, we explore how individuals can construct their own identity analytics solution from scratch. We provide starter code to facilitate understanding, catering to beginners. This knowledge can also serve as guidance for those considering vendor products. The book commences with general principles and progresses to cover specific use cases, accompanied by sample code for each. Much of the available material originates from vendors, which tends to focus more on marketing rather than addressing systemic issues. Alternatively, materials may solely concentrate on Identity and Access Management (IAM) processes and governance without delving deeply into the topics discussed here. What You Will Learn: The relationship between IAM and Identity Analytics. Statistical methods, data curation processes, and risk scoring that contribute to effective IAM strategies. How to utilize advanced ML/AI techniques for implementing impactful IAM programs, including various use cases that demonstrate their effectiveness.

ISBN: 9798868817458$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1745-8doiSubjects--Topical Terms:

219159
Computers
--Access control.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Identity analyticsanalytics for identity and access management /
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505 0 $a Chapter 1: Introduction to Identity and Access Management -- Chapter 2: Fundamentals of Identity Analytics. Chapter 3: Data Preparation -- Chapter 4: Risk Aware Metrics -- Chapter 5: Risk Based Access Management -- Chapter 6: Identity Threat Detection and Response Chapter 7: Analytics for Cloud Access Management Chapter 8:Analytics For Regulatory Reporting Chapter 9: Machine Learning Techniques in Identity Analytics Chapter 10: GenAI for IAM Chapter 11:Analytics For Zero Trust.
520 $a Misconfigured identities are the leading cause of cyber incidents. Organizations are investing significant resources to address these vulnerabilities, but these investments often prioritize meeting compliance requirements set by regulators. However, the controls and monitoring systems implemented are not designed for real-time threat detection and risk mitigation. Consequently, organizations often resort to purchasing multiple expensive vendor products, which then require extensive reconfiguration due to the varied identity landscapes of each organization. In this book, we explore how individuals can construct their own identity analytics solution from scratch. We provide starter code to facilitate understanding, catering to beginners. This knowledge can also serve as guidance for those considering vendor products. The book commences with general principles and progresses to cover specific use cases, accompanied by sample code for each. Much of the available material originates from vendors, which tends to focus more on marketing rather than addressing systemic issues. Alternatively, materials may solely concentrate on Identity and Access Management (IAM) processes and governance without delving deeply into the topics discussed here. What You Will Learn: The relationship between IAM and Identity Analytics. Statistical methods, data curation processes, and risk scoring that contribute to effective IAM strategies. How to utilize advanced ML/AI techniques for implementing impactful IAM programs, including various use cases that demonstrate their effectiveness.
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