Evaluating the Effectiveness of AI-Driven Frameworks in Predicting and Preventing Cyber Attacks

With the escalating use of technology and major computer systems, cyberattack threats loom larger than ever.These attacks, categorized as any malicious attempt to gain unauthorized access to a computer system or network that intends to cause damage, necessitate the development of new security approaches.AI has emerged as a practical solution, enabling early identification and mitigation of various attack vectors.This research delves into AI frameworks for predicting and preventing cyberattacks in diverse organizational contexts.The data collected from qualitative sources, including secondary data and literature, as well as primary sources like case studies, was analyzed using content analysis.The evaluation criteria focused on the framework's applicability in different environmental settings.The key AI frameworks considered are spam detection, fraud detection, breach risk prediction, and behavioral analysis.These frameworks have proven instrumental in enhancing cybersecurity and ensuring operational effectiveness.The research underscores the importance of organizations fully automating AI processes to bolster security.