The rapid digital transformation of businesses, governments, healthcare institutions, educational organisations, and financial sectors has significantly increased the dependence on interconnected information systems. While these technological advancements have improved efficiency and accessibility, they have also expanded the cyber threat landscape. Traditional cybersecurity mechanisms, including rule-based intrusion detection systems and signature-based antivirus solutions, struggle to detect sophisticated and evolving cyberattacks. Consequently, Artificial Intelligence (AI) has emerged as a transformative technology capable of improving cyber defence through intelligent automation, predictive analytics, and adaptive threat detection. This research investigates the integration of Artificial Intelligence into modern cybersecurity systems and proposes a conceptual framework for intelligent threat detection and digital protection. The study reviews current AI techniques—including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Reinforcement Learning (RL)—that enhance malware detection, phishing prevention, network intrusion detection, fraud identification, and automated incident response. The paper adopts a qualitative research methodology based on an extensive review of scholarly literature published in recent years and synthesizes existing knowledge to identify current practices, challenges, and future opportunities. The proposed framework demonstrates how AI-driven security systems can continuously collect security data, analyze network behavior, identify anomalies, classify cyber threats, and initiate automated responses with minimal human intervention. The study further discusses critical implementation challenges such as adversarial attacks, privacy concerns, explainable AI, data quality, algorithmic bias, computational costs, and legal and ethical issues. The findings suggest that AI significantly enhances cybersecurity capabilities by enabling proactive rather than reactive defense strategies. However, sustainable implementation requires robust governance, continuous model training, interdisciplinary collaboration, and responsible AI practices. The proposed framework contributes to the growing body of knowledge by offering a practical model that organizations can adapt to strengthen digital resilience in increasingly complex cyber environments.
Rushida CP (Mon,) studied this question.