Title: Artificial Intelligence in Cybersecurity: An Integrated Framework for Intelligent Threat Detection and Response
Authors: Dr Neha Paliwal
Volume: 10
Issue: 9
Pages: 142-155
Publication Date: 2026/09/28
Abstract:
The rapid expansion of cloud services, connected devices, digital platforms, remote-access environments, and artificial intelligence applications has significantly increased the complexity of cybersecurity management. Security teams must now process large volumes of heterogeneous information while responding to threats that can change rapidly and exploit previously unseen weaknesses. Conventional security mechanisms remain essential, but their dependence on predefined rules and known indicators can limit their effectiveness against adaptive and emerging attacks. This study investigates the application of Artificial Intelligence (AI) to cybersecurity with particular emphasis on machine learning, deep learning, anomaly detection, natural language processing, automated alert prioritization, and intelligent incident response. A structured literature-review methodology was adopted to synthesize research findings, cybersecurity frameworks, and recent institutional reports. The study further develops a conceptual AI-cybersecurity framework linking data acquisition, preprocessing, intelligent analytics, threat classification, response, human validation, and continuous model improvement. A comparative analytical model was developed to evaluate major AI techniques according to detection capability, adaptability, explainability, data requirements, automation potential, and deployment complexity. The analysis indicates that AI has substantial potential to improve continuous monitoring, anomaly identification, alert prioritization, malware analysis, phishing detection, and security operations. At the same time, AI introduces challenges involving data quality, model drift, false predictions, adversarial manipulation, privacy, explainability, cost, and workforce requirements. The study concludes that AI should be implemented as a human-supervised cybersecurity capability rather than as a completely autonomous replacement for security professionals. The proposed framework combines AI-based analytical scalability with human judgment, governance, continuous validation, and responsible risk management. The research contributes an integrated perspective for organizations seeking to adopt AI while maintaining security, accountability, transparency, and resilience.