AI-Powered Threat Intelligence: Enhancing Real-Time Cyber Threat Detection and Response

A cyberthreat refers to any malicious activity targeting digital systems, networks, or data with the intention of disruption, and unauthorized access. These threats impact individuals, businesses, and governments across industries such as finance, healthcare, e-commerce, and critical infrastructure. Cyberthreats includes different forms, including malware such as viruses, phishing, denial-of-service (DoS) attacks, and data breaches. This study explores artificial intelligence enabled solutions to combat phishing, highlighting their role in proactive threat detection, and incident response. By utilizing machine learning (ML) algorithms, natural language processing (NLP), and behavioural analytics, AI-powered systems analyse extensive datasets and social analytics —including email content, URL structures, and user interactions—to detect phishing attempts, and fraudulent communications.ML models are essential in phishing detection, as they examine email patterns, website attributes, and user behaviours, mail trends to identify malicious intent. Unlike traditional rule-based methods, ML models continuously learn and adapt to emerging phishing techniques, improving their detection accuracy over time. These models assess key features such as URL composition, domain legitimacy, sender information, and anomalies in message content. NLP further enhances detection by identifying deceptive language and urgent call-to-action phrases in phishing emails. Additionally, behavioural analytics and clustering techniques like K-Means helps detecting suspicious activity. This study underscores the efficiency of ML-based phishing detection in real-time threat identification, reducing human dependency and response time. Future research should enhance model’s robustness,