## 2 Literature Review  
The expansion of the academic literature in cybersecurity indicates that AI, ML, and DL are becoming emerging technologies that can be leveraged to tackle current complex problems. Key insights in this literature review are synthesised directly from other studies discussing how this technology changed cybersecurity practices from 2016 to 2025. In this scientometric study, the author synthesised key ideas from various studies on how AI has transformed cybersecurity practice. Through these studies, the utilisation of ML techniques has grown to combat the exponential increase in the complexity of cyber threats and how cybersecurity has evolved over the decades. Traditionally, rule-based methodologies are employed in cybersecurity for detection and response to cyber threats [16]. Yet, traditional methods have become futile in addressing the growing complexity of the latest cyber-attacks, including advanced persistent threats (APTs) and zero-day attacks. As the volume of data generated in digital environments increased, the demand for more dynamic and adaptive solutions arose. Against this backdrop, AI and machine learning held out the promise of helping deal with these evolving troubles. Several studies examine the growing development of AI, ML, and DL techniques in the realm of cybersecurity [17; 18; 19; 20; 21; 22]. [PERSON], [PERSON] [23] shows how machine learning algorithms can enhance IDS. These algorithms learn to identify landscapes in network traffic dynamics and act on these threats independently without human intervention. Meanwhile, [PERSON], [PERSON] [24] explores the application of DL models, especially convolutional neural networks (CNNs), to enhance malware detection and mitigation. Unlike traditional detection systems, which operate on beaconing known signatures, DL models learn behavioural patterns to detect new, mutated types of malware. Furthermore, deep learning and machine learning are utilised to predict and prevent phishing attacks by looking at users' behaviours to find anomalies within email communications [25; 26]. According to [PERSON], [PERSON] [27], ML algorithms, especially supervised learning models, have been effectively trained with large datasets of phishing emails to improve prediction accuracy. Machine learning algorithms are capable of swiftly analysing a large volume of data. Organisations can use these studies' implications to discuss other applications of these algorithms and detect fraudulent behaviour by recognising unusual patterns in financial transactions. Several studies have critically investigated the role of ML and DL across various cybersecurity domains. A survey on malware detection examined how we can use ensemble learning techniques that combine multiple ML models to increase detection accuracy [28]. [PERSON], [PERSON] [29] demonstrated that deep learning can detect anomalous network traffic more efficiently than traditional methods. Finally, research on AI in cybersecurity applications for local government has shown its enormous potential. [PERSON] [30] indicates that AI-driven systems are increasingly used to manage public data and protect them from cyber threats. At the same time, [PERSON], [PERSON] [31] raises challenges about AI implementation, such as concerns over data privacy loss, the possibility of algorithmic bias or bias in the data themselves, and the requirement for rigorous testing to confirm that these systems are working as intended. While these promise to be heartening developments, they also come with challenges. Studies discuss the limitations of AI and ML, such as the requirement for more accessible and high-quality datasets to train the models effectively [32; 33; 34; 35; 36; 37]. Moreover, the insufficient explainability and transparency of AI-driven decisions are of great concern, particularly in high-stakes cybersecurity scenarios [38]. Several studies request more research on improving AI models' interpretability so cybersecurity professionals can understand and trust the AI models' decisions.