LEVERAGING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TO STRENGTHEN CYBERSECURITY FRAMEWORKS

As cyber threats evolve in scale and sophistication, traditional security systems are no longer sufficient to detect and prevent attacks in real time. This thesis explores how Artificial Intelligence (AI) and Machine Learning (ML) are redefining cybersecurity paradigms. AI enables intelligent threat detection, behavioral analytics, and autonomous response mechanisms that adapt to unknown attack vectors. ML techniques—ranging from supervised classifiers to deep neural networks—are instrumental in malware detection, phishing prevention, and user anomaly identification. The study also highlights the risks of bias, adversarial manipulation, and over-dependence on automated systems. A responsible and ethically guided implementation of AI/ML can significantly enhance cyber resilience and pave the way toward predictive and adaptive defense ecosystems.