東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Identity analytics = analytics for identity and access management /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identity analytics/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Computers - Access control. -
Online resource:	https://doi.org/10.1007/979-8-8688-1745-8
ISBN:	9798868817458

Identity analytics = analytics 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

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

584008
Computers
--Access control.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Identity analytics = analytics for identity and access management /
LDR:03123nmm a2200349 a 4500 001 2422961
003 DE-He213
005 20260102122604.0
006 m d
007 cr nn 008maaau
008 260505s2025 cau s 0 eng d
020 $a 9798868817458 $q (electronic bk.)
020 $a 9798868817441 $q (paper)
024 7 $a 10.1007/979-8-8688-1745-8 $2 doi
035 $a 979-8-8688-1745-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A25
072 7 $a UR $2 bicssc
072 7 $a UTN $2 bicssc
072 7 $a COM053000 $2 bisacsh
072 7 $a UR $2 thema
072 7 $a UTN $2 thema
082 0 4 $a 005.8 $2 23
090 $a QA76.9.A25 $b B575 2025
100 1 $a Bhoyar, Nilesh. $3 3805168
245 1 0 $a Identity analytics $h [electronic resource] : $b analytics for identity and access management / $c by Nilesh Bhoyar.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xviii, 235 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Computers $x Access control. $3 584008
650 0 $a Computer security. $3 540555
650 1 4 $a Data and Information Security. $3 3538510
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-1745-8
950 $a Professional and Applied Computing (SpringerNature-12059)