Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Irfan, Mohammad; Makkar, Tanisha; Yadav, Ajay Kumar; Ghosal, Indrajit; Durrah, Omar Title A name given to the resource The Evolving Role of AI in Cybersecurity Insurance: Enhancing Risk Assessment and Threat Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Cybersecurity Insurance Frameworks and Innovations in the AI Era;pp.115-138 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-1977-3.ch005" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-1977-3.ch005</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015812128?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015812128?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Irfan M., Chandigarh University, Lucknow, India; Makkar T., Christ University, Bengaluru, India; Yadav A.K., Chandigarh University, Unnao, India; Ghosal I., Brainware University, Kolkata, India; Durrah O., Dhofar University, Oman Description An account of the resource Cybersecurity insurance is a very important defense against data breaches, cyberattacks, and AI-powered system failures. As the use of AI becomes more widespread in business operations, insurers are now assessing emerging risks such as algorithmic errors, adversarial attacks, and data manipulation. As the cybersecurity insurance industry grows, organizations need to implement AI-specific safeguards to mitigate vulnerabilities. This study uses a mixed methods approach, by incorporating qualitative case studies for the integration of AI in claims processing with quantitative analysis on AI-driven risk assessment models. The method will be based on surveys, expert interviews, and real insurance claim analysis. Important goals involve the improvement of assessments of risk, underwriting, and claims processing through AI. Case studies of AI-enabled threat detection tools, including Darktrace, Microsoft Sentinel, and Google's phishing detection, demonstrate the AI impact in cybersecurity. 2026 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833731979-7; 979-833731977-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online