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@article{180850,
        author = {Anjali Kumari and Taneja Sanjay Devkishan},
        title = {Use of AI in Cybersecurity: Threat Detection and Prevention.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3007-3015},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180850},
        abstract = {Artificial Intelligence (AI) has been included into threat detection and prevention systems as a result of the growing sophistication and frequency of cyber threats beyond the capabilities of conventional cybersecurity procedures. The strategic use of AI technologies, such as machine learning, deep learning, and hybrid models, in detecting and reducing cyberthreats in diverse digital contexts is examined in this study. A thorough literature analysis, empirical performance assessments of particular AI models, and case studies from actual cybersecurity deployments in industries including enterprise networks, government, and finance are all part of the study's mixed-methods methodology. Key findings show that in terms of flexibility, accuracy, and real-time responsiveness, AI-driven systems perform noticeably better than traditional signature-based and rule-based detection strategies. Nonetheless, issues with data privacy, morality, and hostile manipulation continue to exist, highlighting the necessity of strong control and interpretability in AI systems. By providing a balanced comparison of AI and conventional systems, suggesting a framework for the responsible use of AI, and outlining potential research avenues, this study adds to the scholarly conversation. Policymakers, cybersecurity experts, and AI developers looking to improve digital defence tactics in a threat scenario that is becoming more complicated may find the consequences especially pertinent.},
        keywords = {},
        month = {June},
        }