The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
The Role of AI in Zero Trust Architectures
|
|
Author(s): Tushar (United College of Engineering and Research, Prayagraj, India), Nandita Pradhan (United College of Engineering and Research, Prayagraj, India), Venktesh Mishra (United College of Engineering and Research, Prayagraj, India), Ajay Sharma (United College of Engineering and Research, Prayagraj, India), Pooja Jaiswal (University of Allahabad, Prayagraj, India), Ajit Kumar Yadav (United College of Engineering and Research, Prayagraj, India), Shyam Sundar (B.C.S. Prayagraj, India), Satya Prakash Singh (United College of Engineering and Research, Prayagraj, India), Shatrughan Mishra (United College of Engineering and Research, Prayagraj, India), Manish Kumar (P.K. University, Shivpuri, India), Sweta Singh (United University, Prayagraj, India), Shivani Mishra (United College of Engineering and Research, Prayagraj, India), Chitranjan Dwivedi (United College of Engineering and Research, Prayagraj, India)and Bhupesh Chandra Kushwaha (United College of Engineering and Research, Prayagraj, India)
Copyright: 2027
Pages: 32
Source title:
Generative AI for Cyber Risk Management
Source Author(s)/Editor(s): Yassine Maleh (Sultan Moulay Slimane University, Morocco), Lahby Mohamed (Hassan II University, Casablanca, Morocco)and Ahmed A. Abd El-Latif (Prince Sultan University, Saudi Arabia)
DOI: 10.4018/979-8-3693-8397-1.ch006
Purchase
|
Abstract
The rapid growth of cybersecurity risks has led organizations to build stricter frameworks, with Zero Trust Architecture (ZTA) emerging as a significant paradigm. Based on “never trust, always verify,” zero trust involves ongoing user and device identification verification, even inside the network perimeter. ZTA has a solid foundation, but cyberattacks are becoming more sophisticated, requiring more flexible solutions. AI improves ZTA by enabling real-time threat detection, behavioral analytics, and automated security event responses. A complete review of AI-powered tools and methodologies such natural language processing, anomaly detection, and predictive analytics is presented in this chapter to improve ZTA. It discusses AI implementation challenges, how AI interacts with ZTA components to improve security, and future research objectives.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|